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A constrained multi-objective evolutionary algorithm assisted by comprehensive ranking technique
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AI
DOI：10.1093/jcde/qwaf146.png)
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Abstract
En 中文
Balancing objective functions and constraints, as well as balancing diversity and convergence, are the main challenges for constrained multi-objective evolutionary algorithms. To address these challenges, this study proposes a comprehensive ranking technique that integrates multiple rankings with different preferences for objective functions and constraints. During the environmental selection process, the population is ranked using the comprehensive ranking technique, and the next generation population is selected based on the ranking results, thereby achieving a balance between objective functions and constraints. In addition, a search algorithm based on comprehensive ranking is proposed, which integrates two mutation operators with different characteristics. One mutation operator is used for exploration, while another mutation operator is used for exploitation, thus balancing diversity and convergence. Finally, a new constrained multi-objective evolutionary algorithm assisted by comprehensive ranking technique is proposed. To verify the performance of the proposed method, 56 test problems are used to compare the proposed method with 10 state-of-the-art algorithms. The experimental results show that the proposed method performs significantly better than the 10 algorithms. Furthermore, we validated the effectiveness of the proposed method in solving real-world problems based on some power electronics problems.
Keywords:
multi-objective optimization
comprehensive ranking
differential evolution
constrained optimization
Journal
IF:
6.1
Papers:
396
Citations:
3.2K
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