Review and analysis of three components of the differential evolution mutation operator in MOEA/D-DE

被引：0
作者：
Ryoji Tanabe
Hisao Ishibuchi
机构：
[1] Southern University of Science and Technology,Shenzhen Key Laboratory of Computational Intelligence, University Key Laboratory of Evolving Intelligent Systems of Guangdong Province, Department of Computer Science and Engineering
来源：
Soft Computing | 2019年 / 23卷
关键词：
Multi-objective optimization; Decomposition-based evolutionary algorithms; Differential evolution operators; Implementation of algorithms;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
A decomposition-based multi-objective evolutionary algorithm with a differential evolution variation operator (MOEA/D-DE) shows high performance on challenging multi-objective problems (MOPs). The DE mutation consists of three key components: a mutation strategy, an index selection method for parent individuals, and a bound-handling method. However, the configuration of the DE mutation operator that should be used for MOEA/D-DE has not been thoroughly investigated in the literature. This configuration choice confuses researchers and users of MOEA/D-DE. To address this issue, we present a review of the existing configurations of the DE mutation operator in MOEA/D-DE and systematically examine the influence of each component on the performance of MOEA/D-DE. Our review reveals that the configuration of the DE mutation operator differs depending on the source code of MOEA/D-DE. In our analysis, a total of 30 configurations (three index selection methods, two mutation strategies, and five bound-handling methods) are investigated on 16 MOPs with up to five objectives. Results show that each component significantly affects the performance of MOEA/D-DE. We also present the most suitable configuration of the DE mutation operator, which maximizes the effectiveness of MOEA/D-DE.
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页码：12843 / 12857
页数：14
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