NDLI: An Improved Particle Swarm Optimization with Mutation Based on Similarity

Content Provider	IEEE Xplore Digital Library
Author	Jianhua Liu Xiaoping Fan Zhihua Qu
Copyright Year	2007
Description	Author affiliation: Central South Univ., Changsha (Jianhua Liu; Xiaoping Fan; Zhihua Qu)
Abstract	Particle swarm optimization (PSO) is a new population-based intelligence algorithm and exhibits good performance on optimization. However, during the running of the algorithm, the particles become more and more similar, and cluster into the best particle in the swarm, which make the swarm premature convergence around the local solution. In this paper, a new conception, collectivity, is proposed which is based on similarity between the particle and the current global best particle in the swarm. And the collectivity was used to randomly mutate the position of the particles, which make swarm keep diversity in the search space. Experiments on benchmark functions show that the new algorithm outperforms the basic PSO and some other improved PSO.
Starting Page	824
Ending Page	828
File Size	266401
Page Count	5
File Format	PDF
ISBN	9780769528755
DOI	10.1109/ICNC.2007.223
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-08-24
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Computer science Information science Genetic mutations Clustering algorithms Evolutionary computation Educational institutions Birds Mathematics Particle swarm optimization Convergence
Content Type	Text
Resource Type	Article

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