Clustering-Based Subset Selection in Evolutionary Multiobjective Optimization

• URL: http://arxiv.org/abs/2108.08453v2
• Date: Sun, 29 Aug 2021 13:48:03 GMT
• Title: Clustering-Based Subset Selection in Evolutionary Multiobjective Optimization
• Authors: Weiyu Chen, Hisao Ishibuchi, and Ke Shang
• Abstract summary: Subset selection is an important component in evolutionary multiobjective optimization (EMO) algorithms. Clustering-based methods have not been evaluated in the context of subset selection from solution sets obtained by EMO algorithms.
• Score: 11.110675371854988
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Subset selection is an important component in evolutionary multiobjective optimization (EMO) algorithms. Clustering, as a classic method to group similar data points together, has been used for subset selection in some fields. However, clustering-based methods have not been evaluated in the context of subset selection from solution sets obtained by EMO algorithms. In this paper, we first review some classic clustering algorithms. We also point out that another popular subset selection method, i.e., inverted generational distance (IGD)-based subset selection, can be viewed as clustering. Then, we perform a comprehensive experimental study to evaluate the performance of various clustering algorithms in different scenarios. Experimental results are analyzed in detail, and some suggestions about the use of clustering algorithms for subset selection are derived. Additionally, we demonstrate that decision maker's preference can be introduced to clustering-based subset selection.

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