NDLI: Improvement of original particle swarm optimization algorithm based on simulated annealing algorithm

Content Provider	IEEE Xplore Digital Library
Author	Cong Liang Hu Chengquan Guo Zongpeng Jiang Yu Sha Lihua
Copyright Year	2008
Description	Author affiliation: Coll. of Comput. Sci. & Technol., Jilin Univ., Changchun (Cong Liang; Hu Chengquan; Guo Zongpeng; Jiang Yu; Sha Lihua)
Abstract	Particle swarm optimization (PSO) algorithm is an optimization algorithm in the field of evolutionary computation, which has been applied widely in function optimization, artificial neural networkspsila training, pattern recognition, fuzzy control and some other fields. Original PSO algorithm could be trapped in the local optimum easily, so in this paper we improved the original PSO algorithm using the idea of simulated annealing algorithm, which makes the PSO algorithm jump out of local optimum. In this paper, two improved strategies was proposed, and after testing and comparing the two improved algorithms with the original PSO algorithm again and again, we conclude at last that efficiency of global searching of the two improved strategies is better than the original PSO.
Starting Page	671
Ending Page	676
File Size	235295
Page Count	6
File Format	PDF
ISBN	9787900719706
DOI	10.1109/CHICC.2008.4605337
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-07-16
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute Of Intelligence M&C Technical Of KMUST
Subject Keyword	Algorithm design and analysis Computational modeling Local optimum Simulated annealing Artificial neural networks Electronic mail Particle swarm optimization Optimization
Content Type	Text
Resource Type	Article

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