TY - GEN
T1 - Brain Storm Optimization Integrated with Cooperative Coevolution for Large-Scale Constrained Optimization
AU - Sun, Yuetong
AU - Xu, Peilan
AU - Zhang, Ziyu
AU - Zhu, Tao
AU - Luo, Wenjian
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2023
Y1 - 2023
N2 - Large-scale constrained optimization problems (LSCOPs) are challenging to solve because of the high dimensionality and constraint limitations. Although cooperative coevolution (CC) has been applied to LSCOPs, more efficient optimizers that could be adapted to CC are still required. In this paper, we propose ConBSO, a variant of the brain storm optimization (BSO) designed for constrained optimization. Then, ConBSO is integrated into constraint-objective cooperative coevolution (COCC), denoted as COCC-ConBSO. To evaluate the performance of COCC-ConBSO, we test it on the benchmark suite with 12 LSCOPs and compared it to several algorithms, including two algorithms based on the COCC framework and three state-of-the-art large-scale constrained optimization algorithms. Experimental results demonstrate the adaptability of ConBSO to COCC and highlight the competitiveness of COCC-ConBSO in solving LSCOPs.
AB - Large-scale constrained optimization problems (LSCOPs) are challenging to solve because of the high dimensionality and constraint limitations. Although cooperative coevolution (CC) has been applied to LSCOPs, more efficient optimizers that could be adapted to CC are still required. In this paper, we propose ConBSO, a variant of the brain storm optimization (BSO) designed for constrained optimization. Then, ConBSO is integrated into constraint-objective cooperative coevolution (COCC), denoted as COCC-ConBSO. To evaluate the performance of COCC-ConBSO, we test it on the benchmark suite with 12 LSCOPs and compared it to several algorithms, including two algorithms based on the COCC framework and three state-of-the-art large-scale constrained optimization algorithms. Experimental results demonstrate the adaptability of ConBSO to COCC and highlight the competitiveness of COCC-ConBSO in solving LSCOPs.
KW - Brain storm optimization
KW - Cooperative coevolution
KW - Large-scale constrained optimization
UR - https://www.scopus.com/pages/publications/85169033751
U2 - 10.1007/978-3-031-36622-2_29
DO - 10.1007/978-3-031-36622-2_29
M3 - 会议稿件
AN - SCOPUS:85169033751
SN - 9783031366215
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 356
EP - 368
BT - Advances in Swarm Intelligence - 14th International Conference, ICSI 2023, Proceedings
A2 - Tan, Ying
A2 - Shi, Yuhui
A2 - Luo, Wenjian
PB - Springer Science and Business Media Deutschland GmbH
T2 - 14th International Conference on Advances in Swarm Intelligence, ICSI 2023
Y2 - 14 July 2023 through 18 July 2023
ER -