Survey of multi-objective particle swarm optimization algorithms and their applications

被引：0
|
作者：
Ye Q. [1 ]
Wang W. [1 ]
Wang Z. [2 ]
机构：
[1] College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou
[2] School of Computer and Computational Sciences, Hangzhou City University, Hangzhou
来源：
Zhejiang Daxue Xuebao (Gongxue Ban)/Journal of Zhejiang University (Engineering Science) | 2024年 / 58卷 / 06期
关键词：
convergence; diversity; multi-objective optimization; Pareto solution set; particle swarm optimization;
D O I：
10.3785/j.issn.1008-973X.2024.06.002
中图分类号：
学科分类号：
摘要：
Few existing studies cover the state-of-the-art multi-objective particle swarm optimization (MOPSO) algorithms. To fill the gap in this area, the research background of multi-objective optimization problems (MOPs) was introduced, and the fundamental theories of MOPSO were described. The MOPSO algorithms were divided into three categories according to their features: Pareto-dominated-based MOPSO, decomposition-based MOPSO, and indicator-based MOPSO, and a detailed description of their existing classical algorithms was also developed. Next, relevant evaluation indicators were described, and seven representative algorithms were selected for performance analysis. The experimental results demonstrated the strengths and weaknesses of each of the traditional MOPSO and three categories of improved MOPSO algorithms. Among them, the indicator-based MOPSO performed better in terms of convergence and diversity. Then, the applications of MOPSO algorithms in production scheduling, image processing, and power systems were briefly introduced. Finally, the limitations and future research directions of the MOPSO algorithm for solving complex optimization problems were discussed. © 2024 Zhejiang University. All rights reserved.
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页码：1107 / 1120+1232
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