A Multi-Objective Particle Swarm Optimization Algorithm Based on Gaussian Mutation and an Improved Learning Strategy

被引:28
|
作者
Sun, Ying [1 ]
Gao, Yuelin [1 ,2 ]
机构
[1] Hefei Univ Technol, Sch Comp Sci & Informat Engn, Hefei 230009, Anhui, Peoples R China
[2] North Minzu Univ, Ningxia Prov Key Lab Intelligent Informat & Data, Yinchuan 750021, Peoples R China
关键词
multi-objective optimization problems; particle swarm optimization (PSO); Gaussian mutation; improved learning strategy; EVOLUTIONARY ALGORITHMS;
D O I
10.3390/math7020148
中图分类号
O1 [数学];
学科分类号
0701 ; 070101 ;
摘要
Obtaining high convergence and uniform distributions remains a major challenge in most metaheuristic multi-objective optimization problems. In this article, a novel multi-objective particle swarm optimization (PSO) algorithm is proposed based on Gaussian mutation and an improved learning strategy. The approach adopts a Gaussian mutation strategy to improve the uniformity of external archives and current populations. To improve the global optimal solution, different learning strategies are proposed for non-dominated and dominated solutions. An indicator is presented to measure the distribution width of the non-dominated solution set, which is produced by various algorithms. Experiments were performed using eight benchmark test functions. The results illustrate that the multi-objective improved PSO algorithm (MOIPSO) yields better convergence and distributions than the other two algorithms, and the distance width indicator is reasonable and effective.
引用
收藏
页数:16
相关论文
共 50 条
  • [31] Improved multi-objective particle swarm optimization algorithm based on phase angle reflection
    Li, T. (litingcsu@163.com), 1600, Northeast University (28):
  • [32] Multi-objective Gaussian particle swarm algorithm optimization based on niche sorting for actuator design
    Liang, Huimin
    Zhang, Kun
    You, Jiaxin
    Yu, Hao
    ADVANCES IN MECHANICAL ENGINEERING, 2015, 7 (12)
  • [33] A novel multi-objective decomposition particle swarm optimization based on comprehensive learning strategy
    Wei, Lixin
    Fan, Rui
    Li, Xin
    PROCEEDINGS OF THE 36TH CHINESE CONTROL CONFERENCE (CCC 2017), 2017, : 2761 - 2766
  • [34] Modified Multi-Objective Particle Swarm Optimization Algorithm for Multi-objective Optimization Problems
    Qiao, Ying
    ADVANCES IN SWARM INTELLIGENCE, ICSI 2012, PT I, 2012, 7331 : 520 - 527
  • [35] Multi-objective optimization of marine nuclear power secondary circuit system based on improved multi-objective particle swarm optimization algorithm
    Zhao, Jiarui
    Li, Yanjun
    Bai, Jinfeng
    Ma, Lin
    Shi, Changwei
    Zhang, Guolei
    Shi, Jianxin
    PROGRESS IN NUCLEAR ENERGY, 2023, 161
  • [36] An improved multi-objective particle swarm optimisation algorithm
    Fu, Tiaoping
    Shang Ya-Ling
    INTERNATIONAL JOURNAL OF MODELLING IDENTIFICATION AND CONTROL, 2011, 12 (1-2) : 66 - 71
  • [37] An Improved Multi-objective Particle Swarm Optimization Algorithm for Polarity Optimization of FPRM Circuits
    Fu Q.
    Wang P.
    Wang M.
    Tong N.
    Zhang H.
    2018, Institute of Computing Technology (30): : 540 - 548
  • [38] Application of improved multi-objective particle swarm optimization algorithm in discrete combinatorial optimization
    Xia, Yu
    Wu, Peng
    Wu, Tianshu
    Chu, Da
    BASIC & CLINICAL PHARMACOLOGY & TOXICOLOGY, 2019, 125 : 156 - 156
  • [39] Application of improved particle swarm optimization algorithm to multi-objective reactive power optimization
    Li, Xinbin
    Zhu, Qingjun
    Diangong Jishu Xuebao/Transactions of China Electrotechnical Society, 2010, 25 (07): : 137 - 143
  • [40] An Optimization Method of Spare Parts Allocation Based on the Improved Multi-objective Particle Swarm Optimization Algorithm
    Pan, Guangze
    Li, Xiaobing
    Luo, Qin
    Wang, Yuanhang
    BASIC & CLINICAL PHARMACOLOGY & TOXICOLOGY, 2020, 126 : 124 - 124