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An efficient multi-objective optimization algorithm based on level swarm optimizer

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  • Zhang, XuWei
  • Liu, Hao
  • Tu, LiangPing
  • Zhao, Jian

Abstract

In the past few decades, evolutionary multi-objective optimization has become a research hotspot in the field of evolutionary computing, and a large number of multi-objective evolutionary algorithms (MOEAs) have been proposed. However, MOEA is still faced with the problem that the diversity and convergence of non-dominated solutions are difficult to balance. To address these problems, an efficient multi-objective optimization algorithm based on level swarm optimizer (EMOSO) is proposed in this paper. In EMOSO, a sorting method is introduced to balance the diversity and convergence of non-dominated solutions in the whole population, which is based on non-dominated relationship and density estimation. Meanwhile, a level-based learning strategy is introduced to maintain the search for non-dominated solutions. Finally, DTLZ, ZDT and WFG series problems are utilized to verify the performance of the proposed EMOSO. Experimental results and statistical analysis indicate that EMOSO has competitive performance compared with 6 popular MOEAs. The source code of EMOSO is provided at: https://github.com/xuweizhang163/EMOSO.

Suggested Citation

  • Zhang, XuWei & Liu, Hao & Tu, LiangPing & Zhao, Jian, 2020. "An efficient multi-objective optimization algorithm based on level swarm optimizer," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 177(C), pages 588-602.
    • Handle: RePEc:eee:matcom:v:177:y:2020:i:c:p:588-602
    DOI: 10.1016/j.matcom.2020.05.025
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    References listed on IDEAS

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    1. Beume, Nicola & Naujoks, Boris & Emmerich, Michael, 2007. "SMS-EMOA: Multiobjective selection based on dominated hypervolume," European Journal of Operational Research, Elsevier, vol. 181(3), pages 1653-1669, September.
    2. Cai Dai & Yuping Wang & Wei Yue, 2015. "A new orthogonal evolutionary algorithm based on decomposition for multi-objective optimization," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 66(10), pages 1686-1698, October.
    3. Jianchang Liu & Fei Li & Xiangyong Kong & Peiqiu Huang, 2019. "Handling many-objective optimisation problems with R2 indicator and decomposition-based particle swarm optimiser," International Journal of Systems Science, Taylor & Francis Journals, vol. 50(2), pages 320-336, January.
    4. Lin, Qiuzhen & Li, Jianqiang & Du, Zhihua & Chen, Jianyong & Ming, Zhong, 2015. "A novel multi-objective particle swarm optimization with multiple search strategies," European Journal of Operational Research, Elsevier, vol. 247(3), pages 732-744.
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    Cited by:

    1. Cuevas, Erik & Becerra, Héctor & Luque, Alberto, 2021. "Anisotropic diffusion filtering through multi-objective optimization," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 181(C), pages 410-429.

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