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A hybrid multi-objective evolutionary algorithm with high solving efficiency for UAV defense programming Swarm Evol. Comput. (IF 10.0) Pub Date : 2024-04-15 Zhenzu Bai, Haiyin Zhou, Jianmai Shi, Lining Xing, Jiongqi Wang
Emerging Unmanned Aerial Vehicle (UAV) application patterns, including the Loyal Wingman and Unmanned Swarms, have significantly challenged the administration and defense against illegal or trespassing UAVs. The weapon-target assignment (WTA) problem, a famous combinatorial optimization problem in military operations research, is decisive for the success of UAV defense programming. Related investigations
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Cooperative constrained multi-objective dual-population evolutionary algorithm for optimal dispatching of wind-power integrated power system Swarm Evol. Comput. (IF 10.0) Pub Date : 2024-04-13 Zhen Zhang, Huifeng Zhang, Yazhang Tian, Chongwei Li, Dong Yue
Due to increasing uncertainty and multiple operational requirements of current power system, it brings great challenge to power dispatch of wind-power integrated power system. To address above problems, a cooperative constrained multi-objective dual-population evolutionary algorithm (CCMDEA) is proposed with uncertainty budget of wind power, which divides the intermittent power into different intervals
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Global Opposition Learning and Diversity ENhancement based Differential Evolution with exponential crossover for numerical optimization Swarm Evol. Comput. (IF 10.0) Pub Date : 2024-04-12 Juncan Li, Zhenyu Meng
Since the inception of Differential Evolution (DE), the vast majority of studies on it indicate that exponential crossover does not solve the real-parameter optimization in continuous spaces very well. However, we find that once the appropriate crossover rate and its corresponding parameter control are found, the DE variants with exponential crossover can achieve comparable performance with the ones
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Multi-agent deep Q-network-based metaheuristic algorithm for Nurse Rostering Problem Swarm Evol. Comput. (IF 10.0) Pub Date : 2024-04-12 Xinzhi Zhang, Yeming Yang, Qingling Zhu, Qiuzhen Lin, Weineng Chen, Jianqiang Li, Carlos A. Coello Coello
The Nurse Rostering Problem (NRP) aims to create an efficient and fair work schedule that balances both the needs of employees and the requirements of hospital operations. Traditional local search-based metaheuristic algorithms, such as adaptive neighborhood search (ANS) and variable neighborhood descent (VND), mainly focus on optimizing the current solution without considering potential long-term
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Multi-objective optimization driven by preponderant individuals and symmetric sampling for operational parameter design in aluminum electrolysis process Swarm Evol. Comput. (IF 10.0) Pub Date : 2024-04-11 Lizhong Yao, Jia Chen, Ling Wang, Rui Li, Haijun Luo, Jun Yi
Developing advanced population-based multi-objective optimization algorithms to explore optimal operating parameters is a crucial means of achieving energy saving and consumption reduction in aluminum electrolysis process (AEP). Many techniques, such as NSGA-II/NSGA-III, rely on dominance relationships among population members to prioritize evolving individuals. However, this mechanism assumes equal
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Finding robust and influential nodes on directed networks using a memetic algorithm Swarm Evol. Comput. (IF 10.0) Pub Date : 2024-04-11 Zhaoxi Ou, Shuai Wang
The influence maximization problem is a research hotspot on social networks, which involves selecting a set of nodes as seeds to maximize the influence spread. The robust influence maximization problem is a further extension of the influence maximization problem considering the external factors pertaining to the propagation process. The current research mainly includes how to select the most influential
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A cross-benchmark examination of feature-based algorithm selector generalization in single-objective numerical optimization Swarm Evol. Comput. (IF 10.0) Pub Date : 2024-04-11 Gjorgjina Cenikj, Gašper Petelin, Tome Eftimov
The task of selecting the best optimization algorithm for a particular problem is known as algorithm selection (AS). This involves training a model using landscape characteristics to predict algorithm performance, but a key challenge remains: making AS models generalize effectively to new, untrained benchmark suites. This study assesses AS models’ generalizability in single-objective numerical optimization
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A compass-based hyper-heuristic for multi-objective optimization problems Swarm Evol. Comput. (IF 10.0) Pub Date : 2024-04-11 Cuixia Li, Sihao Li, Li Shi, Yanzhe Zhao, Shuyan Zhang, Shuozhe Wang
Multi-objective selection hyper-heuristics have attracted more attention of researchers because of their cross-domain ability. However, for multi-objective optimization problems (MOPs), obtaining a manageable number of solutions that are well distributed and converged in the objective space is still a challenge, especially when solving high-dimensional MOPs. In order to solve this problem, this paper
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Automatic feature extraction with Vectorial Genetic Programming for Alzheimer’s Disease prediction through handwriting analysis Swarm Evol. Comput. (IF 10.0) Pub Date : 2024-04-10 Irene Azzali, Nicole D. Cilia, Claudio De Stefano, Francesco Fontanella, Mario Giacobini, Leonardo Vanneschi
Alzheimer’s Disease (AD) is an incurable neurodegenerative disease that strongly impacts the lives of the people affected. Even if, to date, there is no cure for this disease, its early diagnosis helps to manage the course of the disease better with the treatments currently available. Even more importantly, an early diagnosis will also be necessary for the new treatments available in the future. Recently
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A similarity-detection-based evolutionary algorithm for large-scale multimodal multi-objective optimization Swarm Evol. Comput. (IF 10.0) Pub Date : 2024-04-09 Si Long, Jinhua Zheng, Qi Deng, Yuan Liu, Juan Zou, Shengxiang Yang
In recent years, there has been a surge in the development of evolutionary algorithms tailored for multimodal multi-objective optimization problems (MMOPs). These algorithms aim to find multiple equivalent Pareto optimal solution sets (PSs). However, little work has been done on MMOPs with large-scale decision variables, especially when the Pareto optimal solutions are sparse. These problems pose significant
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An effective multi-objective evolutionary algorithm for multiple spraying robots task assignment problem Swarm Evol. Comput. (IF 10.0) Pub Date : 2024-04-08 Jin-Shuai Dong, Quan-Ke Pan, Zhong-Hua Miao, Hong-Yan Sang, Liang Gao
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An enhanced estimation of distribution algorithm with problem-specific knowledge for distributed no-wait flowshop group scheduling problems Swarm Evol. Comput. (IF 10.0) Pub Date : 2024-04-05 Zi-Qi Zhang, Yan-Xuan Xu, Bin Qian, Rong Hu, Fang-Chun Wu, Ling Wang
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A matheuristic-based multi-objective evolutionary algorithm for flexible assembly jobs shop scheduling problem in cellular manufacture Swarm Evol. Comput. (IF 10.0) Pub Date : 2024-04-05 Yifan Hu, Liping Zhang, Qiong Wang, Zikai Zhang, Qiuhua Tang
The flexible assembly job shop scheduling problem (FAJSP) and its extensions have received increased attention. However, multi-objective FAJSP in cellular manufacture (FAJSP-CM), a real-world extension of FAJSP with important applications in the mass customization of complex products, is barely considered in most previous studies. Hence, this study first addresses the multi-objective FAJSP-CM problem
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Evolutionary optimization approach based on heuristic information with pseudo-utility for the quadratic assignment problem Swarm Evol. Comput. (IF 10.0) Pub Date : 2024-04-05 Youcong Ni, Wentao Liu, Xin Du, Ruliang Xiao, Gaolin Chen, Yong Wu
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A recursive framework for improving the performance of multi-objective differential evolution algorithms for gene selection Swarm Evol. Comput. (IF 10.0) Pub Date : 2024-04-04 Min Li, Yangfan Zhao, Rutun Cao, Junke Wang, Depeng Wu
Gene selection is a pivotal process in machine-learning-driven medical diagnostics, where the goal is to identify a subset of genes from microarray expression profiles that can enhance the predictive accuracy of classifiers for disease diagnosis. The two key objectives of gene selection are to reduce the dimensionality of the data and to improve the accuracy of disease diagnosis, which is typically
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Competitive Swarm Optimizer: A decade survey Swarm Evol. Comput. (IF 10.0) Pub Date : 2024-04-04 Dikshit Chauhan, Shivani, Ran Cheng
Since its inception in 2014, the Competitive Swarm Optimizer (CSO) has emerged as a significant advancement in the field of swarm intelligence, particularly in addressing large-scale optimization challenges. This paper offers a comprehensive review of a decade of developments in CSO research, synthesizing a vast array of research efforts to underscore the pivotal CSO variants, their foundational principles
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Growing Neural Gas Network-based surrogate-assisted Pareto set learning for multimodal multi-objective optimization Swarm Evol. Comput. (IF 10.0) Pub Date : 2024-04-04 Fei Ming, Wenyin Gong, Yaochu Jin
The key issue in handling multimodal multi-objective optimization problems (MMOPs) is to find multiple Pareto sets (PSs) corresponding to one Pareto front (PF). Therefore, learning the PSs is critical to facilitate solving MMOPs while unfortunately, current research only focuses on PF learning which is helpless in finding multiple PSs by the information of one PF. Moreover, since the PSs of an MMOP
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oBABC: A one-dimensional binary artificial bee colony algorithm for binary optimization Swarm Evol. Comput. (IF 10.0) Pub Date : 2024-04-03 Fangfang Zhu, Zhenhao Shuai, Yuer Lu, Honghong Su, Rongwen Yu, Xiang Li, Qi Zhao, Jianwei Shuai
Artificial bee colony (ABC) algorithm is a widely utilized swarm intelligence (SI) algorithm for addressing continuous optimization problems. However, most binary variants of ABC (BABC) algorithms may suffer from issues such as invalid searches and high complexity when applied to binary problems. To address these challenges, we first establish a set of criteria for developing a BABC algorithm. Following
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Multi-policy deep reinforcement learning for multi-objective multiplicity flexible job shop scheduling Swarm Evol. Comput. (IF 10.0) Pub Date : 2024-04-01 Linshan Ding, Zailin Guan, Mudassar Rauf, Lei Yue
This study considers the simultaneous minimization of makespan and total tardiness for the multi-objective multiplicity flexible job shop scheduling problem (MOMFJSP). A deep reinforcement learning framework employing a multi-policy proximal policy optimization algorithm (MPPPO) is developed to solve MOMFJSP. The MOMFJSP is treated as a Markov decision process, allowing an intelligent agent to make
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A multi-objective teaching-learning-based optimizer for a cooperative task allocation problem of weeding robots and spraying drones Swarm Evol. Comput. (IF 10.0) Pub Date : 2024-03-29 Cun-Hai Wang, Quan-Ke Pan, Xiao-Ping Li, Hong-Yan Sang, Bing Wang
In recent years, both intelligent robots and drones have been widely used in agriculture. This paper studies a cooperative task allocation problem of weeding robots and spraying drones (WRSDCTA) with two objectives of minimizing the maximum completion time and minimizing the total residual herbicide. Based on the characteristics of the problem, a mathematical model is established. An effective multi-objective
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A DQN-based memetic algorithm for energy-efficient job shop scheduling problem with integrated limited AGVs Swarm Evol. Comput. (IF 10.0) Pub Date : 2024-03-28 Youjie Yao, Xinyu Li, Liang Gao
AGVs have gained significant popularity in various industries. However, the existing literature rarely considers the integrated scheduling of production and logistics on the workshop due to the NP-hard property of both machine scheduling and AGV scheduling. The energy-efficient job shop scheduling problem with limited AGVs is investigated in this paper. A multi-objective memetic algorithm with deep
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Using different Heuristic strategies and an adaptive Neuro-Fuzzy inference system for multi-objective optimization of Hybrid Nanofluid to provide an efficient thermal behavior Swarm Evol. Comput. (IF 10.0) Pub Date : 2024-03-23 Zhe Wang, Hayder Oleiwi Shami, Khudhaier. J. Kazim, Ali Basem, Halah Jawad Al-fanhrawi, Karina Elizabeth Cajamarca Dacto, Soheil Salahshour, Mohammad Khajehkhabaz, S. Ali Eftekhari
The importance of multi-objective optimization in hybrid nanofluid research lies in its wide-ranging applications across fields such as microelectronics, aerospace, and renewable energy. These specialized fluids hold the potential to elevate the performance and efficiency of diverse systems through enhanced heat transfer capabilities. This research endeavor is centered around optimizing a hybrid nanofluid
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Mathematical model and adaptive multi-objective evolutionary algorithm for cellular manufacturing with mixed production mode Swarm Evol. Comput. (IF 10.0) Pub Date : 2024-03-22 Lixin Cheng, Qiuhua Tang, Liping Zhang
As the product mix in production changes dramatically, cell reconfiguration is requisite to smoothen the production process. Meanwhile, multiple production modes are simultaneously adopted in the site to promote productivity and assure flexibility, and thus the coordination scheduling among them becomes a challenging problem. To achieve cell reconfiguration and cell scheduling in a cellular manufacturing
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Constraint subsets-based evolutionary multitasking for constrained multiobjective optimization Swarm Evol. Comput. (IF 10.0) Pub Date : 2024-03-22 Kunjie Yu, Lingjun Wang, Jing Liang, Heshan Wang, Kangjia Qiao, Tianye Liang
Constrained multiobjective optimization problems (CMOPs) are challenging because they need to optimize multiple conflicting objectives and satisfy various constraints simultaneously. To solve CMOPs, various constrained multiobjective evolutionary algorithms have been proposed in recent years. However, most of them tackle constraints by considering all constraints or zero constraint scenarios, these
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Many-objective evolutionary algorithm based on parallel distance for handling irregular Pareto fronts Swarm Evol. Comput. (IF 10.0) Pub Date : 2024-03-21 Zichen Wei, Hui Wang, Shuai Wang, Zhixia Zhang, Zhihua Cui, Feng Wang, Hu Peng, Jia Zhao
In recent years, various many-objective evolutionary algorithms (MaOEAs) have been proved to be successful in solving many-objective optimization problems (MaOPs). However, the performance of most MaOEAs is seriously affected when handling MaOPs with irregular Pareto fronts (PFs). In this paper, a new MaOEA variant based on parallel distance (called PDMaOEA) is proposed to solve MaOPs with irregular
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Diversity-guided particle swarm optimization with multi-level learning strategy Swarm Evol. Comput. (IF 10.0) Pub Date : 2024-03-21 Dongping Tian, Qiu Xu, Xinhui Yao, Guangnan Zhang, Yafeng Li, Chenghu Xu
Particle swarm optimization (termed as PSO) is a metaheuristic algorithm inspired by the swarm intelligence. Since its advent, PSO has been successfully applied to tackle various issues that are hard to optimize. Unfortunately, similar to other evolutionary computation, PSO is also difficult to get rid of the bad luck of premature convergence and local optimization, especially when dealing with complex
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Handling dynamic capacitated vehicle routing problems based on adaptive genetic algorithm with elastic strategy Swarm Evol. Comput. (IF 10.0) Pub Date : 2024-03-21 Jianxia Li, Ruochen Liu, Ruinan Wang
With the development of e-commerce platforms, the logistics industry is also booming. The transportation process is at the core of the logistics industry, which influences the timeliness and rationality of logistics distribution. In the real world, there are many dynamic demands in logistics distribution, so logistics distribution should deal with dynamic demands quickly to realize more effective route
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An efficient collaborative multi-swap iterated greedy algorithm for the distributed permutation flowshop scheduling problem with preventive maintenance Swarm Evol. Comput. (IF 10.0) Pub Date : 2024-03-18 Qiu-Yang Han, Hong-Yan Sang, Quan-Ke Pan, Biao Zhang, Heng-Wei Guo
Under the context of globalization, distributed multi-factory production model is becoming the mainstream of the manufacturing industry because it provides enterprises with flexible and efficient production solutions. In real-world environments, the wear and tear of machines is unavoidable, so it is necessary to maintain equipment for sustainable processing and production. This paper explores the distributed
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Symbiotic Operation Forest (SOF): A novel approach to supervised machine learning Swarm Evol. Comput. (IF 10.0) Pub Date : 2024-03-18 Min-Yuan Cheng, Akhmad F.K. Khitam
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Characterization of rankings generated by pseudo-Boolean functions Swarm Evol. Comput. (IF 10.0) Pub Date : 2024-03-16 Imanol Unanue, María Merino, Jose A. Lozano
In this paper we pursue the study of pseudo-Boolean functions as ranking generators. The objective of the work is to find new insights between the relation of the degree of a pseudo-Boolean function and the rankings that can be generated by these insights. Based on a characterization theorem for pseudo-Boolean functions of degree , several observations are made. First, we verify that pseudo-Boolean
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Generating random complex networks with network motifs using evolutionary algorithm-based null model Swarm Evol. Comput. (IF 10.0) Pub Date : 2024-03-15 Bogdan-Eduard-Mădălin Mursa, Anca Andreica
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A dual population collaborative genetic algorithm for solving flexible job shop scheduling problem with AGV Swarm Evol. Comput. (IF 10.0) Pub Date : 2024-03-11 Xiaoqing Han, Weiyao Cheng, Leilei Meng, Biao Zhang, Kaizhou Gao, Chaoyong Zhang, Peng Duan
With the increase in labor costs and the development of manufacturing automation technology, automatic guided vehicle (AGV) is widely used in various flexible workshop scenarios. The integrated scheduling of processing machines and AGV is of great significance in real-world workshop production. This article studies the integration problem of flexible job shop scheduling problem (FJSP) and AGV with
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Evolutionary modeling approach based on multiobjective genetic programming for strip quality prediction Swarm Evol. Comput. (IF 10.0) Pub Date : 2024-03-04 Yao Wang, Xianpeng Wang, Lixin Tang
In the iron and steel industry, hardness is one of the key indicators of strip quality in the continuous annealing production line (CAPL). However, the complex production process and the strong coupled nonlinearity between process parameters make it difficult to develop accurate mechanism models and pose a challenge for data-driven modeling approaches. More importantly, most of the data-driven learning
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Bi-objective scheduling of physical therapy treatments with coupled operations for inpatients in rehabilitation departments Swarm Evol. Comput. (IF 10.0) Pub Date : 2024-03-02 Xin Li, Haibin Chen
In recent years, the field of physical therapy has experienced significant growth, particularly due to the impact of the Covid-19 pandemic. However, the capacity of rehabilitation services remains inadequate. This current study deals with scheduling physical therapies with coupled operations and multiple resources in rehabilitation departments, considering bi-objective optimization by minimizing both
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A sequential excitation and simplified ant colony optimization based global extreme seeking control method for performance improvement Swarm Evol. Comput. (IF 10.0) Pub Date : 2024-03-02 Guangyu Liu, Yuwei Bai, Ling Zhu, Qingyun Wang, Wei Zhang
To date, it is lacking of an effective global extreme seeking control (GESC) approach to improve transient responses. Motivated by this engineering problem, a novel and general method of global ESC is proposed theoretically and technically in three steps: the step of a simplified ant colony optimization (SACO) algorithm for the task of global extreme seeking, the step of a sequential excitation (SE)
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An efficient evolutionary architecture search for variational autoencoder with alternating optimization and adaptive crossover Swarm Evol. Comput. (IF 10.0) Pub Date : 2024-03-02 Ronghua Shang, Hangcheng Liu, Wenzheng Li, Weitong Zhang, Teng Ma, Licheng Jiao
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A systematic review on the potency of swarm intelligent nanorobots in the medical field Swarm Evol. Comput. (IF 10.0) Pub Date : 2024-02-29 Mahvish Khurshid Bijli, Prabal Verma, Amrit Pal Singh
The field of robotics is emerging quickly, and the miniaturization of robots to the nanoscale has opened up new possibilities for healthcare. Swarm nanorobotics, as a research area, has attracted interest in recent years. It is viewed as a promising option for various medical applications due to its high drug delivery efficiency and low invasiveness. This survey article focuses on the challenges associated
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Reinforcement learning-assisted evolutionary algorithm: A survey and research opportunities Swarm Evol. Comput. (IF 10.0) Pub Date : 2024-02-27 Yanjie Song, Yutong Wu, Yangyang Guo, Ran Yan, Ponnuthurai Nagaratnam Suganthan, Yue Zhang, Witold Pedrycz, Swagatam Das, Rammohan Mallipeddi, Oladayo Solomon Ajani, Qiang Feng
Evolutionary algorithms (EA), a class of stochastic search methods based on the principles of natural evolution, have received widespread acclaim for their exceptional performance in various real-world optimization problems. While researchers worldwide have proposed a wide variety of EAs, certain limitations remain, such as slow convergence speed and poor generalization capabilities. Consequently,
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Multiple strategies based Grey Wolf Optimizer for feature selection in performance evaluation of open-ended funds Swarm Evol. Comput. (IF 10.0) Pub Date : 2024-02-24 Dan Chang, Congjun Rao, Xinping Xiao, Fuyan Hu, Mark Goh
The methods for selecting the features in evaluating fund performance rely heavily on traditional statistics, which can potentially lead to excessive data dimensions in a multi-dimensional context. Grey Wolf Optimizer (GWO), a swarm intelligence optimization algorithm with its simple structure and few parameters, is widely used in feature selection. However, the algorithm suffers from local optimality
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Evolutionary multiobjective optimization assisted by scalarization function approximation for high-dimensional expensive problems Swarm Evol. Comput. (IF 10.0) Pub Date : 2024-02-22 Yuma Horaguchi, Kei Nishihara, Masaya Nakata
Surrogate-assisted evolutionary algorithms (SAEAs) are a promising approach for solving expensive multiobjective optimization problems, but they often cannot address high-dimensional problems. Although one common approach to overcoming this challenge is to construct reliable surrogates, their accuracy inevitably deteriorates in a high-dimensional search space. Thus, this paper presents a novel SAEA
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Artificial intelligence algorithms in unmanned surface vessel task assignment and path planning: A survey Swarm Evol. Comput. (IF 10.0) Pub Date : 2024-02-21 Kaizhou Gao, Minglong Gao, Mengchu Zhou, Zhenfang Ma
Due to the complex environment and variable demands, unmanned surface vessel (USV) task assignment and path planning have received much attention from academia and industry in recent years. Artificial intelligence technologies are increasingly adopted for solving the USV task assignment and path planning problems. This paper aims to give a comprehensive literature review of achievements, trends and
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A multi-population multi-tasking variable neighborhood search algorithm with diversity enhancements for inter-domain path computation problem Swarm Evol. Comput. (IF 10.0) Pub Date : 2024-02-20 Do Tuan Anh, Huynh Thi Thanh Binh, Ban Ha Bang, Nguyen Duc Thai, Phung Bao Ha
In recent years, as networks have become increasingly complex and expansive, network navigation faces new challenges in routing and resource utilization, particularly in the case of multi-domain networks. The Inter-Domain Path Computation under Node-defined Domain Uniqueness Constraint (IDPC-NDU) problem is one of many such scenarios. The problem requires finding the shortest path in a multi-domain
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Dominance relation selection and angle-based distribution evaluation for many-objective evolutionary algorithm Swarm Evol. Comput. (IF 10.0) Pub Date : 2024-02-20 Shengqing Zhou, Yiru Dai, Zihao Chen
In many-objective optimization, most existing evolutionary algorithms struggle to effectively balance the convergence and diversity of population. Given the failure of Pareto-dominance-based selection mechanism in high-dimensional spaces and the growing recognition of the advantages of angle-based similarity evaluation, we propose a Many-objective Evolutionary Algorithm using Dominance Relation Selection
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Decomposition with adaptive composite norm for evolutionary multi-objective combinatorial optimization Swarm Evol. Comput. (IF 10.0) Pub Date : 2024-02-20 Ruihao Zheng, Yin Wu, Genghui Li, Yu Zhang, Zhenkun Wang
The multi-objective evolutionary algorithm based on decomposition (MOEA/D) decomposes a multi-objective problem into a series of single-objective subproblems for collaborative optimization. The weighted sum (WS) method and the Tchebycheff (TCH) method are the two most popular scalarization methods. They have pros and cons in solving multi-objective combinatorial optimization problems (MOCOPs) the WS
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Evolutionary multimodal multiobjective optimization guided by growing neural gas Swarm Evol. Comput. (IF 10.0) Pub Date : 2024-02-20 Yiping Liu, Ling Zhang, Xiangxiang Zeng, Yuyan Han
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Regularity model based offspring generation in surrogate-assisted evolutionary algorithms for expensive multi-objective optimization Swarm Evol. Comput. (IF 10.0) Pub Date : 2024-02-19 Bingdong Li, Yongfan Lu, Hong Qian, Wenjing Hong, Peng Yang, Aimin Zhou
Evolutionary algorithms face significant challenges when it comes to solving expensive multi-objective optimization problems, which require costly evaluations. One of the most popular approaches to addressing this issue is to use surrogate models, which can replace the expensive real function evaluations with cheaper approximations. However, in many surrogate-assisted evolutionary algorithms (SAEAs)
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Benchmark problems for large-scale constrained multi-objective optimization with baseline results Swarm Evol. Comput. (IF 10.0) Pub Date : 2024-02-19 Kangjia Qiao, Jing Liang, Kunjie Yu, Weifeng Guo, Caitong Yue, Boyang Qu, P.N. Suganthan
The interests in evolutionary constrained multiobjective optimization are rapidly increasing during the past two decades. However, most related studies are limited to small-scale problems, despite the fact that many practical problems contain large-scale decision variables. Although several large-scale constrained multi-objective evolutionary algorithms (CMOEAs) have been developed, they are still
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Parallel multi-objective optimization for expensive and inexpensive objectives and constraints Swarm Evol. Comput. (IF 10.0) Pub Date : 2024-02-16 Roy de Winter, Bas Milatz, Julian Blank, Niki van Stein, Thomas Bäck, Kalyanmoy Deb
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A customized adaptive large neighborhood search algorithm for solving a multi-objective home health care problem in a pandemic environment Swarm Evol. Comput. (IF 10.0) Pub Date : 2024-02-09 Wenheng Liu, Mahjoub Dridib, Amir M. Fathollahi-Fard, Amir Hajjam El Hassani
This paper addresses a home health care routing and scheduling problem (HHCRSP) specifically focusing on the context of a pandemic environment. The investigated HHCRSP involves assigning appropriate caregivers to patients and optimizing caregiver routes while minimizing total travel costs, workload differences among caregivers, and negative patient preferences. The problem accounts for synchronized
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A modified competitive swarm optimizer guided by space sampling for large-scale multi-objective optimization Swarm Evol. Comput. (IF 10.0) Pub Date : 2024-02-09 Xiaoxin Gao, Fazhi He, Feng Wang, Xiaoting Wang
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Collaborative Q-learning hyper-heuristic evolutionary algorithm for the production and transportation integrated scheduling of silicon electrodes Swarm Evol. Comput. (IF 10.0) Pub Date : 2024-02-08 Rong Hu, Yu-Fang Huang, Xing Wu, Bin Qian, Ling Wang, Zi-Qi Zhang
Silicon electrodes are widely used in semiconductor etching machines. The periodic consumption of silicon electrodes has become an important consumable in wafer manufacturing. Due to the limited number of silicon electrode manufacturers and the increasing demand for silicon electrodes in global market, the newly processed silicon electrodes need to be immediately delivered to wafer manufacturers to
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An adaptive binary quantum-behaved particle swarm optimization algorithm for the multidimensional knapsack problem Swarm Evol. Comput. (IF 10.0) Pub Date : 2024-02-07 Xiaotong Li, Wei Fang, Shuwei Zhu, Xin Zhang
The multidimensional knapsack problem (MKP) is a classical combinatorial optimization problem with wide real-life applications. Binary quantum-behaved particle swarm optimization (BQPSO) algorithm is a popular heuristic algorithm used in binary optimization. While BQPSO exhibits strong global search capabilities, it is still prone to local optima due to particle aggregation. To address this issue,
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A noise-resistant infill sampling criterion in surrogate-assisted multi-objective evolutionary algorithms Swarm Evol. Comput. (IF 10.0) Pub Date : 2024-02-07 Nan Zheng, Handing Wang
Most existing surrogate-assisted multi-objective evolutionary algorithms are susceptible to the noise, since the noise affects both the approximation performance of objective surrogate models and the selection accuracy. Therefore, the keys to these algorithms are how to reduce the noise impact without additional function evaluations burden and how to maximize the noise immunity of the algorithms. In
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An objective reduction algorithm based on population decomposition and hyperplane approximation Swarm Evol. Comput. (IF 10.0) Pub Date : 2024-02-07 Ning Yang, Hai-Lin Liu, Junrong Xiao
Objective reduction is an efficient method to simplify many-objective optimization problems (MaOPs) with redundant objectives. However, most objective reduction algorithms operate on an entire sample set, which would easily omit local features and lead to an over-reduction of objectives. To alleviate the above problems, this paper proposes an objective reduction algorithm based on population decomposition
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Constrained multi-objective optimization with dual-swarm assisted competitive swarm optimizer Swarm Evol. Comput. (IF 10.0) Pub Date : 2024-02-07 Yubo Wang, Chengyu Hu, Wenyin Gong, Fei Ming
Many metaheuristic methods have been proposed and have shown promising performance in solving multi-objective optimization problems (MOPs). As a representative example, the competitive swarm optimizer (CSO) exhibits excellent performance in solving large-scale MOPs. However, CSO performs poorly when used straightforwardly for constrained MOPs with complex constraints or objective spaces. In this paper
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An experimental approach to designing grouping genetic algorithms Swarm Evol. Comput. (IF 10.0) Pub Date : 2024-02-06 Octavio Ramos-Figueroa, Marcela Quiroz-Castellanos
Grouping problems are a special case of combinatorial problems that emerge in several practical and theoretical situations, where the goal is to find the optimal groups that minimize an objective function. One of the most outstanding metaheuristics to solve them is the Grouping Genetic Algorithm (GGA). Nevertheless, many grouping problems have very complex search spaces with unique and hard features
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A multi-swarm optimizer with a reinforcement learning mechanism for large-scale optimization Swarm Evol. Comput. (IF 10.0) Pub Date : 2024-02-03 Xujie Wang, Feng Wang, Qi He, Yinan Guo
Large-scale global optimization (LSGO) problems involve numerous decision variables, are similar to real-world problems, and have generated research interest. To solve LSGO, a particle swarm optimizer (PSO) has been used. However, the many local optima and huge search space severely limit the effectiveness of the classic PSO. Dealing with the complexity of LSGO while avoiding the local optima is the
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Reliable network-level pavement maintenance budget allocation: Algorithm selection and parameter tuning matter Swarm Evol. Comput. (IF 10.0) Pub Date : 2024-02-03 Amirreza Mahpour, Tamer El-Diraby
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Swarm flocking using optimisation for a self-organised collective motion Swarm Evol. Comput. (IF 10.0) Pub Date : 2024-02-02 Mazen Bahaidarah, Fatemeh Rekabi-Bana, Ognjen Marjanovic, Farshad Arvin
Collective motion, often called flocking, is a prevalent behaviour observed in nature wherein large groups of organisms move cohesively, guided by simple local interactions, as exemplified by bird flocks and fish schools. Inspired by those intelligent species, many cyber–physical systems attempted to increase autonomy by resembling the models that describe those collective behaviours. The main motivation
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Adaptive constraint handling technique selection for constrained multi-objective optimization Swarm Evol. Comput. (IF 10.0) Pub Date : 2024-02-02 Chao Wang, Zhihao Liu, Jianfeng Qiu, Lei Zhang
Constrained multi-objective optimization problems involve the optimization of multiple conflicting objectives simultaneously subject to a number of constraints, which pose a great challenge for the existing algorithms. When utilizing evolutionary algorithms to solve them, the constraint handling technique (CHT) plays a pivotal role in the environmental selection. Several CHTs, such as penalty functions