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

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Abstract

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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Notes

  1. Strictly speaking, the differences between MOEA/D-DE (Li and Zhang 2009) and the original MOEA/D (Zhang and Li 2007) are as follows: (i) the parent individuals are selected from the whole population with some probability, (ii) the number of individuals replaced by a child is restricted, and (iii) the DE variation operator is used in Li and Zhang (2009).

  2. http://jmetal.sourceforge.net/.

  3. http://moeaframework.org/index.html.

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Acknowledgements

This work was supported by National Natural Science Foundation of China (Grant No. 61876075), the Program for Guangdong Introducing Innovative and Entrepreneurial Teams (Grant No. 2017ZT07X386), Shenzhen Peacock Plan (Grant No. KQTD2016112514355531), the Science and Technology Innovation Committee Foundation of Shenzhen (Grant No. ZDSYS201703031748284), and the Program for University Key Laboratory of Guangdong Province (Grant No. 2017KSYS008).

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  1. Shenzhen Key Laboratory of Computational Intelligence, University Key Laboratory of Evolving Intelligent Systems of Guangdong Province, Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, 518055, China

    Ryoji Tanabe & Hisao Ishibuchi

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  1. Ryoji Tanabe
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  2. Hisao Ishibuchi
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Correspondence to Hisao Ishibuchi.

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Tanabe, R., Ishibuchi, H. Review and analysis of three components of the differential evolution mutation operator in MOEA/D-DE. Soft Comput 23, 12843–12857 (2019). https://doi.org/10.1007/s00500-019-03842-6

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