Multi-objective particle swarm optimization with two normal mutations

被引：0
|
作者：
Gao, Sheng-Guo [1 ]
Wu, Zhong [1 ]
Li, Xu-Fang [2 ,3 ]
Liu, Sheng [1 ]
机构：
[1] School of Management, Shanghai University of Engineering Science, Shanghai,201620, China
[2] School of Economics & Management, Tongji University, Shanghai,200092, China
[3] Shanghai Key Laboratory of Data Science, Fudan University, Shanghai,200433, China
来源：
Kongzhi yu Juece/Control and Decision | 2015年 / 30卷 / 05期
关键词：
Particle swarm optimization (PSO) - Pareto principle - Optimal systems;
D O I：
10.13195/j.kzyjc.2014.0426
中图分类号：
学科分类号：
摘要：
A particle swarm algorithm with two types of normal mutations is proposed for the multi-objective problem. One of variations contributes to discover new Pareto optimal solutions in the neighborhoods of these existing solutions, the other can disperse the swarm. The searching process is divided into three stages, and those particles which guide the others are selected with different targeted strategies in each stage. Numerical results show that the algorithm can significantly improve the diversity and convergence of the Pareto optimal solution. ©, 2015, Northeast University. All right reserved.
引用
收藏
页码：939 / 942
相关论文
共 50 条
  • [31] A particle swarm algorithm for multi-objective optimization problem
    Institute of Information Engineering, Xiangtan University, Xiangtan 411105, China
    Moshi Shibie yu Rengong Zhineng, 2007, 5 (606-611):
  • [32] Optimal Combination for Multi-objective Particle Swarm Optimization
    Qin, Zhangliang
    Liu, Yanbing
    2014 IEEE 7TH JOINT INTERNATIONAL INFORMATION TECHNOLOGY AND ARTIFICIAL INTELLIGENCE CONFERENCE (ITAIC), 2014, : 11 - 15
  • [33] Multi-Objective Mean Particle Swarm Optimization Algorithm
    Pei, Shengyu
    Zhou, Yongquan
    2010 8TH WORLD CONGRESS ON INTELLIGENT CONTROL AND AUTOMATION (WCICA), 2010, : 3315 - 3319
  • [34] A particle swarm optimization for multi-objective flowshop scheduling
    Department of Industrial Engineering and Management, National Chiao Tung University, Hsinchu, Taiwan
    Int J Adv Manuf Technol, 2009, 7-8 (749-758):
  • [35] A simplified multi-objective particle swarm optimization algorithm
    Trivedi, Vibhu
    Varshney, Pushkar
    Ramteke, Manojkumar
    SWARM INTELLIGENCE, 2020, 14 (02) : 83 - 116
  • [36] Multi-objective feasibility enhanced particle swarm optimization
    Hasanoglu, Mehmet Sinan
    Dolen, Melik
    ENGINEERING OPTIMIZATION, 2018, 50 (12) : 2013 - 2037
  • [37] Multi-objective particle swarm optimization with random immigrants
    Unal, Ali Nadi
    Kayakutlu, Gulgun
    COMPLEX & INTELLIGENT SYSTEMS, 2020, 6 (03) : 635 - 650
  • [38] Evolutionary Multi-objective Optimization of Particle Swarm Optimizers
    Veenhuis, Christian
    Koeppen, Mario
    Vicente-Garcia, Raul
    2007 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION, VOLS 1-10, PROCEEDINGS, 2007, : 2273 - +
  • [39] Movement Strategies for Multi-Objective Particle Swarm Optimization
    Nguyen, S.
    Kachitvichyanukul, V.
    INTERNATIONAL JOURNAL OF APPLIED METAHEURISTIC COMPUTING, 2010, 1 (03) : 59 - 79
  • [40] Adaptive Multi-objective Particle Swarm Optimization algorithm
    Tripathi, P. K.
    Bandyopadhyay, Sanghamitra
    Pal, S. K.
    2007 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION, VOLS 1-10, PROCEEDINGS, 2007, : 2281 - +
12345