Comparing nonlinear inertia weights and constriction factors in particle swarm optimization

被引：25
作者：
Tuppadung, Yutthapong [1 ]
Kurutach, Werasak [1 ]
机构：
[1] Nakhonratchasima Coll, Fac Informat Sci, Nakhon Ratchasima 30000, Thailand
来源：
INTERNATIONAL JOURNAL OF KNOWLEDGE-BASED AND INTELLIGENT ENGINEERING SYSTEMS | 2011年 / 15卷 / 02期
关键词：
Nonlinear inertia weight; particle swarm optimization; constriction factor; PSO; nonlinear decreasing inertia weight;
D O I：
10.3233/KES-2010-0211
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper, we empirically study the performance of particle swarm optimization (PSO) using a nonlinear inertia weight compared with the performance using a constriction factor. Adjusting the parameters of nonlinear inertia weight directly affects the performance of PSO. Five benchmark functions are used for evaluation. Under all testing functions, the PSO with nonlinear inertia weights consistently converges faster than the PSO with constriction factors.
引用
收藏
页码：65 / 70
页数：6
相关论文
共 50 条
  • [41] Personal best oriented constriction type particle swarm optimization
    Chen, Chang-Huang
    Yeh, Sheng-Nian
    [J]. 2006 IEEE CONFERENCE ON CYBERNETICS AND INTELLIGENT SYSTEMS, VOLS 1 AND 2, 2006, : 436 - +
  • [42] Adaptive mutation particle swarm algorithm with dynamic nonlinear changed inertia weight
    Liang, H. T.
    Kang, F. H.
    [J]. OPTIK, 2016, 127 (19): : 8036 - 8042
  • [43] Particle swarm optimization in nonlinear systems
    Liu, Xiyu
    Zhang, Jianping
    Liu, Hong
    [J]. PROCEEDINGS OF THE 2007 1ST INTERNATIONAL SYMPOSIUM ON INFORMATION TECHNOLOGIES AND APPLICATIONS IN EDUCATION (ISITAE 2007), 2007, : 54 - +
  • [44] Natural exponential inertia weight strategy in particle swarm optimization
    Chen, Guimin
    Huang, Xinbo
    Jia, Jianyuan
    Min, Zhengfeng
    [J]. WCICA 2006: SIXTH WORLD CONGRESS ON INTELLIGENT CONTROL AND AUTOMATION, VOLS 1-12, CONFERENCE PROCEEDINGS, 2006, : 3672 - +
  • [45] Improved Particle Swarm Optimization With Dynamically Changing Inertia Weight
    Wang, Dongyun
    Zeng, Ping
    Wang, Kai
    Li, Luowei
    [J]. PROCEEDINGS OF 2010 INTERNATIONAL CONFERENCE ON INFORMATION TECHNOLOGY AND INDUSTRIAL ENGINEERING, VOLS I AND II, 2010, : 805 - 808
  • [46] A Chaos Particle Swarm Optimization based on Adaptive Inertia Weight
    Jie, Zheng
    [J]. ADVANCED MATERIALS AND COMPUTER SCIENCE, PTS 1-3, 2011, 474-476 : 1458 - 1463
  • [47] Estimation of Power System Inertia Using Particle Swarm Optimization
    Zografos, Dimitrios
    Ghandhari, Mehrdad
    Paridari, Kaveh
    [J]. 2017 19TH INTERNATIONAL CONFERENCE ON INTELLIGENT SYSTEM APPLICATION TO POWER SYSTEMS (ISAP), 2017,
  • [48] A novel particle swarm optimization algorithm with adaptive inertia weight
    Nickabadi, Ahmad
    Ebadzadeh, Mohammad Mehdi
    Safabakhsh, Reza
    [J]. APPLIED SOFT COMPUTING, 2011, 11 (04) : 3658 - 3670
  • [49] An Analysis of the Inertia Weight Parameter for Binary Particle Swarm Optimization
    Liu, Jianhua
    Mei, Yi
    Li, Xiaodong
    [J]. IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, 2016, 20 (05) : 666 - 681
  • [50] Uniform design and inertia mutation based particle swarm optimization
    Zhang, Boquan
    Yang, Yimin
    Wang, Jianbin
    [J]. MIPPR 2007: MEDICAL IMAGING, PARALLEL PROCESSING OF IMAGES, AND OPTIMIZATION TECHNIQUES, 2007, 6789
12345