Differential evolution for high-dimensional function optimization

Zhenyu YANG, Ke TANG, Xin YAO

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Researchpeer-review

204 Citations (Scopus)


Most reported studies on differential evolution (DE) are obtained using low-dimensional problems, e.g., smaller than 100, which are relatively small for many real-world problems. In this paper we propose two new efficient DE variants, named DECC-I and DECC-II, for high-dimensional optimization (up to 1000 dimensions). The two algorithms are based on a cooperative coevolution framework incorporated with several novel strategies. The new strategies are mainly focus on problem decomposition and subcomponents cooperation. Experimental results have shown that these algorithms have superior performance on a set of widely used benchmark functions. © 2007 IEEE.
Original languageEnglish
Title of host publication2007 IEEE Congress on Evolutionary Computation, CEC 2007
Number of pages8
Publication statusPublished - Sept 2007
Externally publishedYes


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