θ-PSO: a new strategy of particle swarm optimization

被引:0
|
作者
Zhong Wei-min
Li Shao-jun
Qian Feng
机构
[1] East China University of Science and Technology,State Key Laboratory of Chemical Engineering
[2] East China University of Science and Technology,Automation Institute
关键词
Particle swarm optimization (PSO); Phase angle; Benchmark function; TP301.6;
D O I
暂无
中图分类号
学科分类号
摘要
Particle swarm optimization (PSO) is an efficient, robust and simple optimization algorithm. Most studies are mainly concentrated on better understanding of the standard PSO control parameters, such as acceleration coefficients, etc. In this paper, a more simple strategy of PSO algorithm called θ-PSO is proposed. In θ-PSO, an increment of phase angle vector replaces the increment of velocity vector and the positions are decided by the mapping of phase angles. Benchmark testing of nonlinear functions is described and the results show that the performance of θ-PSO is much more effective than that of the standard PSO.
引用
收藏
页码:786 / 790
页数:4
相关论文
共 50 条
  • [1] θ-PSO:: a new strategy of particle swarm optimization
    Zhong, Wei-min
    Li, Shao-jun
    Qian, Feng
    JOURNAL OF ZHEJIANG UNIVERSITY-SCIENCE A, 2008, 9 (06): : 786 - 790
  • [3] A NEW KIND OF PSO: PREDATOR PARTICLE SWARM OPTIMIZATION
    Neshat, Mehdi
    Sargolzaei, Mehdi
    Masoumi, Azra
    Najaran, Adel
    INTERNATIONAL JOURNAL ON SMART SENSING AND INTELLIGENT SYSTEMS, 2012, 5 (02): : 521 - 539
  • [4] Particle swarm optimization (PSO). A tutorial
    Marini, Federico
    Walczak, Beata
    CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS, 2015, 149 : 153 - 165
  • [5] A New Strategy for Improving Particle Swarm Optimization
    Yang, Xixiang
    Zhang, Weihua
    ICICTA: 2009 SECOND INTERNATIONAL CONFERENCE ON INTELLIGENT COMPUTATION TECHNOLOGY AND AUTOMATION, VOL I, PROCEEDINGS, 2009, : 228 - 232
  • [6] PSO plus : A new particle swarm optimization algorithm for constrained problems
    Kohler, Manoela
    Vellasco, Marley M. B. R.
    Tanscheit, Ricardo
    APPLIED SOFT COMPUTING, 2019, 85
  • [7] A new strategy of acceleration coefficients for particle swarm optimization
    Guo, Wenzhong
    Chen, Guolong
    Feng, Xiang
    2006 10TH INTERNATIONAL CONFERENCE ON COMPUTER SUPPORTED COOPERATIVE WORK IN DESIGN, PROCEEDINGS, VOLS 1 AND 2, 2006, : 72 - 76
  • [8] A new fitness estimation strategy for particle swarm optimization
    Sun, Chaoli
    Zeng, Jianchao
    Pan, Jengshyang
    Xue, Songdong
    Jin, Yaochu
    INFORMATION SCIENCES, 2013, 221 : 355 - 370
  • [9] Robotic Applications with Particle Swarm Optimization (PSO)
    Das, M. Taylan
    Dulger, L. Canan
    Das, G. Sena
    2013 INTERNATIONAL CONFERENCE ON CONTROL, DECISION AND INFORMATION TECHNOLOGIES (CODIT), 2013, : 160 - 165
  • [10] Fuzzy PSO: A generalization of particle swarm optimization
    Abdelbar, AM
    Abdelshahid, S
    Wunsch, DC
    PROCEEDINGS OF THE INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), VOLS 1-5, 2005, : 1086 - 1091