NDLI: Particle swarm optimization algorithm with variable random function

Content Provider	IEEE Xplore Digital Library
Author	Zhou Xiao-Jun Yang Chun-Hua Gui Wei-Hua
Copyright Year	2011
Description	Author affiliation: School of Information Science and Engineering, Central South University, Changsha 410083, China (Zhou Xiao-Jun; Yang Chun-Hua; Gui Wei-Hua)
Abstract	The random function of standard particle swarm optimization algorithm is distributed uniformly on the range of [0, 1], which makes the algorithm become a stochastic search method. To avoid the trap into blindness of stochastic search, research on the distribution of random function is discussed. Unlike the traditional direction of search based on gradient, making the best use of thought of statistics, a new direction is proposed, based on the concept of skewness. Selecting an appropriate random function to adapt to skewness, particle swarm optimization algorithm with variable random function (VRFPSO) is proposed. Using the 4 common Benchmark functions, the experiment results show that it has a better performance in global search capacity and convergence rate. In retrospect, the results approve the significant effect of distribution of random function on particle swarm optimization and research on new direction of search based on statistical information is of great importance.
Starting Page	5408
Ending Page	5412
File Size	434512
Page Count	5
File Format	PDF
ISBN	9781457706776
ISSN	21612927
e-ISBN	9789881725592
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-07-22
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Chinese Assoc of Automati
Subject Keyword	Information science Humans Direction Of Search Blindness Skewness Electronic mail Random Function Particle swarm optimization Particle Swarm Optimization Convergence
Content Type	Text
Resource Type	Article

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