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  • Balanced crossover operators in Genetic Algorithms
    Swarm Evol. Comput. (IF 6.330) Pub Date : 2020-01-20
    Luca Manzoni; Luca Mariot; Eva Tuba

    In several combinatorial optimization problems arising in cryptography and design theory, the admissible solutions must often satisfy a balancedness constraint, such as being represented by bitstrings with a fixed number of ones. For this reason, several works in the literature tackling these optimization problems with Genetic Algorithms (GA) introduced new balanced crossover operators which ensure that the offspring has the same balancedness characteristics of the parents. However, the use of such operators has never been thoroughly motivated, except for some generic considerations about search space reduction. In this paper, we undertake a rigorous statistical investigation on the effect of balanced and unbalanced crossover operators against three optimization problems from the area of cryptography and coding theory: nonlinear balanced Boolean functions, binary Orthogonal Arrays (OA) and bent functions. In particular, we consider three different balanced crossover operators (each with two variants: “left-to-right” and “shuffled”), two of which have never been published before, and compare their performances with classic one-point crossover. We are able to confirm that the balanced crossover operators performs better than all three balanced crossover operators. Furthermore, in two out of three crossovers, the “left-to-right” version performs better than the “shuffled” version.

  • Investigating the equivalence between PBI and AASF scalarization for multi-objective optimization
    Swarm Evol. Comput. (IF 6.330) Pub Date : 2020-01-14
    Hemant Kumar Singh; Kalyanmoy Deb

    Scalarization refers to a generic class of methods to combine multiple conflicting objectives into one in order to find a Pareto optimal solution to the original problem. Augmented achievement scalarizing function (AASF) is one such method used popularly in the multi-criterion decision-making (MCDM) field. In evolutionary multi-objective optimization (EMO) literature, scalarization methods such as penalty boundary intersection (PBI) are commonly used to compare similar solutions within a population. Both AASF and PBI methods require a reference point and a reference direction for their calculation. In this paper, we aim to analytically derive and understand the commonalities between these two metrics and gain insights into the limitations of their standard parametric forms. We show that it is possible to find an equivalent modified AASF formulation for a given PBI parameter and vice versa for bi-objective problems. Numerical experiments are presented to validate the theory developed. We further discuss the challenges in extending this to higher objectives and show that it is still possible to achieve limited equivalence along symmetric reference vectors. The study connects the two philosophies of solving multi-objective optimization problems, provides a means to gain a deeper understanding of both these measures, and expands their parametric range to provide more flexibility of controlling the search behavior of the EMO algorithms.

  • Adaptive Global WASF-GA to handle many-objective optimization problems
    Swarm Evol. Comput. (IF 6.330) Pub Date : 2020-01-13
    Mariano Luque; Sandra Gonzalez-Gallardo; Rubén Saborido; Ana B. Ruiz

    In this paper, a new version of the aggregation-based evolutionary algorithm Global WASF-GA (GWASF-GA) for many-objective optimization is proposed, called Adaptive Global WASF-GA (A-GWASF-GA). The fitness function of GWASF-GA is defined by an achievement scalarizing function (ASF) based on the Tchebychev distance, which considers two reference points (the nadir and utopian points) and a set of weight vectors. Despite of the benefits of using these two reference points simultaneously and a well-distributed set of weight vectors, it is necessary to go a step further to get better approximations in problems with complicated Pareto optimal fronts. For this, in A-GWASF-GA, some of the weight vectors are re-calculated during the optimization process based on the sparsity of the solutions found so far, and taking into account some theoretical results demonstrated in this paper regarding the ASF considered. Different strategies are carried out to accelerate the convergence and to maintain the diversity. The computational results, carried out in comparison with RVEA, NSGA-III, and different versions of MOEA/D, show the potential of A-GWASF-GA in well-known but also in novel many-objective optimization benchmark problems.

  • Development of particle swarm and topology optimization-based modeling for mandibular distractor plates
    Swarm Evol. Comput. (IF 6.330) Pub Date : 2020-01-11
    Abdullah Tahir Şensoy; Irfan Kaymaz; Ümit Ertaş

    Mandibular Distraction Osteogenesis (MDO) is a common clinical procedure to correct mandibular retrognathia. However, since there is not a gold standard for determining the screw positions for current MDO operations, deviation of distraction direction and malocclusion increases. This case results in need of additional operations that affect the callus stability. In these cases, relapse risk increases and remodelling period gets longer. On the other hand, large volume of the distractor plates results in more invasive treatment and negatively affects the patients’ comfort. To overcome these problems, this study offers a new method including; virtual surgery simulation, determining the optimum screw configuration using particle swarm optimization loop linked between MATLAB-PYTHON-ANSYS programs and the design of distractor plate geometry with topology optimization. In order to test the proposed method, two different Finite Element (FE) models, CM and OM, were established based on conventional and optimum method, respectively. FEA results of the current study reveals that OM has 33.56% less displacement compared to CM, and the most critical screw in terms of screw loosening for OM has 35.29% less strain value than CM. These outcomes show OM shows superior callus stability in comparison with CM. On the other hand, redesign of the distractor plates using topology optimization according to the best screw positions provides 43.32% reduction in the total implant volume which means reduced cost and a less invasive MDO operation. Therefore, the clinical use of this protocol is expected to increase the success of the operation by shortening the recovery period.

  • Metaheuristics to solve grouping problems: A review and a case study
    Swarm Evol. Comput. (IF 6.330) Pub Date : 2020-01-06
    Octavio Ramos-Figueroa; Marcela Quiroz-Castellanos; Efrén Mezura-Montes; Oliver Schütze

    Grouping problems are a special type of combinatorial optimization problems that have gained great relevance because of their numerous real-world applications. The solution process required by some grouping problems represents a high complexity, and currently, there is no algorithm to find the optimal solution efficiently in the worst case. Consequently, the scientific community has classified such problems as NP-hard. For the solution of grouping problems, numerous elaborate procedures have been designed incorporating different techniques. The specialized literature includes enumerative methods, neighborhood searches, evolutionary algorithms as well as swarm intelligence algorithms. In this study, a review of twenty-two NP-hard grouping problems is carried out, considering seventeen metaheuristics. The state-of-the-art suggests that Genetic Algorithms (GAs) have shown the best performance in most of the cases, and the group-based representation scheme can be used to improve the performance of different metaheuristics designed to solve grouping problems. Finally, a case study is presented to compare the performance of three metaheuristic algorithms with different representation schemes for the Parallel-Machine Scheduling problem with unrelated machines, including the GA with extended permutation encoding, the Particle Swarm Optimization (PSO) with machine-based encoding, and the GA with group-based encoding. Experimental results suggest that the GA with the group-based encoding is the best option to address this problem.

  • A many-objective evolutionary algorithm with diversity-first based environmental selection
    Swarm Evol. Comput. (IF 6.330) Pub Date : 2020-01-06
    Chao Wang; Huimin Pan; Yansen Su

    Environmental selection in Pareto-based many-objective evolutionary algorithms generally employ Pareto-dominance relation to first consider the convergence and give higher priority to convergence than diversity. When the many-objective optimization problem has a complicated Pareto front, this selection strategy can easily miss the promising areas and converge into a subregion of the Pareto front. To address this issue, we propose a many-objective evolutionary algorithm with diversity-first based environmental selection. Different from the existing selection strategies, the environmental selection procedure in the proposed algorithm adopts a diversity-first-and-convergence-second principle, which first selects the representative solutions that having better diversity and then considers using the well-converged solutions to replace them in subregions. This selection-replacement strategy can maintain the diversity and make contribution to the convergence. In addition, a selection criterion, termed adaptive angle penalized distance, is designed to judge whether the replacement is implemented or not. The proposed algorithm is compared with five state-of-the-art many-objective evolutionary algorithms on a large number of test problems with various characteristics. Experimental studies demonstrate that the proposed algorithm has competitive performance on many-objective optimization problems.

  • Survivable networks via on-line real-time evolution of dual air-ground swarm
    Swarm Evol. Comput. (IF 6.330) Pub Date : 2020-01-03
    Jiangjun Tang; George Leu

    The use of unmanned airborne agents as relays for ground agents to ensure ground network survivability is gaining traction at both theoretical and practical levels, in research that targets contexts like search and rescue in disaster areas, farming with autonomous equipment, surveillance, internet of things, military operations, autonomous transportation systems, and many others. This comes with some challenges, which include the dynamics of the ground unit behaviors, the scalability of the system, the mobility model of the airborne agents, and the integration between ground and air agents. This paper contributes to addressing the above challenges by using a bio-inspired approach that combines swarm intelligence and evolutionary computation for providing ground network survivability for a wide range of ground movement patterns. The proposed approach models ground and airborne agents as a dual air-ground swarm that uses boids-like rules, and optimizes the movement of the airborne agents using an on-line real-time genetic algorithm with shadow simulation and prediction. Arguably, the proposed approach provides system scalability both size-wise and context-wise, while also offering a certain amount of integration between the ground and air swarms of agents. The results of the investigation demonstrate that the methods employed endow the airborne agents with the needed capability to ensure network survivability for complex ground activity, including resilience to changes in the ground movement pattern and good responsiveness to activities that have no pattern at all, such as uniform random walks.

  • On explaining machine learning models by evolving crucial and compact features
    Swarm Evol. Comput. (IF 6.330) Pub Date : 2019-12-27
    Marco Virgolin; Tanja Alderliesten; Peter A.N. Bosman

    Feature construction can substantially improve the accuracy of Machine Learning (ML) algorithms. Genetic Programming (GP) has been proven to be effective at this task by evolving non-linear combinations of input features. GP additionally has the potential to improve ML explainability since explicit expressions are evolved. Yet, in most GP works the complexity of evolved features is not explicitly bound or minimized though this is arguably key for explainability. In this article, we assess to what extent GP still performs favorably at feature construction when constructing features that are (1) Of small-enough number, to enable visualization of the behavior of the ML model; (2) Of small-enough size, to enable interpretability of the features themselves; (3) Of sufficient informative power, to retain or even improve the performance of the ML algorithm. We consider a simple feature construction scheme using three different GP algorithms, as well as random search, to evolve features for five ML algorithms, including support vector machines and random forest. Our results on 21 datasets pertaining to classification and regression problems show that constructing only two compact features can be sufficient to rival the use of the entire original feature set. We further find that a modern GP algorithm, GP-GOMEA, performs best overall. These results, combined with examples that we provide of readable constructed features and of 2D visualizations of ML behavior, lead us to positively conclude that GP-based feature construction still works well when explicitly searching for compact features, making it extremely helpful to explain ML models.

  • Solving the multi-objective flexible job shop scheduling problem with a novel parallel branch and bound algorithm
    Swarm Evol. Comput. (IF 6.330) Pub Date : 2019-12-23
    Carlos Soto; Bernabé Dorronsoro; Héctor Fraire; Laura Cruz-Reyes; Claudia Gomez-Santillan; Nelson Rangel

    This work presents a novel parallel branch and bound algorithm to efficiently solve to optimality a set of instances of the multi-objective flexible job shop scheduling problem for the first time, to the very best of our knowledge. It makes use of the well-known NSGA-II algorithm to initialize its upper bound. The algorithm is implemented for shared-memory architectures, and among its main features, it incorporates a grid representation of the solution space, and a concurrent priority queue to store and dispatch the pending sub-problems to be solved. We report the optimal Pareto front of thirteen well-known instances from the literature, which were unknown before. They will be very useful for the scientific community to provide more accuracy in the performance measurement of their algorithms. Indeed, we carefully analyze the performance of NSGA-II on these instances, comparing the results against the optimal ones computed in this work. Extensive computational experiments show that the proposed algorithm using 24 cores achieves a speedup of 15.64x with an efficiency of 65.20%.

  • Role of swarm and evolutionary algorithms for intrusion detection system: A survey
    Swarm Evol. Comput. (IF 6.330) Pub Date : 2019-12-13
    Ankit Thakkar; Ritika Lohiya

    The growth of data and categories of attacks, demand the use of Intrusion Detection System(IDS) effectively using Machine Learning(ML) and Deep Learning(DL) techniques. Apart from the ML and DL techniques, Swarm and Evolutionary (SWEVO) Algorithms have also shown significant performance to improve the efficiency of the IDS models. This survey covers SWEVO-based IDS approaches such as Genetic Algorithm(GA), Ant Colony Optimization(ACO), Particle Swarm Optimization(PSO), Artificial Bee Colony Optimization(ABC), Firefly Algorithm(FA), Bat Algorithm(BA), and Flower Pollination Algorithm(FPA). The paper also discusses applications of the SWEVO in the field of IDS along with challenges and possible future directions.

  • Applying graph-based differential grouping for multiobjective large-scale optimization
    Swarm Evol. Comput. (IF 6.330) Pub Date : 2019-12-13
    Bin Cao; Jianwei Zhao; Yu Gu; Yingbiao Ling; Xiaoliang Ma

    An increasing number of multiobjective large-scale optimization problems (MOLSOPs) are emerging. Optimization based on variable grouping and cooperative coevolution is a good way to address MOLSOPs, but few attempts have been made to decompose the variables in MOLSOPs. In this paper, we propose multiobjective graph-based differential grouping with shift (mogDG-shift) to decompose the large number of variables in an MOLSOP. We analyze the variable properties, then detect the interactions among variables, and finally group the variables based on their properties and interactions. We modify the decision variable analyses (DVA) in the multiobjective evolutionary algorithm based on decision variable analyses (MOEA/DVA), extend graph-based differential grouping (gDG) to MOLSOPs, and test the method on many MOLSOPs. The experimental results show that mogDG-shift can achieve 100% grouping accuracy for LSMOP and DTLZ as well as almost all WFG instances, which are much better than DVA. We further combine mogDG-shift with two representative multiobjective evolutionary algorithms: the multiobjective evolutionary algorithm based on decomposition (MOEA/D) and the non-dominated sorting genetic algorithm II (NSGA-II). Compared with the original algorithms, the algorithms combined with mogDG-shift show improved optimization performance.

  • A multi-objective adaptive evolutionary algorithm to extract communities in networks
    Swarm Evol. Comput. (IF 6.330) Pub Date : 2019-12-10
    Qi Li, Zehong Cao, Weiping Ding, Qing Li

    Community structure is one of the most important attributes of complex networks, which reveals the hidden rules and behavior characteristics of complex networks. Existing works need to pre-set weight parameters to control the different emphasis on the objective function, and cannot automatically identify the number of communities. In the process of optimization, there will be some challenges, such as premature and inefficiency. This paper presents a multi-objective adaptive fast evolutionary algorithm (F-SGCD) for community detection in complex networks. Firstly, it transforms the problem of community detection into a multi-objective optimization problem and constructs two objective functions of community score and community fitness. Secondly, an external elite gene pool is introduced to store non-inferior solutions with high fitness. At the same time, an adaptive genetic operator is executed to return a set of non-dominant solutions compromised between the two objective functions. Finally, a Pareto optimal solution with the highest modularity is selected and decoded to generate a set of independent subnetworks. Experiments show that the multi-objective adaptive fast evolutionary algorithm greatly improves the accuracy of community detection in complex networks, and can discover the hierarchical structure of complex networks better.

  • Improved Differential Evolution for Noisy Optimization
    Swarm Evol. Comput. (IF 6.330) Pub Date : 2019-11-27
    Pratyusha Rakshit

    A novel approach is proposed in this paper to improve the optimization proficiency of the differential evolution (DE) algorithm in the presence of stochastic noise in the objective surface by utilizing the composite benefit of four strategies. The first strategy is devised with an aim to employ reinforcement learning scheme of stochastic learning automata for autonomous selection of the sample size of a trial solution (for its repeated fitness evaluation) based on the characteristics of the fitness landscape in its local neighborhood. The second stratagem is proposed to estimate the effective fitness measure from multiple fitness samples of a trial solution, resulting from sampling. The novelty of the second policy lies in considering the distribution of noisy samples during effective fitness evaluation, instead of their direct averaging. The third strategy deals with amelioration of the DE/current-to-best/1 mutation scheme to judiciously direct the search in promising region, even in prevailing existence of noise in the objective surface. Finally, the greedy selection policy of the traditional DE is modified by introducing the principle of probabilistic crowding induced niching to ensure both the population quality and the population diversity. Comparative analysis performed on simulation results for diverse noisy benchmark functions reveal the statistically significant superiority of the proposed algorithm to its contenders with respect to function error value.

  • Handling bound constraints in CMA-ES: An experimental study
    Swarm Evol. Comput. (IF 6.330) Pub Date : 2019-11-18
    Rafał Biedrzycki

    Bound constraints are the lower and upper limits defined for each coordinate of the solution. There are many methods to deal with them, but there is no clear guideline for which of them should be preferred. This paper is devoted to handling bound constraints in the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) algorithm. It surveys 22 Bound Constraint Handling Methods (BCHMs). The experiments cover both unimodal and multimodal functions taken from the CEC 2017 and the BBOB benchmarks. The performance of CMA-ES was found to change when different BCHMs were used. The worst and the best BCHMs were identified. The results of CMA-ES with the best BCHM and restarts were compared on CEC 2017 with the results of recently published derivatives of Differential Evolution (DE).

  • A benchmark for equality constrained multi-objective optimization
    Swarm Evol. Comput. (IF 6.330) Pub Date : 2019-11-16
    Oliver Cuate, Lourdes Uribe, Adriana Lara, Oliver Schütze

    Evolutionary multi-objective optimization (EMO) is certainly a story of great success considering the numerous contributions and their applications to different problems and fields during the last two decades. One issue, however, that has been almost neglected so far is the consideration of multi-objective optimization problems (MOPs) that contain equality constraints. Such constraints play a special role as the inclusion of each equality constraint typically reduces the dimension of the search space by one. Consequently, the probability for a randomly chosen candidate solution of an equality constrained MOP to be feasible is zero, which makes the treatment of such problems very hard for EMO algorithms. In this paper, we propose a new benchmark of equality constrained MOPs. The problems are derived from the well-known DTLZ and IDTLZ problems and hence inherit their properties. The new benchmarks, Eq-DTLZ and Eq-IDTLZ, are scalable both in decision and objective space as well as in the number of equality constraints. Furthermore, all Pareto sets differ from the solution sets of the unconstrained problems and can be expressed analytically which make them good candidates for testing EMO algorithms on this important problem class. Based on the new benchmark, we investigate the performance of some state-of-the-art evolutionary algorithms. The results show that the new problems are indeed hard to solve for all considered algorithms and that further investigation has to be done for the reliable treatment of equality constrained MOPs.

  • An optimized model based on convolutional neural networks and orthogonal learning particle swarm optimization algorithm for plant diseases diagnosis
    Swarm Evol. Comput. (IF 6.330) Pub Date : 2019-11-15
    Ashraf Darwish, Dalia Ezzat, Aboul Ella Hassanien

    The plant disease classification based on using digital images is very challenging. In the last decade, machine learning techniques and plant images classification tools such as deep learning can be used for recognizing, detecting and diagnosing plant diseases. Currently, deep learning technology has been used for plant disease detection and classification. In this paper, an ensemble model of two pre-trained convolutional neural networks (CNNs) namely VGG16 and VGG19 have been developed for the task plant disease diagnosis by classifying the leaves images of healthy and unhealthy. In this context, CNNs are used due to its capability of overcoming the technical problems which are associated with the classification problem of plant diseases. However, CNNs suffer from a great variety of hyperparameters with specific architectures which is considered as a challenge to identify manually the optimal hyperparameters. Therefore, orthogonal learning particle swarm optimization (OLPSO) algorithm is utilized in this paper to optimize a number of these hyperparameters by finding optimal values for these hyperparameters rather than using traditional methods such as the manual trial and error method. In this paper, to prevent CNNs from falling into the local minimum and to train efficiently, an exponentially decaying learning rate (EDLR) schema is used. In this paper, the problem of the imbalanced used dataset has been solved by using random minority oversampling and random majority undersampling methods, and some restrictions in terms of both the number and diversity of samples have been overcome. The obtained results of this work show that the accuracy of the proposed model is very competitive. The experimental results are compared with the performance of other pre-trained CNN models namely InceptionV3 and Xception, whose hyperparameters were selected using a non-evolutionary method. The comparison results demonstrated that the proposed diagnostic approach has achieved higher performance than the other models.

  • Multimodal Memetic Framework for low-resolution protein structure prediction
    Swarm Evol. Comput. (IF 6.330) Pub Date : 2019-11-14
    Rumana Nazmul, Madhu Chetty, Ahsan Raja Chowdhury

    In this paper, we propose a systematic design of evolutionary optimization, namely Multimodal Memetic Framework (MMF), to effectively search the vast complex energy landscape. Our proposed memetic framework is implemented in hierarchical stages with the optimization of each stage performed in parallel in three different states: Exploratory, Exploitative and Central. Each state, with its own set of sub-populations, either explores or exploits by beneficial mixing of potential solutions to direct the search towards a global solution. Instead of implementing identical genetic operators, the proposed approach employs different selection and survival criteria in each state according to their designated task. The Exploratory state employs a knowledge-based initial population generation technique with appropriately tuned genetic operators to guide the search to the “nearest peak”. The Exploitative state fine-tunes the individuals representing different regions by applying a building block based local search. Finally, by utilizing the imbibed knowledge from different peaks, the Central state carries out information-exchange among the highly fit solutions for exploring the undiscovered regions. The information exchange employs a novel non-random parental selection technique to distribute the reproduction opportunity intelligently among the individuals for making cross-over more effective. The method has been tested on a set of various benchmark protein sequences for 2D and 3D lattice models. The experimental results demonstrate the superiority of the proposed method over other state-of-the-art algorithms.

  • On the efficient computation and use of multi-objective descent directions within constrained MOEAs
    Swarm Evol. Comput. (IF 6.330) Pub Date : 2019-11-12
    Lourdes Uribe, Adriana Lara, Oliver Schütze

    Multi-objective evolutionary algorithms (MOEAs) are a widely accepted choice for the numerical treatment of multi-objective optimization problems (MOPs). For constrained problems, however, these methods still have room for improvement to compute satisfactory approximations of the solution sets. A possible remedy is the hybridization of MOEAs with specialized local search mechanisms; which is not a simple task due to their high cost. In this work, we consider the information of the constraints when performing the local search, and propose a new and effective way to compute descent directions for constrained bi-objective optimization problems. Since the directions are computed via neighborhood sampling, the method is perfectly suited for the use within MOEAs or any other population based algorithm as the samples can be taken precisely from the populations. The new method can be used as local search engine within, in principle, any MOEA. As demonstrator, we will consider two particular hybrids. Numerical results on some benchmark problems support the benefits of the novel approach. Though this work focuses on the bi-objective case, this represents an important step to formalize gradient-free multi-objective descent directions and its efficient interleaving into MOEAs.

  • Unsupervised feature selection based on bio-inspired approaches
    Swarm Evol. Comput. (IF 6.330) Pub Date : 2019-11-11
    Nádia Junqueira Martarelli, Marcelo Seido Nagano

    In recent years, the scientific community has witnessed an explosion in the use of pattern recognition algorithms. However, little attention has been paid to the tasks preceding the execution of these algorithms, the preprocessing activities. One of these tasks is dimensionality reduction, in which a subset of features that improves the performance of the mining algorithm is located and algorithm's runtime is reduced. Although there are many methods that address the problems in pattern recognition algorithms, effective solutions still need to be researched and explored. Hence, this paper aims to address three of the issues surrounding these algorithms. First, we propose adapting a promising meta-heuristic called biased random-key genetic algorithm, which considers a random initial population construction. We call this algorithm as unsupervised feature selection by biased random-key genetic algorithm I. Next, we propose an approach for building the initial population partly in a deterministic way. Thus, we applied this idea in two algorithms, named unsupervised feature selection by particle swarm optimization and unsupervised feature selection by biased random-key genetic algorithm II. Finally, we simulated different datasets to study the effects of relevant and irrelevant attributes, and of noisy and missing data on the performance of the algorithms. After the Wilcoxon rank-sum test, we can state that the proposed algorithms outperform all other methods in different datasets. It was also observed that the construction of the initial population in a partially deterministic way contributed to the better performance. It should be noted that some methods are more sensitive to noisy and missing data than others, as well as to relevant and irrelevant attributes.

  • Efficient multi-objective algorithm for the lot-streaming hybrid flowshop with variable sub-lots
    Swarm Evol. Comput. (IF 6.330) Pub Date : 2019-11-06
    Jun-qing Li, Xin-rui Tao, Bao-xian Jia, Yu-yan Han, Chuang Liu, Peng Duan, Zhi-xin Zheng, Hong-yan Sang

    Recent years, the multi-objective evolutionary algorithm based on decomposition (MOEA/D) has been researched and applied for numerous optimization problems. In this study, we propose an improved version of MOEA/D with problem-specific heuristics, named PH-MOEAD, to solve the hybrid flowshop scheduling (HFS) lot-streaming problems, where the variable sub-lots constraint is considered to minimize four objectives, i.e., the penalty caused by the average sojourn time, the energy consumption in the last stage, as well as the earliness and the tardiness values. For solving this complex scheduling problem, each solution is coded by a two-vector-based solution representation, i.e., a sub-lot vector and a scheduling vector. Then, a novel mutation heuristic considering the permutations in the sub-lots is proposed, which can improve the exploitation abilities. Next, a problem-specific crossover heuristic is developed, which considered solutions with different sub-lot size, and therefore can make a solution feasible and enhance the exploration abilities of the algorithm as well. Moreover, several problem-specific lemmas are proposed and a right-shift heuristic based on them is subsequently developed, which can further improve the performance of the algorithm. Lastly, a population initialization mechanism is embedded that can assign a fit reference vector for each solution. Through comprehensive computational comparisons and statistical analysis, the highly effective performance of the proposed algorithm is favorably compared against several presented algorithms, both in solution quality and population diversity.

  • A discrete-time switched linear model of the particle swarm optimization algorithm
    Swarm Evol. Comput. (IF 6.330) Pub Date : 2019-11-05
    Haopeng Zhang

    In this paper, the convergence issue of the Particle Swarm Optimization (PSO) algorithm is investigated. Most of the models of PSO algorithms are time-invariant linear models with the assumption the local and global best solutions do not change, i.e., the stagnation assumption. However, in this paper, a discrete-time switched linear model is introduced to study the stability and convergence of the PSO algorithm without the stagnation assumption. By considering the updates of local best positions and global best solutions, a sequence of state transform matrixes is generated during the searching process. The semistability of the proposed switched linear system is studied. The conditions of the convergence in mean and convergence in probability are derived by using the recently developed results in paracontraction. Moreover, numerical examples are provided to verify the results proposed in this paper.

  • A novel design of differential evolution for solving discrete traveling salesman problems
    Swarm Evol. Comput. (IF 6.330) Pub Date : 2019-11-02
    Ismail M. Ali, Daryl Essam, Kathryn Kasmarik

    Differential evolution is one of the most powerful and popular evolutionary algorithms, which is primarily used to solve continuous-based optimization problems. Although differential evolution has been considered unsuitable for solving many permutation-based real-world combinatorial problems, several efforts for designing an efficient discrete version of the differential evolution algorithm have occurred in recent years. This paper introduces a novel discrete differential evolution algorithm for improving the performance of the standard differential evolution algorithm when it is used to solve complex discrete traveling salesman problems. In this approach, we propose to use a combination of, 1) an improved mapping mechanism to map continuous variables to discrete ones and vice versa, 2) a k-means clustering-based repairing method for enhancing the solutions in the initial population, 3) an ensemble of mutation strategies for increasing the exploration capability of the algorithm. Finally, for improving the local capability of the proposed algorithm when solving discrete problems, two well-known local searches have also been adapted. To judge its performance, the proposed algorithm is compared with those of 27 state-of-the-art algorithms, for solving 45 instances of traveling salesman problems, with different numbers of cities. The experimental results demonstrated that our technique significantly outperforms most of the comparative methods, in terms of the average errors from the best-known solutions, and achieved very competitive results with better computational time than others.

  • Identifying influential nodes based on ant colony optimization to maximize profit in social networks
    Swarm Evol. Comput. (IF 6.330) Pub Date : 2019-11-02
    Chiman Salavati, Alireza Abdollahpouri

    One of the most important applications for identification of influential nodes in social networks is viral marketing. In viral marketing, there are valuable users from which companies or smaller businesses benefit most at the lowest cost. Inspired from the behavior of real ants and based on the ant colony optimization algorithm, we propose new methods named PMACO and IMOACO in this paper to find the most valuable users. First, the influence graph is derived from the analysis of users’ interactions and communications in a social network. The negative influence among users is also considered in the process of generating the influence graph. For reduction of computational complexity and removal of unimportant nodes from the influence graph, the nodes the levels of influence of which on their neighbors are less than a specific threshold value are eliminated. Then, the representation of the search space as a weighted graph is constructed by the remaining nodes, where the weight of each edge is the similarity between the two nodes of which that edge is composed. Next, the ants begin their search processes with the goal of maximizing profit and minimizing the similarity among the selected nodes. Assessments have been made on real and synthetic datasets, and compared the proposed algorithm with well-known ones. The results of the experiments demonstrate the efficiency of the proposed algorithm.

  • A hybrid multi-agent Coordination Optimization Algorithm
    Swarm Evol. Comput. (IF 6.330) Pub Date : 2019-11-02
    Haopeng Zhang, Siheng Su

    Many-Objective optimization problems (MaOPs) are the optimization problems which contain more than three conflicting objectives. Extensive interests from both algorithms development and practical applications are attracted to study the MaOPs. The success of the Particle Swarm Optimization (PSO) algorithm and Evolutionary Algorithm (EA) as single-objective optimizers motivated researchers to extend the use of those techniques to solve the MaOPs: many-objective particle swarm optimization algorithms (MOPSOs) and many-objective evolutionary algorithms (MOEAs). In this paper, we extend a recently developed bio-inspired optimization algorithm, Multi-agent Coordination Optimization Algorithm (MCO) from a single-objective optimizer to a many-objective optimizer: Many-Objective Multi-agent Coordination Optimization Algorithm (MOMCO). The cooperative mechanism in the MCO accelerates the searching process. To tackle the MaOPs, an inverted generational distance indicator method is used to distinguish the non-dominated solutions in MOMCO to balance the diversity ability and convergence ability of the solutions during the searching process. Together with a hybrid combination with EA, the diversity and accuracy of the MOMCO will be improved. Moreover, the convergence issue is studied for the proposed MOMCO algorithm by using the Jury's test. Experimental results are provided to demonstrate the effectiveness of the proposed MOMCO by comprising with six state-of-the-art MOPSOs and MOEAs. By calculating the Wilcoxon's rank sum test, the proposed MOMCO algorithm demonstrated superior performance among all the seven algorithms.

  • Preference-based cone contraction algorithms for interactive evolutionary multiple objective optimization
    Swarm Evol. Comput. (IF 6.330) Pub Date : 2019-11-01
    Miłosz Kadziński, Michał K. Tomczyk, Roman Słowiński

    We introduce a family of interactive evolutionary algorithms for Multiple Objective Optimization (MOO). In the phase of preference elicitation, a Decision Maker (DM) is asked to compare some pairs of solutions from the current population. Such holistic information is represented by a set of compatible instances of achievement scalarizing or quasi-convex preference functions, which contribute to the construction of preference cones in the objective space. These cones are systematically contracted during the evolutionary search, because an incremental specification of the DM's pairwise comparisons is progressively reducing the DM's most preferred region in the objective space. An inclusion of evolved solutions in this region is used along with the dominance relation to revise an elitism principle of the employed optimization algorithm. In this way, the evolutionary search is focused on a subset of Pareto optimal solutions that are particularly interesting to the DM. We investigate, moreover, how the convergence is influenced by the use of some pre-defined and newly proposed self-adjusting (dynamic) interaction patterns. We also propose a new way for visualizing the progress of an evolutionary search. It supports understanding the differences between effects of a selection pressure imposed by various optimization algorithms.

  • Dimensionality reduction in evolutionary algorithms-based feature selection for motor imagery brain-computer interface given
    Swarm Evol. Comput. (IF 6.330) Pub Date : 2019-11-01
    Ping Tan, Xin Wang, Yong Wang

    For the classification of motor imagery brain-computer interface (BCI) based on electroencephalography (EEG), appropriate features are crucial to obtain a high classification accuracy. Considering the characteristics of the EEG signals, the time-frequency-space three-dimensional features are extracted. Due to a considerable number of the extracted features, the performance of a classifier will degrade. Therefore, it is necessary to implement feature selection. However, existing feature selection methods are easy to fall into a local optimum of a high-dimensional feature selection problem. In this paper, a dimensionality reduction mechanism (called DimReM) is proposed, which gradually reduces the dimension of the search space by removing some unimportant features. In principle, DimReM transforms a high-dimensional feature selection problem into a low-dimensional one. DimReM does not introduce any additional parameters and its implementation is simple. To verify its effectiveness, DimReM is combined with different evolutionary algorithms and different classifiers to select features on various kinds of datasets. Compared with evolutionary algorithms without dimensionality reduction, their augmented versions equipped with DimReM can find feature subsets with higher classification accuracies while smaller numbers of selected features.

  • An adaptive multi-objective algorithm based on decomposition and large neighborhood search for a green machine scheduling problem
    Swarm Evol. Comput. (IF 6.330) Pub Date : 2019-10-31
    Luciano P. Cota, Frederico G. Guimarães, Roberto G. Ribeiro, Ivan R. Meneghini, Fernando B. de Oliveira, Marcone J.F. Souza, Patrick Siarry

    Green machine scheduling consists in the allocation of jobs in order to maximize production, in view of the sustainable use of energy. This work addresses the unrelated parallel machine scheduling problem with setup times, with the minimization of the makespan and the total energy consumption. The latter takes into account the power consumption of each machine in different operation modes. We propose multi-objective extensions of the Adaptive Large Neighborhood Search (ALNS) metaheuristic with Learning Automata (LA) to improve the search process and to solve the large scale instances efficiently. ALNS combines ad-hoc destroy and repair (also named removal and insertion) operators and a local search procedure. The LA is used to adapt the selection of insertion and removal operators within the framework of ALNS. Two new algorithms are developed: the MO-ALNS and the MO-ALNS/D. The first algorithm is a direct extension of single objective ALNS by using multi-objective local search. As this method does not offer much control of the diversification of the Pareto front approximation, a second strategy employs the decomposition approach similar to MOEA/D algorithm. The results show that the MO-ALNS/D algorithm has better performance than MO-ALNS and MOEA/D in all indicators. These findings show that the decomposition strategy is beneficial not only for evolutionary algorithms, but it is indeed an efficient way to extend ALNS to multi-objective problems.

  • jMetalPy: A Python framework for multi-objective optimization with metaheuristics
    Swarm Evol. Comput. (IF 6.330) Pub Date : 2019-10-31
    Antonio Benítez-Hidalgo, Antonio J. Nebro, José García-Nieto, Izaskun Oregi, Javier Del Ser

    This paper describes jMetalPy, an object-oriented Python-based framework for multi-objective optimization with metaheuristic techniques. Building upon our experiences with the well-known jMetal framework, we have developed a new multi-objective optimization software platform aiming not only at replicating the former one in a different programming language, but also at taking advantage of the full feature set of Python, including its facilities for fast prototyping and the large amount of available libraries for data processing, data analysis, data visualization, and high-performance computing. As a result, jMetalPy provides an environment for solving multi-objective optimization problems focused not only on traditional metaheuristics, but also on techniques supporting preference articulation, constrained and dynamic problems, along with a rich set of features related to the automatic generation of statistical data from the results generated, as well as the real-time and interactive visualization of the Pareto front approximations produced by the algorithms. jMetalPy offers additionally support for parallel computing in multicore and cluster systems. We include some use cases to explore the main features of jMetalPy and to illustrate how to work with it.

  • Evolutionary algorithms for many-objective cloud service composition: Performance assessments and comparisons
    Swarm Evol. Comput. (IF 6.330) Pub Date : 2019-10-31
    Jiajun Zhou, Liang Gao, Xifan Yao, Chunjiang Zhang, Felix T.S. Chan, Yingzi Lin

    Service composition and optimal selection (SCOS) concerns the building of optimal composite service by integrating existing services with the aim of performing complex task. Due to a plethora of affordable cloud services providing similar functionalities while differing in quality of service (QoS), how to determine suitable candidates to orchestrate the best composite service, also known as QoS-aware SCOS problem, becomes more complicated. A number of evolutionary optimizers have been developed to resolve SCOS. Unfortunately, a large majority of these optimizers carry out the optimization by aggregating many diverse QoS attributes into a single objective or simply considering two or three representative QoS attributes. SCOS, particularly, from the perspective of many-objective optimization, has not received an appropriate attention. As more factors come into play, SCOS is strictly a many-objective problem. This study explores the scalability of recently state-of-the-art evolutionary many-objective optimization (EMaO) algorithms in addressing SCOS. Comparative results reveal that these EMaO algorithms, never before applied to many-objective SCOS, exhibit distinct search abilities with respect to the objective space dimensionality and problem scale. Based on the empirical observation, useful suggestions and insights for choosing suitable EMaO algorithms pertaining to different SCOS problems are given.

  • Using Animal Instincts to Design Efficient Biomedical Studies via Particle Swarm Optimization.
    Swarm Evol. Comput. (IF 6.330) Pub Date : 2014-10-07
    Jiaheng Qiu,Ray-Bing Chen,Weichung Wang,Weng Kee Wong

    Particle swarm optimization (PSO) is an increasingly popular metaheuristic algorithm for solving complex optimization problems. Its popularity is due to its repeated successes in finding an optimum or a near optimal solution for problems in many applied disciplines. The algorithm makes no assumption of the function to be optimized and for biomedical experiments like those presented here, PSO typically finds the optimal solutions in a few seconds of CPU time on a garden-variety laptop. We apply PSO to find various types of optimal designs for several problems in the biological sciences and compare PSO performance relative to the differential evolution algorithm, another popular metaheuristic algorithm in the engineering literature.

  • Pressure point driven evolutionary algorithm for many-objective optimization
    Swarm Evol. Comput. (IF 6.330) Pub Date : 2019-10-29
    Jianghan Zhu, Huangke Chen, Guohua Wu, Chen Li, Haifeng Li

    How to form good trade-offs between convergence and diversity for many-objective optimization is an ongoing challenge. With the increase in objectives, the ratio of non-dominated solutions in a population increases sharply, which challenges individual discrimination and selection pressures. Besides, the Pareto fronts of many-objective optimization problems (MaOPs) have various shapes, such as disconnected, degenerate, biased deceptive, and mixed shapes, which further challenge the trade-offs between convergence and diversity. To address the above issues, we propose a pressure point driven Evolutionary Algorithm (proEA). Specifically, a pressure point based strategy is developed to update the pressure point, such strengthening the selection pressure. Then, the reference vector based environmental selection strategy is improved by integrating an angle-based selection strategy to account for the complicated shapes of Pareto fronts. Finally, a series of numerical experiments are conducted to compare the proposed proEA with five representative algorithms in the context of 36 test instances with 5, 7, 10, and 15 objectives. The experimental results demonstrate the superiority of algorithm proEA.

  • Self-adapting self-organizing migrating algorithm
    Swarm Evol. Comput. (IF 6.330) Pub Date : 2019-10-21
    Lenka Skanderova, Tomas Fabian, Ivan Zelinka

    The self-organizing migrating algorithm is a population-based algorithm belonging to swarm intelligence, which has been successfully applied in several areas for solving non-trivial optimization problems. However, based on our experiments, the original formulation of this algorithm suffers with some shortcomings as loss of population diversity, premature convergence, and the necessity of the control parameters hand-tuning. The main contribution of this paper is the development of the novel algorithm mitigating the mentioned issues of the original self-organizing migrating algorithm. We have applied the ideas of the self-adaptation of the control parameters, the different principle of the leader creation, and the external archive of the successful particles. For some special cases, we are able to utilize the differential grouping to detect the interacting variables effectively removing the need for the perturbation parameter. To prove the efficiency of the novel algorithm, we have performed experiments on fifteen unconstrained problems from the CEC 2015 benchmark. The algorithm is compared with seven well-known evolutionary and swarm algorithms. The results of the experiments indicate that the mechanisms used in the novel algorithm had significantly improved the performance of the original self-organizing migrating algorithm, and the new algorithm can now compete with the selected algorithms.

  • The influence of franchisee loss on logistics network: A new perspective from NK model
    Swarm Evol. Comput. (IF 6.330) Pub Date : 2019-10-19
    Yihang Feng, Bin Hu, Changying Ke

    Franchisee has become a powerful tool for many logistics enterprises in affiliate mode to seize market share. But affiliate mode has emerged many drawbacks that can aggravate the divergence of interests between franchisees and headquarter and then leads to the problem of franchisee loss. In this paper, the franchisee loss problem is studied from the perspective of vulnerability. The loss of a franchisee is seen as a node that has been attacked. The performance change before and after an attack is used to reflect the strength of a network's vulnerability. Efficacy and efficiency are proposed to measure the performance change based on NK model, which specializes in studying complex adaptive system such as logistics network. Results show that efficacy and efficiency vary according to network scale. For relatively small networks, their efficacy shows more obvious fluctuation with the variation of network scale than efficiency does. In these networks, the redundancy of network nodes is high. But the percentage of redundant nodes decreases with the increase of scale and the efficacy can be improved with the reduction of redundant nodes. The network structure of them is not stable. As for relatively large networks, efficiency fluctuates more apparently with the change of complexity than efficacy does. In these networks, most nodes are influential and the percentage of influential nodes shows obvious change with complexity. The network structure tends to be stable. In conclusion, the study on the influence of franchisee loss from vulnerability can provide us with greater visibility and insight into the organization structure of logistics networks. Corresponding cases are introduced to confirm our conclusions.

  • An improved particle swarm optimization algorithm for dynamic job shop scheduling problems with random job arrivals
    Swarm Evol. Comput. (IF 6.330) Pub Date : 2019-10-18
    Zhen Wang, Jihui Zhang, Shengxiang Yang

    Random job arrivals that happen frequently in manufacturing practice may create a need for dynamic scheduling. This paper considers an issue of how to reschedule the randomly arrived new jobs to pursue both performance and stability in a job shop. Firstly, a mixed integer programming model is established to minimize three objectives, including the discontinuity rate of new jobs during the processing, the makespan deviation of initial schedule, and the sequence deviation on machines. Secondly, four match-up strategies from references are modified to determine the rescheduling horizon. Once new jobs arrive, the rescheduling process is immediately triggered with ongoing operations remain. The ongoing operations are treated as machine unavailable constraints (MUC) in the rescheduling horizon. Then, a particle swarm optimization (PSO) algorithm with improvements is proposed to solve the dynamic job shop scheduling problem. Improvement strategies consist of a modified decoding scheme considering MUC, a population initialization approach by designing a new transformation mechanism, and a novel particle movement method by introducing position changes and a random inertia weight. Lastly, extensive experiments are conducted on several instances. The experiments results show that the modified rescheduling strategies are statistically and significantly better than the compared strategies. Moreover, comparative studies with five variants of PSO algorithm and three state-of-the-art meta-heuristics demonstrate the high performance of the improved PSO algorithm.

  • Line-prioritized environmental selection and normalization scheme for many-objective optimization using reference-lines-based framework
    Swarm Evol. Comput. (IF 6.330) Pub Date : 2019-10-12
    Deepak Sharma, Pradyumn Kumar Shukla

    The Pareto-dominance-based multi-objective evolutionary algorithms (MOEAs) have been successful in solving many test problems and other engineering optimization problems. However, their performance gets affected when solving more than 3-objective optimization problems due to lack of sufficient selection pressure. Many attempts have been made by the researchers toward improving the environmental selection of those MOEAs. One such attempt is selecting solutions using the reference-lines-based framework. In this paper, an efficient environmental selection and normalization scheme are proposed for this framework. The environmental selection operator is developed to equally prioritize solutions associated with different lines drawn from the origin and the reference points. A normalization scheme is also suggested in which the extreme point is used which gets updated on the designed rules. The framework is referred to as LEAF, and it is tested on 3-, 5-, 10-, and 15-objective DTLZ and WFG test instances. LEAF demonstrates its outperformance on almost all DTLZ instances and shows better performance on most of WFG instances over six MOEAs from the literature.

  • Improved Self-adaptive Search Equation-based Artificial Bee Colony Algorithm with competitive local search strategy
    Swarm Evol. Comput. (IF 6.330) Pub Date : 2019-10-11
    Gürcan Yavuz, Doğan Aydın

    The search equations and the local search procedures used in Artificial Bee Colony (ABC) algorithm are two key components that affect the performance of the algorithm. However, there is no search equation or local search that provides good results for all problem types. In this article, an ABC algorithm called “Self-adaptive Search Equation-based Artificial Bee Colony” (SSEABC) is proposed which can determine the appropriate local search procedure and the search equation internally during execution. SSEABC integrates three strategies into the canonical ABC algorithm. The first strategy is a self-adaptive strategy that determines the appropriate search equations for a particular problem by eliminating improper ones from a pool consisting of randomly generated search equations. The second strategy is a competitive local search selection. It decides the most effective local search procedure by comparing the performances of SSEABC, Mtsls1 and IPOP-CMA-ES. The third strategy is an incremental population size strategy, which is based on adding new food sources located around the best-so-far food source position after a predefined number of iterations. This helps to increase convergence speed. The SSEABC algorithm is tested on benchmark functions proposed in the CEC'14 abd CEC'17 competition on single objective bound constrained real-parameter numerical optimization. SSEABC is compared with several ABC variants, competitor algorithms of CEC'14 and CEC'17, and several state-of-the-art algorithms. Finally, we applied SSEABC to the infinite impulse response (IIR) system identification problem as an engineering application. The results showed the superiority of the SSEABC algorithm.

  • Divide-and-conquer based non-dominated sorting with Reduced Comparisons
    Swarm Evol. Comput. (IF 6.330) Pub Date : 2019-10-08
    Sumit Mishra, Sriparna Saha, Samrat Mondal, Carlos A. Coello Coello

    Non-dominated sorting has attracted a lot of attention of the research community due to its use in solving multi- and many-objective optimization problems. In recent years, several approaches for non-dominated sorting have been proposed. In this paper, we have developed a non-dominated sorting framework, namely DCNSRC (Divide-and-Conquer based Non-dominated Sorting with Reduced Comparisons). Based on this framework, two approaches have been proposed by varying the search technique. These approaches perform a lower number of dominance comparisons than various other approaches. The duplicate solutions are also handled efficiently. These approaches save various comparisons while comparing the two solutions. The proposed approaches are validated using some theoretical analyses. The number of dominance comparisons performed by the proposed framework are theoretically analyzed in three different scenarios, both in the worst and the best cases. Experimental results on synthetic datasets and the benchmark problems show the superiority of the proposed approach over state-of-the-art algorithms.

  • Multilevel thresholding by fuzzy type II sets using evolutionary algorithms
    Swarm Evol. Comput. (IF 6.330) Pub Date : 2019-10-05
    Diego Oliva, Sayan Nag, Mohamed Abd Elaziz, Uddalok Sarkar, Salvador Hinojosa

    The image segmentation based on Multilevel thresholding has attracted more attention in recent years, they have been used in different applications. Therefore, several evolutionary computation methods have been proposed to determine the optimal threshold values. However, these approaches suffer from some limitations such as the stagnation point which leads to degradation the quality of the segmented image. In addition, most of them used either Otsu or Kapur as a fitness function, and the complexity of these methods is increased with increasing the threshold levels. Moreover, they don't provide accurate results. To overcome such situations, in this paper is proposed the use of evolutionary computation algorithms combined with the Type II Fuzzy Entropy as the objective function. Such methods are the Backtracking Search Optimization Algorithm (BSA) and the Salp Swarm Algorithm (SSA). The BSA and SSA are able to avoid the limitation of similar techniques for image threshold because the objective function removes the ambiguities helping to find more accurate results. The BSA and SSA are used to find the best parameters of the Type II Fuzzy Entropy that extracts the optimal thresholds that properly segment the histogram of a digital image. To evaluate the performance of the proposed two methods, a set of experiments are performed using a set of benchmark images which have different characteristics. Moreover, the experiments are also performed over medical images from blood cells. The experimental results indicate that the proposed two methods have a good performance. However, they provide better performance than other algorithms in terms of quality and accuracy.

  • Graph structure optimization of Genetic Network Programming with ant colony mechanism in deterministic and stochastic environments
    Swarm Evol. Comput. (IF 6.330) Pub Date : 2019-10-04
    Mohamad Roshanzamir, Maziar Palhang, Abdolreza Mirzaei

    Evolutionary Algorithms are of the most successful algorithms in solving various optimization problems. Genetic network programming is one of the Evolutionary Algorithms with good capabilities in agent control problems. In this algorithm, the individuals’ structure is a directed graph. Using this structure, it is possible to model the solution of many complex problems. However, in this algorithm, crossover and mutation operators repeatedly break the structures of individuals and make new ones. Although this can lead to better structures, it may break suitable structures in elite individuals. Meanwhile, in stochastic environments, each time an individual is evaluated, it leads to different fitness values. So, calculating the fitness value of individuals requires evaluating each individual several times. This extremely decreases the evolution process speed. In this paper, inspired by mechanisms of ant colony algorithm, a new method is proposed to prevent the algorithm from iteratively breaking down the structures of individuals. This method improves the performance of individuals from one generation to the next using a constructive process. Unlike generative process that the individuals are generated by combination of some others, in constructive process they are produced according to the experience of previous generations. Using this mechanism, we not only prevent breaking suitable structures but also can manage uncertainty in stochastic environments. Our proposed method is used to solve two agent control problems when the environment is deterministic or stochastic. The results show that the proposed algorithm has very high ability in creating an efficient decision making strategies especially in stochastic environments.

  • Fast multiobjective immune optimization approach solving multiobjective interval number programming
    Swarm Evol. Comput. (IF 6.330) Pub Date : 2019-09-23
    Zhuhong Zhang

    As an uncertain programming model with multiple conflicting performance indices and bounded uncertain parameter(s), multiobjective interval number programming is a daunting topic in the fields of mathematics and intelligent optimization. Despite its comprehensive engineering application background, it is still open, and further studies are needed on basic theory, model transformation and intelligent optimizers. Therein, this work not only gropes a new shortcut to tackling one such model, but also proposes a novel multiobjective interval number immune optimization algorithm. The intrinsic solution relation between the model and a related natural interval extension one is discovered in terms of the new concept of optimal-value vector solution, by which a fast interval nondominated sorting approach is acquired. The algorithm mainly consists of population division, proliferation, evolution, selection and memory update, in which a co-evolutionary mechanism is designed to promote the current population to move quickly towards the Pareto front with the assistance of the sorting approach and an external archive set. The algorithm's resource consumption depends mainly on the archive's size. Comparative experiments have validated that the optimizer can effectively perform well over the compared approaches and is significantly superior to them with regard to efficiency.

  • Frequent pattern mining assisted energy consumption aceevolutionary optimization approach based on surrogate model at GCC compile time
    Swarm Evol. Comput. (IF 6.330) Pub Date : 2019-09-14
    Nia Youcong, Xin Dua, Peng Ye, Ruliang Xiao, Yuan Yuan, WangBiao Li

    The evolutionary algorithms have been used to improve the energy consumption of embedded software by searching the optimal compilation options of GCC compiler. However, these algorithms do not consider the complex multivariate interactions between compilation options, which has negative effect on solution quality and convergence rate. Furthermore, it is also not investigated how to reduce the computational cost incurred by fitness evaluation using adaptive surrogate models. To address these problems, a novel approach to energy consumption optimization at GCC compile time, named FACET, is proposed in this paper. Firstly, the high-frequency multivariate interactions with positive effect on energy consumption are captured during evolution by our proposed frequent patterns mining algorithm. Secondly, the captured multivariate positive interactions are regarded as heuristic knowledge to guide the design of two mutation operators of ADD and DELETE. Thirdly, the adaptive surrogate models are introduced to assist fitness evaluation in order to reduce the high time-consumption. Finally, we evaluate our approach on 8 typical problem instances, drawn from 5 categories using 6 measurement metrics. Our results show that FACET was significantly better (p<0.05) than Tree-EDA in terms of solution quality and convergence rate in all compared experiments (with high Vargha-Delaney Aˆ12 effect size). Specifically, FACET can reduce energy consumption by 2.5% on average and 16.4% at best; accelerate convergence by 36.3% on average and 80.6% at best. Moreover, FACET was also significantly better (p<0.05) than Tree-EDA and SM-GA in terms of multivariate positive correlation in the optimal solutions (with high Vargha-Delaney Aˆ12 effect size). FACET can enhance the proportion of positive correlation by 23.3% against Tree-EDA and by 36.4% against SM-GA on average. At the same time, the adaptive surrogate models significantly save time by 73.96% on average while their accuracy reaches as high as above 99%. In addition, the adaptive surrogate models have no significant negative impact on solution quality of FACET.

  • MOEA based memetic algorithms for multi-objective satellite range scheduling problem
    Swarm Evol. Comput. (IF 6.330) Pub Date : 2019-09-14
    Yonghao Du, Lining Xing, Jiawei Zhang, Yingguo Chen, Yongming He

    Satellite range scheduling plays a very important role in guaranteeing the normal operation and the real-time control of in-orbit satellites. Although there appears a stronger demand for multi-objective optimization of satellite monitoring departments, multiple scheduling criteria are rarely considered simultaneously. To address the multi-objective satellite range scheduling problem (MOSRSP), a general MOEA based memetic algorithm (MOEA-MA) framework is proposed, which optimizes the failure rate of ground-satellite communication requests and the load-balance degree of remote-tracking antennas. Based on a novel decision model for MOSRSP, the conflict-resolution and load-balance operators and the tabu search metaheuristic are designed to implement the local search operations in the MOEA-MA. Different types of the MOEAs, including the domination-based MOEAs, decomposition-based MOEAs and metric-based MOEAs are adopted to implement the evolutionary operations in the MOEA-MA. The highlight of this paper is the effective application of the MOEA-MAs to practical scheduling problems, where the two most concerning objectives are well addressed. The MOEA-MAs that adopt five well-known MOEAs are given and examined by the Benchmarks problems. Computational results indicate that the MOEA-MAs outperform the original MOEAs in terms of the metrics of coverage, hypervolume and spacing, which show good performance and application prospect for the MOSRSP.

  • A discrete multi-objective fireworks algorithm for flowshop scheduling with sequence-dependent setup times
    Swarm Evol. Comput. (IF 6.330) Pub Date : 2019-09-11
    Lijun He, Wenfeng Li, Yu Zhang, Yulian Cao

    Multi-objective flow shop scheduling problem with sequence-dependent setup times (MOFSP-SDST) is a class of important production scheduling problem with strong industry background. In this paper, a MOFSP-SDST mathematic model with the objectives of total production cost, makespan, mean flow time and mean idle time of machines is developed. To solve this multi-objective model, a novel multi-objective approach based on fuzzy correlation entropy analysis is proposed firstly. In this multi-objective approach, two types of objective function value sequences, namely the referenced function value sequence and comparable function value sequence, are constructed and mapped into two types of fuzzy sets by a modified relative membership function. The fuzzy correlation entropy coefficient between the two types of fuzzy sets is used to select better solutions in a multi-objective problem. A discrete multi-objective fireworks algorithm (DMOFWA) is proposed to address the MOFSP-SDST. In the DMOFWA, a new multi-objective approach is adopted to handle the multiple objectives and guide the search of the algorithm. Two kinds of machine learning strategies are adopted, namely opposition-based learning (OBL) and clustering analysis (CA). The OBL is employed to learn from the current search space and improve the exploration ability of DMOFWA, and the CA based on fuzzy correlation entropy coefficient is proposed to cluster firework individuals. Computational and statistical results show that the novel multi-objective approach, OBL and CA strategies can effectively improve the performance of DMOFWA. Furthermore, the results indicate that DMOFWA performs better than four state-of-the-art comparison algorithms.

  • A local cooperative approach to solve large-scale constrained optimization problems
    Swarm Evol. Comput. (IF 6.330) Pub Date : 2019-09-10
    Adan E. Aguilar-Justo, Efrén Mezura-Montes

    Cooperative Co-evolutionary algorithms are very popular to solve large-scale problems. A significant part of these algorithms is the decomposition of the problems according to the variables interaction. In this paper, an approach based on a memetic scheme, where its local stage (and not the global stage) is guided by the decomposition method (Local Cooperative Search LoCoS), is presented to solve large-scale constrained optimization problems. Two decomposition methods are tested: the improved version of the Variable Interdependence Identification for Constrained problems and Differential Grouping version 2. A recently-proposed benchmark with eighteen test problems with different features is solved to assess the performance of LoCoS when compared against a similar memetic algorithm but without decomposition and also against a state-of-the-art cooperative co-evolutionary algorithm. The results show a faster convergence, better final results and higher feasibility ratio by LoCosS with respect to the values provided by the compared algorithms.

  • Chaotic particle swarm optimization with sigmoid-based acceleration coefficients for numerical function optimization
    Swarm Evol. Comput. (IF 6.330) Pub Date : 2019-09-09
    Dongping Tian, Xiaofei Zhao, Zhongzhi Shi

    Particle swarm optimization (PSO) is a stochastic computation technique motivated by intelligent collective behavior of some animals, which has been widely used to address many hard optimization problems. However, like other evolutionary algorithms, PSO also suffers from premature convergence and entrapment into local optima when dealing with complex multimodal problems. In this paper, we propose a chaotic particle swarm optimization with sigmoid-based acceleration coefficients (abbreviated as CPSOS). On the one hand, the frequently used logistic map is applied to generate well-distributed initial particles. On the other hand, the sigmoid-based acceleration coefficients are formulated to balance the global search ability in the early stage and the global convergence in the latter stage. In particular, two sets of slowly varying function and regular varying function embedded update mechanism in conjunction with the chaos based re-initialization and Gaussian mutation strategies are employed at different evolution stages to update the particles during the whole search process, which can effectively keep the diversity of the swarm and get out of possible local optima to continue exploring the potential search regions of the solution space. To validate the performance of CPSOS, a series of experiments are conducted and the simulation results reveal that the proposed method can achieve better performance compared to several state-of-the-art PSO variants in terms of solution accuracy and effectiveness.

  • A three-level particle swarm optimization with variable neighbourhood search algorithm for the production scheduling problem with mould maintenance
    Swarm Evol. Comput. (IF 6.330) Pub Date : 2019-09-07
    Xiaoyue Fu, Felix T.S. Chan, Ben Niu, Nick S.H. Chung, Ting Qu

    To improve the reliability of production systems in the plastics industry, researchers are now taking mould maintenance into consideration, besides machine maintenance, in the production scheduling problem. Different strategies and approaches have been proposed to solve the production scheduling with mould maintenance (PS-MM) problem. However, it remains a challenge to provide a satisfactory solution. In this research, a new hybrid metaheuristic algorithm (TLPSO-VNS algorithm) is proposed, which is a combination of the three-level particle swarm optimization (TLPSO) algorithm devised in this study and variable neighbourhood search (VNS). Differing from the joint scheduling strategies used in existing research, this study divides the integrated problem into three sub-problems and solves them through three interrelated PSOs named TLPSO. Then, the solutions obtained by TLPSO are enhanced by VNS. The key characteristics of TLPSO and VNS are employed simultaneously to achieve superior solutions in solving the addressed optimization problem. In the proposed hybrid algorithm, the TLPSO performs a global search whereas the VNS has a local search role. These two techniques complement each other to enhance the search diversification and intensification. Numerical experiments on a variety of simulated scenarios show the efficiency and effectiveness of the proposed algorithm by comparing it with other algorithms.

  • Multi-scenario microgrid optimization using an evolutionary multi-objective algorithm
    Swarm Evol. Comput. (IF 6.330) Pub Date : 2019-09-04
    Wenhua Li, Rui Wang, Tao Zhang, Mengjun Ming, Hongtao Lei

    Multi-scenario microgrid optimization arises regularly in real life. It refers to finding optimal scheduling strategies of a microgrid under multiple scenarios where each scenario corresponds to a specific working condition. For example, in an industrial park, there are often many users with different load demands. We need to efficiently find the optimal scheduling strategies for all users. The easiest way is to conduct the operation search for each user separately, which however, is obviously inefficient. Inspired by the underlying parallelism of evolutionary multi-objective optimization (EMO), this study proposes to optimize all scenarios simultaneously, i.e., finding the optimal scheduling strategies for all users in a single algorithm run. Specifically, the multi-scenario microgrid optimization problem is transformed into a bi-objective problem in which one objective is to minimize the number of scenarios and the other is to minimize the overall cost of the microgrid. The bi-objective problem is then solved by a typical EMO algorithm. The obtained Pareto optimal solutions correspond to the optimal scheduling strategies for different scenarios. Experimental results show that the proposed method is both effective and efficient, and can find more appropriate scheduling strategies than dealing with each scenario individually.

  • Multiobjective differential evolution enhanced with principle component analysis for constrained optimization
    Swarm Evol. Comput. (IF 6.330) Pub Date : 2019-08-30
    Wei Huang, Tao Xu, Kangshun Li, Jun He

    Multiobjective evolutionary algorithms (MOEAs) have been successfully applied to a number of constrained optimization problems. Many of them adopt mutation and crossover operators from differential evolution. However, these operators do not explicitly utilise features of fitness landscapes. To improve the performance of algorithms, this paper aims at designing a search operator adapting to fitness landscapes. Through an observation, we find that principle component analysis (PCA) can be used to characterise fitness landscapes. Based on this finding, a new search operator, called PCA-projection, is proposed. In order to verify the effectiveness of PCA-projection, we design two algorithms enhanced with PCA-projection for solving constrained optimization problems, called PMODE and HECO-PDE, respectively. Experiments have been conducted on the IEEE CEC 2017 competition benchmark suite in constrained optimization. PMODE and HECO-PDE are compared with the algorithms from the IEEE CEC 2018 competition and another recent MOEA for constrained optimization. Experimental results show that an algorithm enhanced with PCA-projection performs better than its corresponding opponent without this operator. Furthermore, HECO-PDE is ranked first on all dimensions according to the competition rules. This study reveals that decomposition-based MOEAs, such as HECO-PDE, are competitive with best single-objective and multiobjective evolutionary algorithms for constrained optimization, but MOEAs based on non-dominance, such as PMODE, may not.

  • A cluster based PSO with leader updating mechanism and ring-topology for multimodal multi-objective optimization
    Swarm Evol. Comput. (IF 6.330) Pub Date : 2019-08-26
    Weizheng Zhang, Guoqing Li, Weiwei Zhang, Jing Liang, Gary G. Yen

    In the multimodal multi-objective optimization problems (MMOPs), there exists more than one Pareto optimal solutions in the decision space corresponding to the same location on the Pareto front in the objective space. To solve the MMOPs, the designed algorithm is supposed to converge to the accurate and well-distributed Pareto front, and at the same time to search for the multiple Pareto-optimal solutions in the decision space. This paper presents a new cluster based particle swarm optimization algorithm (PSO) with leader updating mechanism and ring-topology for solving MMOPs. Multiple subpopulations are formed by a new decision variable clustering method with the aim of searching for the multiple Pareto optima solutions and maintaining the diversity. Global-best PSO is employed for independent evolution of subpopulations, while local-best PSO with ring topology is used to enhance the information interaction among subpopulations. Seamlessly integrated, the proposed algorithm provides a good balance between exploration and exploitation. In addition, leader updating strategy is introduced to identify the best leaders in PSO. The performance of the proposed algorithm is compared with six state-of-the-art designs over 11 multimodal multi-objective optimization test functions. Experimental results demonstrate the effectiveness of the proposed algorithm.

  • PBI function based evolutionary algorithm with precise penalty parameter for unconstrained many-objective optimization
    Swarm Evol. Comput. (IF 6.330) Pub Date : 2019-08-24
    Chenglin Yang, Cong Hu, Yu Zou

    Fixed or experiential penalty parameter of the penalty-based boundary intersection (PBI) function method cannot simultaneously ensure the convergence and diversity for all shape of Pareto front (PF). Too large penalty parameter may lead to bad convergence while too small parameter can not ensure the diversity. Specially, if the penalty parameter is too small, some reference weight vectors may have no solution on it. This error is hard to be rectified. In this paper, we prove that the lower bound of the penalty parameter is determined by three factors. The first one is the shape of the PF. The second one is the cosine distance between two adjacent reference vectors. The third one is the number of objectives. We deduce the lower bound of the penalty parameter. Once the penalty parameter was calculated, an individual with minimal PBI function is attached to the corresponding reference vector. The minimal-PBI-function-first principle is used in the environmental selection to guarantee the wideness and uniformity of the solution set. The time complexity is low. The proposed method is compared with other three state-of-the-art many-objective evolutionary algorithms on the unconstrained test problems MaOP, DTLZ and WFG with up to fifteen objectives. The experimental results show the competitiveness and effectiveness of the proposed algorithm in both time efficiency and accuracy.

  • Model and metaheuristics for robotic two-sided assembly line balancing problems with setup times
    Swarm Evol. Comput. (IF 6.330) Pub Date : 2019-08-23
    Zixiang Li, Mukund Nilakantan Janardhanan, Qiuhua Tang, S.G. Ponnambalam

    Two-sided robotic assembly lines are employed to assemble large-sized high-volume products, where robots are allocated to the workstations to perform the tasks and human workers are replaced for achieving lower cost and greater flexibility in production. In the two-sided robotic assembly lines, setup times are unavoidable and it has been ignored in most of the reported works. There has been limited attention on this till date. This paper focusses on the robotic two-sided assembly line with consideration of sequence-dependent setup times and robot setup times. A new mixed integer linear programming model is developed with the objective of optimizing the cycle time. Due to the NP-hard nature of the considered problem, this paper proposes a set of metaheuristics to solve this considered problem, where two main scenarios with low and high setup time's variability are considered. Computational results verify that this new model is capable to achieve the optimal solutions for small-size instances whereas the simple adoption of the published mathematical model might produce wrong solutions for the considered problem. A comprehensive study with 13 algorithms demonstrates that the two variants of artificial bee colony algorithm and migrating bird optimization algorithm are capable to achieve the optimality for small-size instances and to obtain promising results for large-size instances.

  • Review of methodologies and tasks in swarm robotics towards standardization
    Swarm Evol. Comput. (IF 6.330) Pub Date : 2019-08-15
    Nadia Nedjah, Luneque Silva Junior

    Swarm Robotics (SR) is an extension of the study of Multi-Robot Systems that exploits concepts of communication, coordination and collaboration among a large number of robots. The massive parallelization yielded by the robots working together can make a task faster than in the case of the usage of a single complex robot. One of the main aspects in robotic swarms is that the control is decentralized by definition and distributed among the robots of the swarm, improving the system robustness and fault-tolerance. Furthermore, this characteristic often allows the emergence of collective behaviors from the robot's interaction with each other and with the environment through their embodied sensors and actuators. In most cases, the number of inputs from sensor readings turns analytical solutions hard or even impossible. Thus, many ad-hoc approaches are contributed to deal with the situation at hand. The main goal of this review is to find out, through the study of existing research works of the field, the reason behind the lack of exploitation of swarm robotic systems in real-world applications. For this purpose, we first review the different possibilities of study in SR: physical and simulated robotic platforms, development methodologies and the variety of basic tasks and collective behaviors. We then briefly describe some fields related do SR that have a big impact on the development of SR. After that, based on existing taxonomies found in literature, we categorize existing research works regarding SR in two large main groups: those that deal with SR design and those that deal with tasks as required in SR. The review of both existing robots and techniques in the literature show a diversity of approaches to discuss SR issues. Nonetheless, it is easily noticeable from these works that there is a clamant absence of solid real-world applications of SR. An analysis of the interests and bottlenecks of this field indicates that the number of research works is smaller than those in other related areas. This suggests that, even though with many research studies, the field of SR is not yet mature enough, mainly due the absence of a universal methodology and generic robots that can be used in any, or at least in many, applications. Thus, we emphasize, discuss and analyze the urgent need for standardization of many aspects in SR, including hardware and software, as to allow a possible flourishing of SR applicability to real-world applications. This standardization could accelerate a great deal the field of SR, thus facilitating the development of SR solutions for applications that impact our daily life.

  • A novel Error-Correcting Output Codes algorithm based on genetic programming
    Swarm Evol. Comput. (IF 6.330) Pub Date : 2019-08-13
    Ke-Sen Li, Han-Rui Wang, Kun-Hong Liu

    Error-Correcting Output Codes (ECOC) is widely used in the field of multiclass classification. As an optimal codematrix is key to the performance of an ECOC algorithm, this paper proposes a genetic programming (GP) based ECOC algorithm (GP-ECOC). In the design of individual of our GP, each terminal node represents a class, and nonterminal nodes combine the classes in their child nodes. In this way, an individual is a class combination tree, and represents an ECOC codematrix. A legality checking process is embedded in our algorithm to check each codematrix, so as to ensure each codematrix satisfying ECOC constraints. Those violating the constraints will be corrected by a proposed Guided Mutation operator. Before fitness evaluation, a local optimization algorithm is proposed to append new columns for tough classes, so as to improve the generalization ability of each individual and accelerate the evolutionary speed. In this way, our GP can evolve optimal codematrices through the evolutionary process. Experiments show that compared with other ensemble algorithms, our algorithm can achieve stable and high performances with relatively small ensemble scales on various UCI data sets. To the best of our knowledge, it is the first time that GP has been applied to implement the ECOC encoding algorithm. Our Python code is available at https://github.com/samuellees/gpecoc.

  • A large-scale multiobjective satellite data transmission scheduling algorithm based on SVM+NSGA-II
    Swarm Evol. Comput. (IF 6.330) Pub Date : 2019-08-12
    Jiawei Zhang, Lining Xing, Guansheng Peng, Feng Yao, Cheng Chen

    The satellite data transmission traffic appears a considerable growth tendency with the increase in the number of satellites and the client requirements, so how to solve the large-scale multiobjective satellite data transmission scheduling problem (SDTSP) within a valid period of time has become more and more important. In this context, a multiobjective satellite data transmission model is developed for the practical application, and a novel SVM + NSGA-II algorithm is proposed based on the periodicity of resource confliction in satellite data transmission, the large-scale characteristic of SDTSP and the multidimensional characteristic of the optimization objectives. Experimental results have demonstrated that SVM + NSGA-II can efficiently solve the large-scale multiobjective SDTSP in a very short period of time on the basis of ensuring the satisfactory optimization objectives by comparing with the other four state-of-the-art MOEAs.

  • A Multi Ant System based hybrid heuristic algorithm for Vehicle Routing Problem with Service Time Customization
    Swarm Evol. Comput. (IF 6.330) Pub Date : 2019-08-08
    Yuan Wang, Ling Wang, Zhiping Peng, Guangcai Chen, Zhaoquan Cai, Lining Xing

    This article introduces an extension of Vehicle Routing Problem with Time Window (VRPTW) called Vehicle Routing Problem with Time Window Considering Service Time Customization (VRPTW-STC). This problem considers total service time as a problem objective. We give out the mathematical model of VRPTW-STC and an ant-based heuristic algorithm to solve it. The algorithm that we apply to this problem is called Multi-Ant System with Local Search (MAS-LS). It combines MAS algorithm with four kinds of local search operators. And, a customer selection heuristic is designed to help find customers that can be added extra service time. Finally, we test performance of MAS-LS on selected Solomon's benchmarks and Homberger's benchmarks. Comparison algorithms include ant-based heuristics, population-based heuristics and variable neighborhood search heuristics. Computation experiment results show that MAS-LS has a good and robust performance of finding solutions with lower travelling distance in most tested instances.

  • Shallow and deep neural network training by water wave optimization
    Swarm Evol. Comput. (IF 6.330) Pub Date : 2019-08-07
    Xiao-Han Zhou, Min-Xia Zhang, Zhi-Ge Xu, Ci-Yun Cai, Yu-Jiao Huang, Yu-Jun Zheng

    It is well known that the performance of artificial neural networks (ANNs) is significantly affected by their structure design and parameter selection, for which traditional training methods have drawbacks such as long training times, over-fitting, and premature convergence. Evolutionary algorithms (EAs) have provided an effective tool for ANN parameter optimization. However, simultaneously optimizing ANN structures and parameters remains a difficult problem. In this study, we adapt water wave optimization (WWO), a relatively new EA, for optimizing both the parameters and structures of ANNs, including classical shallow ANNs and deep neural networks (DNNs). We use a variable-dimensional solution encoding to represent both the structure and parameters of an ANN, and adapt WWO propagation, refraction, and breaking operators to efficiently evolve variable-dimensional solutions to solve the complex network optimization problems. Computational experiments on a variety of benchmark datasets show that the WWO algorithm achieves a very competitive performance compared to other popular gradient-based algorithms and EAs.

  • Evolving rollout-justification based heuristics for resource constrained project scheduling problems
    Swarm Evol. Comput. (IF 6.330) Pub Date : 2019-08-07
    Shelvin Chand, Hemant Singh, Tapabrata Ray

    Resource constrained project scheduling is critical in logistic and planning operations across a range of industries. An interesting heuristic for solving this problem is the Rollout-Justification (RJ) procedure. This procedure, which has conceptual similarities with dynamic programming, incrementally builds a solution by identifying the next activity to schedule based on the projections made using a guiding priority rule (heuristic) coupled with forward-backward local search. A critical component that affects the performance of RJ procedure is the guiding priority rule (or a set of rules). In this study, instead of using existing rules from literature, we aim to evolve new priority rules using genetic programming, and systematically investigate their use with the RJ procedure. Apart from evolving new rules, we also investigate new ways of integrating/utilizing the rules within RJ procedure. To this end we consider the use of both forward and backward scheduling, independent and cohesive ensemble rule approaches, limited and unlimited number of function evaluations, among others. We use data from the project scheduling library (PSPLib) to train and test the evolved rules and their integration with RJ. A comprehensive set of numerical experiments are performed to benchmark the rules evolved using the proposed approach against a range of existing rules. The results demonstrate the competence and potential of the proposed approach, both in terms of accuracy and complexity.

  • A hybrid enhanced bat algorithm for the generalized redundancy allocation problem
    Swarm Evol. Comput. (IF 6.330) Pub Date : 2019-08-07
    Yue Xu, Dechang Pi

    A majority of existing works dealing with redundancy allocation problems are based on traditional series-parallel structures. While in many real-life scenarios, the way of connecting subsystems is not limited to a series-only configuration. This paper considers a generalized redundancy allocation problem (GRAP), where the system structure is a more general network. Since the reliability evaluation in GRAPs is a NP-hard problem and the traditional exact symbolic reliability calculation is not suitable, a cellular automata based monte carlo simulation method is implemented in this paper to estimate the system reliability. It is a relatively simple but effective method without knowing the MPs/MCs. Moreover, to deal with GRAPs, a novel discrete bat algorithm is proposed in this paper with a goal of determining an optimal system structure that achieves the minimum cost under several constraints by using redundant components in parallel. Computational complexity of the proposed algorithm is also calculated in this paper. In the end, three experiments are carried out based on ten networks to set parameters, measure the effectiveness of the modifications, and compare with other state-of-the-art algorithms, separately. The reported computational results show that the proposed algorithm is powerful, which is more superior on this sort of problems.

  • A high-efficiency adaptive artificial bee colony algorithm using two strategies for continuous optimization
    Swarm Evol. Comput. (IF 6.330) Pub Date : 2019-08-02
    Xiaoyu Song, Ming Zhao, Qifeng Yan, Shuangyun Xing

    It has always been a problem faced by Artificial Bee Colony (ABC) algorithm that how to adjust exploration and exploitation dynamically in the evolution process. In order to overcome this problem, this paper presents a highly efficient variant of ABC algorithm which is two-strategy adaptive. Among the two proposed search strategies, one has strong exploration ability and the other has strong exploitation ability; Based on the adaptability of the two search strategies to the problem solving and the search process, the selection probability of each search strategy is dynamically adjusted according to success rate, and then the cooperative optimization of the two search strategies is realized to improve the performance of the algorithm. It can be seen that the improved algorithm is enhanced significantly on accuracy of solution and success rate from comparing experiment results with the other state-of-the-art ABC algorithms.

  • Evolutionary dynamic constrained optimization: Test suite construction and algorithm comparisons
    Swarm Evol. Comput. (IF 6.330) Pub Date : 2019-07-30
    Yong Wang, Jian Yu, Shengxiang Yang, Shouyong Jiang, Shuang Zhao

    Many real-world applications can be modelled as dynamic constrained optimization problems (DCOPs). Due to the fact that objective function and/or constraints change over time, solving DCOPs is a challenging task. Although solving DCOPs by evolutionary algorithms has attracted increasing interest in the community of evolutionary computation, the design of benchmark test functions of DCOPs is still insufficient. Therefore, we propose a test suite for DCOPs. A dynamic unconstrained optimization benchmark with good time-varying characteristics, called moving peaks benchmark, is chosen to be the objective function of our test suite. In addition, we design adjustable dynamic constraints, by which the size, number, and change severity of the feasible regions can be flexibly controlled. Furthermore, the performance of three dynamic constrained optimization evolutionary algorithms is tested on the proposed test suite, one of which is presented in this paper, named dynamic constrained optimization differential evolution (DyCODE). DyCODE includes three main phases: 1) the first phase intends to enter the feasible region from different directions promptly via a multi-population search strategy; 2) in the second phase, some excellent individuals chosen from the first phase form a new population to search for the optimal solution of the current environment; and 3) the third phase combines the memory individuals of the first two phases with some randomly generated individuals to re-initialize the population for the next environment. From the experiments, one can understand the strengths and weaknesses of the three compared algorithms for solving DCOPs in depth. Moreover, we also give some suggestions for researchers to apply these three algorithms on different occasions.

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上海纽约大学William Glover