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Adaptive Parameters for a Modified Comprehensive Learning Particle Swarm Optimizer
Joint Authors
Guan, Qiu
Ling, Haifeng
Zheng, Yujun
Source
Mathematical Problems in Engineering
Issue
Vol. 2012, Issue 2012 (31 Dec. 2012), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2012-12-20
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Abstract EN
Particle swarm optimization (PSO) is a stochastic optimization method sensitive to parameter settings.
The paper presents a modification on the comprehensive learning particle swarm optimizer (CLPSO), which is one of the best performing PSO algorithms.
The proposed method introduces a self-adaptive mechanism that dynamically changes the values of key parameters including inertia weight and acceleration coefficient based on evolutionary information of individual particles and the swarm during the search.
Numerical experiments demonstrate that our approach with adaptive parameters can provide comparable improvement in performance of solving global optimization problems.
American Psychological Association (APA)
Zheng, Yujun& Ling, Haifeng& Guan, Qiu. 2012. Adaptive Parameters for a Modified Comprehensive Learning Particle Swarm Optimizer. Mathematical Problems in Engineering،Vol. 2012, no. 2012, pp.1-11.
https://search.emarefa.net/detail/BIM-1029504
Modern Language Association (MLA)
Zheng, Yujun…[et al.]. Adaptive Parameters for a Modified Comprehensive Learning Particle Swarm Optimizer. Mathematical Problems in Engineering No. 2012 (2012), pp.1-11.
https://search.emarefa.net/detail/BIM-1029504
American Medical Association (AMA)
Zheng, Yujun& Ling, Haifeng& Guan, Qiu. Adaptive Parameters for a Modified Comprehensive Learning Particle Swarm Optimizer. Mathematical Problems in Engineering. 2012. Vol. 2012, no. 2012, pp.1-11.
https://search.emarefa.net/detail/BIM-1029504
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1029504