NDLI: Tackling high dimensional nonseparable optimization problems by cooperatively coevolving particle swarms

Content Provider	IEEE Xplore Digital Library
Author	Xiaodong Li Xin Yao
Copyright Year	2009
Description	Author affiliation: School of Computer Science and IT, RMIT University, VIC 3001, Melbourne, Australia (Xiaodong Li) \|\| School of Computer Science, The University of Birmingham, Edgbaston, B15 2TT, U.K. (Xin Yao)
Abstract	This paper attempts to address the question of scaling up Particle Swarm Optimization (PSO) algorithms to high dimensional optimization problems. We present a cooperative coevolving PSO (CCPSO) algorithm incorporating random grouping and adaptive weighting, two techniques that have been shown to be effective for handling high dimensional nonseparable problems. The proposed CCPSO algorithms out-performed a previously developed coevolving PSO algorithm on nonseparable functions of 30 dimensions. Furthermore, the scalability of the proposed algorithm to high dimensional nonseparable problems (of up to 1000 dimensions) is examined and compared with two existing coevolving Differential Evolution (DE) algorithms, and new insights are obtained. Our experimental results show the proposed CCPSO algorithms can perform reasonably well with only a small number of evaluations. The results also suggest that both the random grouping and adaptive weighting schemes are viable approaches that can be generalized to other evolutionary optimization methods.
Starting Page	1546
Ending Page	1553
File Size	209788
Page Count	8
File Format	PDF
ISBN	9781424429585
DOI	10.1109/CEC.2009.4983126
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-05-18
Publisher Place	Norway
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Particle swarm optimization Computer science Testing Scalability Performance evaluation Optimization methods Stochastic processes Evolutionary computation Australia
Content Type	Text
Resource Type	Article

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