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摘要:
Since the exploration of multiple solution sets will lead to the deterioration of convergence in multi-objective particle swarm optimization, the motion of the particles is severely disturbed by the under-convergence solutions in multi-modal multi-objective optimization problems (MMOPs). To solve this problem, a multi-modal multi-objective particle swarm optimization with selfadjusting strategy (MMOPSOSS) is proposed to promote the complete convergence of multiple solution sets through the self-adjusting of parameters and population size. First, a multi-swarm optimization framework is designed to obtain diverse convergence directions. Second, a selfadjusting local search mechanism is introduced to improve the search performance of subswarms in the potential regions according to the feedback information detected by diversity entropy under this framework. Third, a sub-swarm-balancing strategy is developed to balance the degree of convergence among different regions by adjusting the size of the sub-swarms. Finally, MMOPSOSS is compared with several multi-modal multi-objective optimization algorithms in benchmark experiments and engineering simulation experiments. The results demonstrate that MMOPSOSS has a positive effect on the convergence of multiple solution sets for MMOPs.
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来源 :
INFORMATION SCIENCES
ISSN: 0020-0255
年份: 2023
卷: 629
页码: 580-598
8 . 1 0 0
JCR@2022
ESI学科: COMPUTER SCIENCE;
ESI高被引阀值:19
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