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Multiswarm comprehensive learning particle swarm optimization for solving multiobjective optimization problems

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  • Xiang Yu
  • Xueqing Zhang

Abstract

Comprehensive learning particle swarm optimization (CLPSO) is a powerful state-of-the-art single-objective metaheuristic. Extending from CLPSO, this paper proposes multiswarm CLPSO (MSCLPSO) for multiobjective optimization. MSCLPSO involves multiple swarms, with each swarm associated with a separate original objective. Each particle’s personal best position is determined just according to the corresponding single objective. Elitists are stored externally. MSCLPSO differs from existing multiobjective particle swarm optimizers in three aspects. First, each swarm focuses on optimizing the associated objective using CLPSO, without learning from the elitists or any other swarm. Second, mutation is applied to the elitists and the mutation strategy appropriately exploits the personal best positions and elitists. Third, a modified differential evolution (DE) strategy is applied to some extreme and least crowded elitists. The DE strategy updates an elitist based on the differences of the elitists. The personal best positions carry useful information about the Pareto set, and the mutation and DE strategies help MSCLPSO discover the true Pareto front. Experiments conducted on various benchmark problems demonstrate that MSCLPSO can find nondominated solutions distributed reasonably over the true Pareto front in a single run.

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  • Xiang Yu & Xueqing Zhang, 2017. "Multiswarm comprehensive learning particle swarm optimization for solving multiobjective optimization problems," PLOS ONE, Public Library of Science, vol. 12(2), pages 1-21, February.
    • Handle: RePEc:plo:pone00:0172033
    DOI: 10.1371/journal.pone.0172033
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    References listed on IDEAS

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    2. Xiang Yu & Hui Sun & Hui Wang & Zuhan Liu & Jia Zhao & Tianhui Zhou & Hui Qin, 2016. "Multi-Objective Sustainable Operation of the Three Gorges Cascaded Hydropower System Using Multi-Swarm Comprehensive Learning Particle Swarm Optimization," Energies, MDPI, vol. 9(6), pages 1-18, June.
    3. Du, Wen-Bo & Gao, Yang & Liu, Chen & Zheng, Zheng & Wang, Zhen, 2015. "Adequate is better: particle swarm optimization with limited-information," Applied Mathematics and Computation, Elsevier, vol. 268(C), pages 832-838.
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