On convergence analysis of multi-objective particle swarm optimization algorithm

Gang Xu, Kun Luo, Guoxiu Jing, Xiang Yu, Xiaojun Ruan and Jun Song

European Journal of Operational Research, 2020, vol. 286, issue 1, 32-38

Abstract: Multi-objective particle swarm optimization (MOPSO), a population-based stochastic optimization algorithm, has been successfully used to solve many multi-objective optimization problems. However, the analysis of algorithm convergence is still inadequate nowadays. In this paper, probability theory is applied to analyze the convergence of the original MOPSO. First, a convergence metric is defined. Afterwards, the global convergence of the original MOPSO is transformed into the convergence of the convergence metric sequence. Finally, the defined convergence metric is utilized to analyze the global convergence of the original MOPSO in terms of probability theory. Our results show that the original MOPSO cannot guarantee global convergence with probability one. Moreover, the analysis of the original MOPSO indicates that the improved vision of the original MOPSO is a global convergence algorithm. The proof of the original MOPSO convergence in this work is new, simple and more effective without specific implementation.

Keywords: Global optimization; Particle swarm optimization; Multi-objective optimization; Convergence; Probability (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (6)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:286:y:2020:i:1:p:32-38

DOI: 10.1016/j.ejor.2020.03.035

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