TY - GEN
T1 - A Neighborhood-Based Speciation Brain Storm Optimization with Evolution Strategy for Multimodal Optimization
AU - Jin, Honglin
AU - Cheng, Shi
AU - Wang, Xueping
AU - Liu, Yue
AU - Shan, Yuyuan
AU - Ran, Hao
AU - Lu, Hui
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Finding multiple optimal solutions is challenging for solving multimodal optimization problems (MMOPs). In this paper, a neighborhood-based speciation brain storm optimization with evolution strategy (NS-BSO-ES) is proposed to solve MMOPs, which combines the advantages of better exploration of the neighborhood-based speciation brain storm optimization (NS-BSO) and more robust exploitation of the evolution strategy with covariance matrix adaptation (CMA-ES). In NS-BSO-ES, NS- BSO is used to generate candidate solutions to maintain the diversity of the population, CMA-ES is adopted to enhance the local search ability and locate optimal solutions accurately, and the archive is used to store inferior solutions to fully utilize the valuable information contained in these solutions as potential directions towards the optimal solution. To test the performance of NS-BSO-ES for solving MMOPs, compared with related algorithms on the 20 benchmark MMOPs in CEC-2013 Functions. Experimental results indicate NS-BSO-ES outperforms the other compared algorithms on most tested benchmark functions.
AB - Finding multiple optimal solutions is challenging for solving multimodal optimization problems (MMOPs). In this paper, a neighborhood-based speciation brain storm optimization with evolution strategy (NS-BSO-ES) is proposed to solve MMOPs, which combines the advantages of better exploration of the neighborhood-based speciation brain storm optimization (NS-BSO) and more robust exploitation of the evolution strategy with covariance matrix adaptation (CMA-ES). In NS-BSO-ES, NS- BSO is used to generate candidate solutions to maintain the diversity of the population, CMA-ES is adopted to enhance the local search ability and locate optimal solutions accurately, and the archive is used to store inferior solutions to fully utilize the valuable information contained in these solutions as potential directions towards the optimal solution. To test the performance of NS-BSO-ES for solving MMOPs, compared with related algorithms on the 20 benchmark MMOPs in CEC-2013 Functions. Experimental results indicate NS-BSO-ES outperforms the other compared algorithms on most tested benchmark functions.
KW - brain storm optimization
KW - evolution strategy with covariance matrix adaptation
KW - multimodal optimization
KW - neighborhood-based speciation
UR - https://www.scopus.com/pages/publications/85182738045
U2 - 10.1109/CSIS-IAC60628.2023.10363944
DO - 10.1109/CSIS-IAC60628.2023.10363944
M3 - 会议稿件
AN - SCOPUS:85182738045
T3 - 2023 International Annual Conference on Complex Systems and Intelligent Science, CSIS-IAC 2023
SP - 123
EP - 128
BT - 2023 International Annual Conference on Complex Systems and Intelligent Science, CSIS-IAC 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 International Annual Conference on Complex Systems and Intelligent Science, CSIS-IAC 2023
Y2 - 20 October 2023 through 22 October 2023
ER -