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A hierarchical clustering algorithm for addressing multi-modal multi-objective optimization problems
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AI
DOI：10.1016/j.eswa.2024.125710.png)
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Abstract
En 中文
Many evolutionary multi-modal multi-objective algorithms (MMEAs) have been proposed to solve multi-modal multi-objective optimization problems (MMOPs). Unfortunately, the environmental selection process causes many algorithms to place too much emphasis on solution variety in the decision space, which leads to solutions with low convergence quality. As a result, not only are all local Pareto fronts reversed, but objective values are also far lower than the global Pareto Fronts. To tackle these tasks, this paper proposes a hierarchical clustering-based MMOEA_DC_HR model that uses decision space clustering methods to group neighborhood solutions into several local clusters, preserving local Pareto Sets. And secondary clustering is performed in the objective space to select temporary populations from these local clusters to maintain the diversity of the objective space. Additionally, a hierarchical ranking method is introduced to update the convergence archive, aiding in maintaining the convergence of the algorithm and controlling the quality of the Pareto Front. The test results show that this novel algorithm exhibits competitive performance in solving selected benchmark problems when compared to other cutting-edge MMEAs.
Keywords:
Multi-modal multi-objective optimization
Evolutionary computation
Hierarchical clustering
Local convergence
Journal
IF:
7.5
Papers:
2.9W
Citations:
10.2W
