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An improved estimation of distribution algorithm for multi-objective optimization problems with mixed-variable

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Abstract

Multi-objective evolutionary algorithms face many challenges in optimizing mixed-variable multi-objective problems, such as quantization error, low search efficiency of discontinuous discrete variables, and difficulty in coding non-integer discrete variables. To overcome these challenges, this paper proposes a mixed-variable multi-objective evolutionary algorithm based on estimation of distribution algorithm (MVMO-EDA). Compared with traditional multi-objective evolutionary algorithms, MVMO-EDA has the following improvements: (1) instead of crossover and mutation, statistics and sampling are used to generate offspring, which can avoid the quantization error caused by crossover and mutation operations; (2) using index coding for discrete variables to improve the search efficiency; and (3) a scalable histogram probability distribution model and two crowding distance-based diversity maintenance strategies are used to improve the global optimization ability. The performance of the proposed MVMO-EDA is evaluated on the modified ZDT and DTLZ benchmark sets with mixed-variable, and the results show that MVMO-EDA has a competitive performance both in convergence and diversity.

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Funding

This work was supported by the Key Field Special Project of Guangdong Provincial Department of Education with No.2021ZDZX1029. And it was also supported by Guangdong Basic and Applied Basic Research Foundation under No. Grant 2022A1515011447.

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Authors and Affiliations

  1. College of Mathematics and Informatics, South China Agricultural University, Guangzhou, 51006, China

    Wenxiang Wang, Kangshun Li & Hassan Jalil

  2. School of Computer Science, Guangdong University of Science and Technology, Dongguan, 523000, China

    Kangshun Li

  3. School of Software Engineering, Shenzhen Institute of Information Technology, Shenzhen, 518172, China

    Hui Wang

Authors
  1. Wenxiang Wang
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  2. Kangshun Li
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  3. Hassan Jalil
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  4. Hui Wang
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Correspondence to Kangshun Li.

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Wang, W., Li, K., Jalil, H. et al. An improved estimation of distribution algorithm for multi-objective optimization problems with mixed-variable. Neural Comput & Applic 34, 19703–19721 (2022). https://doi.org/10.1007/s00521-022-07695-3

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