Velocity Restriction-Based Improvised Particle Swarm Optimization Algorithm

被引:4
|
作者
Mouna, H. [1 ]
Azhagan, M. S. Mukhil [1 ]
Radhika, M. N. [1 ]
Mekaladevi, V. [1 ]
Devi, M. Nirmala [1 ]
机构
[1] Amrita Univ, Dept Elect & Commun Engn, Coimbatore, Tamil Nadu, India
来源
PROGRESS IN ADVANCED COMPUTING AND INTELLIGENT ENGINEERING, VOL 2 | 2018年 / 564卷
关键词
Swarm intelligence; Global optimization; Intelligent search; Inertia weight; Velocity restriction; Pareto principle;
D O I
10.1007/978-981-10-6875-1_34
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The Particle Swarm Optimization (PSO) Algorithm attempts on the use of an improved range for inertia weight, social, and cognitive factors utilizing the Pareto principle. The function exhibits better convergence and search efficiency than PSO algorithms that use conventional linearly varying or exponentially varying inertia weights. It also presents a technique to intelligently navigate the search space around the obtained optima and looks for better optima if available and continue converging with the new values using a velocity restriction factor based on the Pareto principle. The improvised algorithm searches the neighborhood of the global optima while maintaining frequent resets in the position of some particles in the form of a mutation based on its escape probability. The results have been compared and tabulated against popular PSO with conventional weights and it has been shown that the introduced PSO performs much better on various benchmark functions.
引用
收藏
页码:351 / 360
页数:10
相关论文
共 50 条
  • [41] Optimization of the Particle Swarm Algorithm
    Chytil, J.
    PIERS 2014 GUANGZHOU: PROGRESS IN ELECTROMAGNETICS RESEARCH SYMPOSIUM, 2014, : 2355 - 2359
  • [42] An improved two-swarm based particle swarm optimization algorithm
    Li, Ting
    Lai, Xuzhi
    Wu, Min
    WCICA 2006: SIXTH WORLD CONGRESS ON INTELLIGENT CONTROL AND AUTOMATION, VOLS 1-12, CONFERENCE PROCEEDINGS, 2006, : 3129 - +
  • [43] Hybrid optimization algorithm based on chaos,cloud and particle swarm optimization algorithm
    Mingwei Li
    Haigui Kang
    Pengfei Zhou
    Weichiang Hong
    JournalofSystemsEngineeringandElectronics, 2013, 24 (02) : 324 - 334
  • [44] Hybrid optimization algorithm based on chaos, cloud and particle swarm optimization algorithm
    Li, Mingwei
    Kang, Haigui
    Zhou, Pengfei
    Hong, Weichiang
    JOURNAL OF SYSTEMS ENGINEERING AND ELECTRONICS, 2013, 24 (02) : 324 - 334
  • [45] A Hybrid Algorithm Based on Particle Swarm Optimization and Ant Colony Optimization Algorithm
    Lu, Junliang
    Hu, Wei
    Wang, Yonghao
    Li, Lin
    Ke, Peng
    Zhang, Kai
    SMART COMPUTING AND COMMUNICATION, SMARTCOM 2016, 2017, 10135 : 22 - 31
  • [46] Cooperative Velocity Updating model based Particle Swarm Optimization
    Wang, Hongbo
    Zhao, Xiaoqi
    Wang, Kezhen
    Xia, Kejian
    Tu, Xuyan
    APPLIED INTELLIGENCE, 2014, 40 (02) : 322 - 342
  • [47] Improved VRP based on particle swarm optimization algorithm
    Chen, Zixia
    Xuan, Youshi
    DCABES 2006 PROCEEDINGS, VOLS 1 AND 2, 2006, : 436 - 439
  • [48] Blending scheduling based on particle swarm optimization algorithm
    Zhao, Xiaoqiang
    2010 CHINESE CONTROL AND DECISION CONFERENCE, VOLS 1-5, 2010, : 1192 - 1196
  • [49] Particle swarm optimization algorithm based on escape boundary
    Han, Wenhua
    NATURAL RESOURCES AND SUSTAINABLE DEVELOPMENT, PTS 1-3, 2012, 361-363 : 1426 - 1431
  • [50] New particle swarm optimization algorithm based on similarity
    Liu, Jian-Hua
    Fan, Xiao-Ping
    Qu, Zhi-Hua
    Kongzhi yu Juece/Control and Decision, 2007, 22 (10): : 1155 - 1159