Improving the performance of particle swarm optimization using adaptive critics designs

被引：0
|
作者：
Doctor, S [1 ]
Venayagamoorthy, GK [1 ]
机构：
[1] Univ Missouri, Dept Elect & Comp Engn, Real Time Power & Intelligent Syst Lab, Rolla, MO 65409 USA
来源：
2005 IEEE SWARM INTELLIGENCE SYMPOSIUM | 2005年
关键词：
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Swarm intelligence algorithms are based on natural behaviors. Particle swarm optimization (PSO) is a stochastic search and optimization tool. Changes in the PSO parameters, namely the inertia weight and the cognitive and social acceleration constants, affect the performance of the search process. This paper presents a novel method to dynamically change the values of these parameters during the search. Adaptive critic design (ACD) has been applied for dynamically changing the values of the PSO parameters.
引用
收藏
页码：393 / 396
页数：4
相关论文
共 50 条
  • [1] Cyber Swarm Algorithms - Improving particle swarm optimization using adaptive memory strategies
    Yin, Peng-Yeng
    Glover, Fred
    Laguna, Manuel
    Zhu, Jia-Xian
    [J]. EUROPEAN JOURNAL OF OPERATIONAL RESEARCH, 2010, 201 (02) : 377 - 389
  • [2] Performance Analysis of Adaptive Beamforming using Particle Swarm Optimization
    Banerjee, Smita
    Dwivedi, Ved Vyas
    [J]. 2016 11TH INTERNATIONAL CONFERENCE ON INDUSTRIAL AND INFORMATION SYSTEMS (ICIIS), 2016, : 242 - 246
  • [3] Comparison of nonuniform optimal quantizer designs for speech coding with adaptive critics and particle swarm
    Venayagamoorthy, Ganesh Kumar
    Zha, Wenwei
    [J]. IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS, 2007, 43 (01) : 238 - 244
  • [4] Improving Financial Returns using Neural Networks and Adaptive Particle Swarm Optimization
    Xiao, Yi
    Xiao, Ming
    Zhao, Fuzhe
    [J]. 2012 FIFTH INTERNATIONAL CONFERENCE ON BUSINESS INTELLIGENCE AND FINANCIAL ENGINEERING (BIFE), 2012, : 15 - 19
  • [5] Improving Adaptive Filters for Active Noise Control Using Particle Swarm Optimization
    Monteiro, Rodrigo P.
    Lima, Gabriel A.
    Oliveira, Jose P. G.
    Cunha, Daniel S. C.
    Bastos-Filho, Carmelo J. A.
    [J]. INTERNATIONAL JOURNAL OF SWARM INTELLIGENCE RESEARCH, 2018, 9 (04) : 47 - 64
  • [6] Particle Swarm Optimization using adaptive mutation
    Pant, Millie
    Thangaraj, Radha
    Abraham, Ajith
    [J]. DEXA 2008: 19TH INTERNATIONAL CONFERENCE ON DATABASE AND EXPERT SYSTEMS APPLICATIONS, PROCEEDINGS, 2008, : 519 - +
  • [7] Improving the performance of Particle Swarm Optimization with Diversive Curiosity
    Zhang, Hong
    Ishikawa, Masumi
    [J]. IMECS 2008: INTERNATIONAL MULTICONFERENCE OF ENGINEERS AND COMPUTER SCIENTISTS, VOLS I AND II, 2008, : 1 - 6
  • [8] A review of parameters for improving the performance of particle swarm optimization
    Computer Science Department, Guru Nanak Dev University, Regional Campus, Jalandhar, India
    [J]. Int. J. Hybrid Inf. Technol., 4 (7-14):
  • [9] An Adaptive Approach to Swarm Surveillance using Particle Swarm Optimization
    Srivastava, Roopak
    Budhraja, Akshit
    Pradhan, Pyari Mohan
    [J]. 2016 INTERNATIONAL CONFERENCE ON ELECTRICAL, ELECTRONICS, AND OPTIMIZATION TECHNIQUES (ICEEOT), 2016, : 3780 - 3783
  • [10] Adaptive Particle Swarm Optimization using velocity information of swarm
    Yasuda, K
    Iwasaki, N
    [J]. 2004 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN & CYBERNETICS, VOLS 1-7, 2004, : 3475 - 3481
12345