Solving dynamic multimodal optimization problems via a niching-based brain storm optimization with two archives algorithm

Jin, Honglin1; Wang, Xueping1; Cheng, Shi1; Sun, Yifei2; Zhang, Mingming3; Lu, Hui4; Wu, Husheng5; Shi, Yuhui6

2024-08-01

10.1016/j.swevo.2024.101649

SWARM AND EVOLUTIONARY COMPUTATION   影响因子和分区

2210-6502

2210-6510

Dynamic and multimodal properties are simultaneously possessed in the dynamic multimodal optimization problems (DMMOPs), which aim to find multiple optimal solutions in a dynamic environment. However, more work still needs to be devoted to solving DMMOPs, which still require significant attention. A nichingbased brain storm optimization with two archives (NBSO2A) algorithm is proposed to solve DMMOPs. The two niching methods, i.e. , neighborhood-based speciation (NS), and nearest-better clustering (NBC), are incorporated into a BSO algorithm to generate new solutions. The two archives preserve the optimal solutions that meet the requirements and practical, inferior solutions discarded during the generation. Improved taboo area (ITA) removes highly similar individuals from the population. An evolution strategy with covariance matrix adaptation (CMA-ES) is adopted to enhance the local search ability and improve the quality of the solutions. The NBSO2A algorithm and four other algorithms were tested on 12 benchmark problems to validate the performance of the NBSO2A algorithm on DMMOPs. The experimental results show that the NBSO2A algorithm outperforms the other compared algorithms on most tested benchmark problems.

Brain storm optimization Dynamic optimization Multimodal optimization Dynamic multimodal optimization Embodied swarm intelligence Swarm optimization algorithm

SCI ; EI

英语

其他

National Natural Science Foundation of China[61806119] ; Natural Science Basic Research Plan In Shaanxi Province of China[2024JC-YBMS-516] ; Fundamental Research Funds for the Central Universities, China[GK202201014] ; Foundation of State Key Laboratory of Public Big Data, China[PBD2022-08]

Computer Science

Computer Science, Artificial Intelligence ; Computer Science, Theory & Methods

WOS:001267054600001

ELSEVIER

20242816670626

Benchmarking ; Clustering algorithms ; Covariance matrix ; Evolutionary algorithms ; Optimal systems ; Storms ; Swarm intelligence

Precipitation:443.3 ; Artificial Intelligence:723.4 ; Information Sources and Analysis:903.1 ; Mathematics:921 ; Optimization Techniques:921.5 ; Systems Science:961

Web of Science

1.Shaanxi Normal Univ, Sch Comp Sci, Xian 710119, Peoples R China
2.Shaanxi Normal Univ, Sch Phys & Informat Technol, Xian 710119, Peoples R China
3.Univ Auckland, Dept Mech & Mechatron Engn, Auckland 1010, New Zealand
4.Beihang Univ, Sch Elect & Informat Engn, Beijing 100191, Peoples R China
5.Engn Univ PAP, Coll Equipment Support & Management, Xian 710086, Peoples R China
6.Southern Univ Sci & Technol SUSTech, Dept Comp Sci & Engn, Shenzhen 518055, Peoples R China

Jin, Honglin,Wang, Xueping,Cheng, Shi,et al. Solving dynamic multimodal optimization problems via a niching-based brain storm optimization with two archives algorithm[J]. SWARM AND EVOLUTIONARY COMPUTATION,2024,89.

Jin, Honglin.,Wang, Xueping.,Cheng, Shi.,Sun, Yifei.,Zhang, Mingming.,...&Shi, Yuhui.(2024).Solving dynamic multimodal optimization problems via a niching-based brain storm optimization with two archives algorithm.SWARM AND EVOLUTIONARY COMPUTATION,89.

Jin, Honglin,et al."Solving dynamic multimodal optimization problems via a niching-based brain storm optimization with two archives algorithm".SWARM AND EVOLUTIONARY COMPUTATION 89(2024).