Tackling high dimensional nonseparable optimization problems by cooperatively coevolving particle swarms

Xiaodong LI, Xin YAO

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135 Citations (Scopus)

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 outperformed 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. © 2009 IEEE.
Original languageEnglish
Title of host publication2009 IEEE Congress on Evolutionary Computation, CEC 2009
Pages1546-1553
Number of pages8
DOIs
Publication statusPublished - May 2009
Externally publishedYes

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