A population-based hybrid extremal optimization algorithm

Authors:
Yu Chen;Kai Zhang;Xiufen Zou
Affiliations:
School of Mathematics and Statistics, Wuhan University, Wuhan, China;School of Mathematics and Statistics, Wuhan University, Wuhan, China;School of Mathematics and Statistics, Wuhan University, Wuhan, China
Venue:
ICIC'11 Proceedings of the 7th international conference on Intelligent Computing: bio-inspired computing and applications
Year:
2011

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Abstract

The extremal optimization (EO) algorithm is a kind of evolutionary algorithm which has been applied successfully in combinatorial optimization, while its application on continuous optimization encounters the problems of heavy complexity and weak exploration ability. This paper proposes a new hybrid population-based EO algorithm, named as the adaptive co-evolution population-based extremal optimization (ACPEO) algorithm, in which all individuals co-evolve adaptively with each other and the differential evolution (DE) operator is incorporated to improve the global search ability. By employing a novel evaluation method of variables, the ACPEO algorithm performs well on several kind of benchmark problems. Experimental results show that the ACPEO algorithm is robust due to the capability for solving different problems with the same parameter setting, and it is also stable because changes in the parameters' values do not influence its performances seriously.