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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
Variational autoencoder is a commonly unsupervised learning model. However, its complex structure hinders the utilization of the network architecture search algorithm to release researchers from tedious manual design. To design excellent architectures automatically, this paper proposes an efficient evolutionary architecture search for variational autoencoder with alternating optimization and adaptive
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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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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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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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Dominance relation selection and angle-based distribution evaluation for many-objective evolutionary algorithm Swarm Evol. Comput. (IF 10.0) Pub Date : 2024-02-19 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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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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Evolutionary multimodal multiobjective optimization guided by growing neural gas Swarm Evol. Comput. (IF 10.0) Pub Date : 2024-02-19 Yiping Liu, Ling Zhang, Xiangxiang Zeng, Yuyan Han
Evolutionary multimodal multiobjective optimization aims to search for a set of Pareto optimal solutions that are well distributed in both the objective and decision spaces. In recent years, there has been a growing interest in enhancing the search ability of evolutionary algorithms using machine learning techniques. However, there are few studies on machine learning-assisted evolutionary multimodal
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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
Expensive objectives and constraints are key characteristics of real-world multi-objective optimization problems. In practice, they often occur jointly with inexpensive objectives and constraints. This paper presents the (IOC-SAMO-COBRA) for such problems. This is motivated by the recently proposed Inexpensive Constraint Surrogate-Assisted Non-dominated Sorting Genetic Algorithm II (IC-SA-NSGA-II)
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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
Multi-objective evolutionary algorithms have demonstrated promising performance in solving multi/many-objective problems. However, their performance decreases sharply when dealing with multi-objective optimization problems with hundreds or thousands of decision variables, which prevent them from quickly converging to the Pareto front. To this end, this article proposes a modified competitive swarm
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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
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An enhanced Kalman filtering and historical learning mechanism driven estimation of distribution algorithm Swarm Evol. Comput. (IF 10.0) Pub Date : 2024-02-02 Ningning Zhu, Fuqing Zhao, Ling Wang, Chenxin Dong
As a representative evolutionary algorithm based on probabilistic models, the estimation of distribution algorithm (EDA) is widely applied in complex continuous optimization problems based on remarkable characteristics of modeling with macro-dominant information. However, the success of EDA depends on the quality of dominant solutions, modeling, sampling methods, and the efficiency of searching. An
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A Q-learning memetic algorithm for energy-efficient heterogeneous distributed assembly permutation flowshop scheduling considering priorities Swarm Evol. Comput. (IF 10.0) Pub Date : 2024-02-02 Cong Luo, Wenyin Gong, Fei Ming, Chao Lu
Most studies on distributed assembly permutation flowshop scheduling do not consider product priorities and factory heterogeneity. This causes delays in critical products and cannot reflect the real-world production situation. This paper focuses on the energy-efficient heterogeneous distributed assembly permutation flowshop scheduling considering priorities (EHDAPFS-P) to minimize total tardiness and
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A cascaded flowshop joint scheduling problem with makespan minimization: A mathematical model and shifting iterated greedy algorithm Swarm Evol. Comput. (IF 10.0) Pub Date : 2024-01-24 Chuang Wang, Quan-Ke Pan, Hong-Yan Sang, Xue-Lei Jing
This paper studies a cascaded flowshop joint scheduling problem that has critical applications in the electronic information equipment manufacturing industry but has received limited attention in the scheduling field. The cascaded flowshop joint scheduling problem encompasses both a distributed permutation flowshop scheduling problem and a hybrid flowshop scheduling problem. This paper investigates
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Optimizing parameters in swarm intelligence using reinforcement learning: An application of Proximal Policy Optimization to the iSOMA algorithm Swarm Evol. Comput. (IF 10.0) Pub Date : 2024-01-24 Lukáš Klein, Ivan Zelinka, David Seidl
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Population based metaheuristics in Spark: Towards a general framework using PSO as a case study Swarm Evol. Comput. (IF 10.0) Pub Date : 2024-01-23 Xoán C. Pardo, Patricia González, Julio R. Banga, Ramón Doallo
Over the years metaheuristics have been successfully applied to optimization problems in many real-world applications. The increasing complexity and scale of the problems addressed has posed new challenges to researchers in the field. The application of distributed metaheuristics is a common approach to speed up the time to solution or improve its quality by leveraging traditional parallel programming
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A genetic algorithm with critical path-based variable neighborhood search for distributed assembly job shop scheduling problem Swarm Evol. Comput. (IF 10.0) Pub Date : 2024-01-23 Shichen Tian, Chunjiang Zhang, Jiaxin Fan, Xinyu Li, Liang Gao
Production scheduling in distributed manufacturing systems has become an active research field, where large-sized complicated products, such as airplanes and ships, are taken as the primary focus. This paper investigates a distributed assembly job shop scheduling problem (DAJSP) which consists of two production phases. The first stage processes components in several job shops, and the second stage
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Two-stage evolutionary algorithm with fuzzy preference indicator for multimodal multi-objective optimization Swarm Evol. Comput. (IF 10.0) Pub Date : 2024-01-20 Yinghong Xie, Junhua Li, Yufei Li, Wenhao Zhu, Chaoqing Dai
Multimodal multi-objective optimization problems (MMOPs) are multi-objective optimization problems (MOPs) involving multiple equivalent global or local Pareto optimal solution sets (PSs). For decision-makers, not only the global optimal solution sets need to be found, but also the value of local optimal solution sets cannot be ignored. However, most multimodal multi-objective evolutionary algorithms
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Improved particle swarm optimization with reverse learning and neighbor adjustment for space surveillance network task scheduling Swarm Evol. Comput. (IF 10.0) Pub Date : 2024-01-20 Xi Long, Weiwei Cai, Leping Yang, Huan Huang
Space Surveillance Network (SSN) plays a crucial role in Space Domain Awareness (SDA) as it helps to maintain the catalog of Resident Space Objects (RSOs). Howe`ver, as the population of RSOs continues to grow, the contradiction between the increasing number of RSOs and limited sensor resources will be further apparent. How to effectively utilize existing sensor resources becomes a vital concern. This
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Differential evolution with evolutionary scale adaptation Swarm Evol. Comput. (IF 10.0) Pub Date : 2024-01-20 Sheng Xin Zhang, Xin Rou Hu, Shao Yong Zheng
The performance of differential evolution (DE) algorithm heavily depends on the evolutionary scale, which is controlled by the generation operations including mutation, crossover and the control parameters including mutation factor and crossover rate. Adjusting the evolutionary scale to suit different types of problems is a critical yet challenging open question in DE research. To efficiently address
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Multi-objective migrating birds optimization for solving stochastic home health care routing and scheduling problems considering caregiver working time constraints Swarm Evol. Comput. (IF 10.0) Pub Date : 2024-01-17 Yaping Fu, Xiaomeng Ma, Kaizhou Gao, Hongfeng Wang, Ali Sadollah, L.Y. Chen
Currently, many countries are suffering from a heavy burden caused by population aging since the elderly occupy a large number of medical resources. Home health care (HHC) is a prospective approach to relieve and cope with this issue. This study addresses a multi-objective HHC routing and scheduling problem (HHCRSP) considering uncertainties and caregivers’ working time balance. First, a stochastic
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Hierarchical multi-objective optimization of proton exchange membrane fuel cell with parameter uncertainty Swarm Evol. Comput. (IF 10.0) Pub Date : 2024-01-15 Yunlong Wang, Cunliang Ye, Yongfu Wang
It is important to improve the performance indexes including the net power and system efficiency for polymer electrolyte membrane fuel cell (PEMFC), especially considering the parameter uncertainty. To this end, this paper proposes a hierarchical multi-objective optimization framework including the off-line and on-line optimization. Firstly, a steadystate non-linear PEMFC system model is established
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Large-scale evolutionary optimization: A review and comparative study Swarm Evol. Comput. (IF 10.0) Pub Date : 2024-01-10 Jing Liu, Ruhul Sarker, Saber Elsayed, Daryl Essam, Nurhadi Siswanto
Large-scale global optimization (LSGO) problems have widely appeared in various real-world applications. However, their inherent complexity, coupled with the curse of dimensionality, makes them challenging to solve. Continuous efforts have been devoted to designing computational intelligence-based approaches to solve them. This paper offers a comprehensive review of the latest developments in the field
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MRLM: A meta-reinforcement learning-based metaheuristic for hybrid flow-shop scheduling problem with learning and forgetting effects Swarm Evol. Comput. (IF 10.0) Pub Date : 2024-01-10 Zeyu Zhang, Zhongshi Shao, Weishi Shao, Jianrui Chen, Dechang Pi
In the real-world production environment, the employee skills affect production efficiency. Especially, the learning and forgetting effects largely influence the processing time. This paper investigates a hybrid flow-shop scheduling problem with learning and forgetting effects (HFSP-LF). Two learning and forgetting effects models are constructed. The sequence-dependent setup time (SDST) is also considered
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MOEA/D with customized replacement neighborhood and dynamic resource allocation for solving 3L-SDHVRP Swarm Evol. Comput. (IF 10.0) Pub Date : 2024-01-09 Han Li, Genghui Li, Qiaoyong Jiang, Jiashu Wang, Zhenkun Wang
Split delivery heterogeneous vehicle routing problem with three-dimensional loading (3L-SDHVRP) is a critical issue in manufacturing logistics. Current mainstream research formulates this problem as a single objective optimization problem, which fails to reveal the relationship among multiple conflicting objectives and cannot provide various trade-off solutions for decision-makers in real-world logistics
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Knowledge-transfer based genetic programming algorithm for multi-objective dynamic agile earth observation satellite scheduling problem Swarm Evol. Comput. (IF 10.0) Pub Date : 2024-01-09 Luona Wei, Ming Chen, Lining Xing, Qian Wan, Yanjie Song, Yuning Chen, Yingwu Chen
The multi-objective dynamic agile earth observation satellite scheduling problem (MO-DAEOSSP) aims to schedule a set of real-time arrival requests and form a reasonable observation plan to satisfy various criteria. According to the requirements in practical applications, the total profit and the average image quality of scheduled requests are taken as optimization goals in this study. Compared to manually
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Automated design of action advising trigger conditions for multiagent reinforcement learning: A genetic programming-based approach Swarm Evol. Comput. (IF 10.0) Pub Date : 2024-01-08 Tonghao Wang, Xingguang Peng, Tao Wang, Tong Liu, Demin Xu
Action advising is a popular and effective approach to accelerating independent multiagent reinforcement learning (MARL), especially for the learning systems that all the agents learn from scratch and the roles of them (advisors or advisees) cannot be predefined. The key component of action advising is the trigger condition, which answers the question of . Previous works mainly focus on the design
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Double-space environmental change detection and response strategy for dynamic multi-objective optimize problem Swarm Evol. Comput. (IF 10.0) Pub Date : 2024-01-05 Xuemin Ma, Jingming Yang, Hao Sun, Ziyu Hu, Lixin Wei
Dynamic multi-objective optimization problems (DMOPs) which contain various Pareto-optimal front (PF) and Pareto-optimal set (PS) have gained much attention. Accurate environmental change detection reveals the change degree of DMOPs and contributes the algorithm to quickly respond to the environment changes. In order to fully detect environmental changes and efficiently track front, a double-space
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Intelligent Pest Trap Monitoring under Uncertainty in Food Industry Swarm Evol. Comput. (IF 10.0) Pub Date : 2023-12-31 Suling Duan, Yong Li, Bin Zhu, Brian Adam, Zhenan He
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A cooperative evolutionary algorithm with simulated annealing for integrated scheduling of distributed flexible job shops and distribution Swarm Evol. Comput. (IF 10.0) Pub Date : 2024-01-01 Zhengpei Zhang, Yaping Fu, Kaizhou Gao, Hui Zhang, Lei Wang
Production and distribution are two essential parts in supply chains. An integration of production and distribution has received amount of attention from both academia and industry. This article investigates an integrated scheduling problem of distributed flexible job shops and distribution. First, a mixed integer programming model is developed to minimize maximum completion time. Second, a cooperative
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Multi-guiding spark fireworks algorithm: Solving multimodal functions by multiple guiding sparks in fireworks algorithm Swarm Evol. Comput. (IF 10.0) Pub Date : 2023-12-29 Xiangrui Meng, Ying Tan
Many real-world problems can be abstracted as multimodal global optimization, which is one of the main challenges for optimization algorithms due to its complexity. The fireworks algorithm (FWA) is a swarm intelligence optimization algorithm that has been widely studied and applied by virtue of the synergistic property among fireworks. Current FWA variants have poor exploitation capability to handle
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Multi-operator opposition-based learning with the neighborhood structure for numerical optimization problems and its applications Swarm Evol. Comput. (IF 10.0) Pub Date : 2023-12-29 Jiahang Li, Liang Gao, Xinyu Li
Opposition-based learning (OBL) is an effective strategy that adjusts the population to accelerate the convergence of the algorithm. However, OBL involves two phases (generation and calculation) that are seldom mentioned simultaneously. Besides, individual information in OBL is not fully used to guide evolution. First, a novel neighborhood generation strategy is proposed to fully use individual information
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A two-stage cross-neighborhood search algorithm bridging different solution representation spaces for solving the hybrid flow shop scheduling problem Swarm Evol. Comput. (IF 10.0) Pub Date : 2023-12-27 Yuan Kuang, Xiuli Wu, Ziqi Chen, Wence Li
In this paper, we address the classical hybrid flow shop scheduling problem(HFSP) to minimize makespan. This problem is known to be NP-hard and widely exists in many industrial systems such as electronics, paper, textile, and manufacturing industries. The existing work has shown that searching in multiple solution representation spaces is beneficial to improving the performance of the algorithm. However
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Memory backtracking strategy: An evolutionary updating mechanism for meta-heuristic algorithms Swarm Evol. Comput. (IF 10.0) Pub Date : 2023-12-27 Heming Jia, Chenghao Lu, Zhikai Xing
The search domain of meta-heuristic algorithms is always constantly changing, which make it difficult to adapt the diverse optimization issues. To overcome above issue, an evolutionary updating mechanism called Memory Backtracking Strategy (MBS) is proposed, which contains thinking stage, recall stage, and memory stage. Overall, the adoption of the MBS enhances the efficiency of MHSs by incorporating
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An adaptive parental guidance strategy and its derived indicator-based evolutionary algorithm for multi- and many-objective optimization Swarm Evol. Comput. (IF 10.0) Pub Date : 2023-12-20 Jiawei Yuan, Hai-Lin Liu, Shuiping Yang
The indicator-based multi-objective evolutionary algorithms have demonstrated their superiority in handling diverse types of multi- and many-objective optimization problems. However, these evolutionary algorithms still face significant challenges in balancing convergence and diversity of the evolutionary population, despite numerous auxiliary mechanisms designed to improve their performance. To address
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A survey of meta-heuristic algorithms in optimization of space scale expansion Swarm Evol. Comput. (IF 10.0) Pub Date : 2023-12-21 Jinlu Zhang, Lixin Wei, Zeyin Guo, Hao Sun, Ziyu Hu
Optimization problem of space scale expansion widely exists in practical applications, such as transportation, logistics, scheduling, social networks, etc. According to different expansion directions, the problem of space scale expansion can be divided into three categories: expansion of decision space, expansion of objective space and simultaneous expansion of decision space and objective space. These
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A many-objective evolutionary algorithm assisted by ideal hyperplane Swarm Evol. Comput. (IF 10.0) Pub Date : 2023-12-20 Zhixia Zhang, Xiangyu Shi, Zhigang Zhang, Zhihua Cui, Wensheng Zhang, Jinjun Chen
In many-objective optimization problems (MaOPs), balancing convergence and diversity while rapidly converging to the Pareto front is an arduous task for evolutionary algorithms. In addition, with the increase of the number of targets, the number of non-dominant solutions increases exponentially, and the individual selection pressure is insufficient. For this problem, we propose a many-objective evolution
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Triple competitive differential evolution for global numerical optimization Swarm Evol. Comput. (IF 10.0) Pub Date : 2023-12-20 Qiang Yang, Zhuo-Yin Qiao, Peilan Xu, Xin Lin, Xu-Dong Gao, Zi-Jia Wang, Zhen-Yu Lu, Sang-Woon Jeon, Jun Zhang
As optimization problems become more and more complex in real-world scenarios, the effectiveness of many existing differential evolution (DE) methods is critically challenged. To circumvent this predicament, this paper proposes a triple competitive DE (TCDE) to tackle increasingly complicated optimization problems. Specifically, a triple competition mechanism is devised to first randomly arrange individuals
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Attraction–Repulsion Optimization Algorithm for Global Optimization Problems Swarm Evol. Comput. (IF 10.0) Pub Date : 2023-12-20 Karol Cymerys, Mariusz Oszust
In this study, a novel meta-heuristic search (MHS) algorithm for constrained global optimization problems is proposed. Since many algorithms aim to achieve well-balanced exploitation–exploration stages with often unsatisfactory results, in the approach introduced in this paper, Attraction–Repulsion Optimization Algorithm (AROA), the balance associated with attraction–repulsion phenomena that occur
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Differential Evolution with perturbation mechanism and covariance matrix based stagnation indicator for numerical optimization Swarm Evol. Comput. (IF 10.0) Pub Date : 2023-12-16 Zhenghao Song, Chongle Ren, Zhenyu Meng
Differential Evolution (DE), as a promising population-based stochastic optimization algorithm, has drawn attention from researchers of various fields owing to its simple operation, strong robustness and few control parameters. However, classic DE suffers from drawbacks such as premature convergence and stagnation resulted from reduced population diversity when tackling complicated optimization problems
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TinyTLA: Topological landscape analysis for optimization problem classification in a limited sample setting Swarm Evol. Comput. (IF 10.0) Pub Date : 2023-12-08 Gašper Petelin, Gjorgjina Cenikj, Tome Eftimov
In numerical optimization, the characterization of optimization problems and their properties has been a long-standing issue. Overcoming it is a crucial prerequisite for many optimization-related tasks such as building quality benchmarks, algorithm selection, and algorithm configuration. Several approaches to extracting features from single-objective optimization problems have been proposed but they