期刊
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
卷 25, 期 6, 页码 1064-1078出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TEVC.2021.3078441
关键词
Optimization; Convergence; Statistics; Sociology; Pareto optimization; Task analysis; Sensitivity; Diversity-preserving mechanisms; evolutionary computation; indicator-based algorithms; multimodal multiobjective optimization
资金
- National Natural Science Foundation of China [72071205, 7207010433]
- Hunan Youth Elite Program [2018RS3081]
- Scientific Key Research Project of National University of Defense Technology [ZK18-0209, ZZKY-ZX-11-04, 193-A11-101-03-01]
This study proposes an MMEA-WI algorithm based on a weighted indicator for solving multimodal multiobjective problems, which outperforms some state-of-the-art MMEAs in terms of performance metrics. By integrating diversity information and introducing a convergence archive, the algorithm effectively maintains diversity and ensures a better approximation of the Pareto-optimal front.
Multimodal multiobjective problems (MMOPs) arise frequently in the real world, in which multiple Pareto-optimal solution (PS) sets correspond to the same point on the Pareto front. Traditional multiobjective evolutionary algorithms (MOEAs) show poor performance in solving MMOPs due to a lack of diversity maintenance in the decision space. Thus, recently, many multimodal MOEAs (MMEAs) have been proposed. However, for most existing MMEAs, the convergence performance in the objective space does not meet expectations. In addition, many of them cannot always obtain all equivalent Pareto solution sets. To address these issues, this study proposes an MMEA based on a weighted indicator, termed MMEA-WI. The algorithm integrates the diversity information of solutions in the decision space into an objective space performance indicator to maintain the diversity in the decision space and introduces a convergence archive to ensure a more effective approximation of the Pareto-optimal front (PF). These strategies can readily be applied to other indicator-based MOEAs. The experimental results show that MMEA-WI outperforms some state-of-the-art MMEAs on the chosen benchmark problems in terms of the inverted generational distance (IGD) and IGD in the decision space (IGDX) metrics.
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