Differential Evolution With Composite Trial Vector Generation Strategies and Control Parameters

Authors:
Yong Wang;Zixing Cai;Qingfu Zhang
Affiliations:
Sch. of Inf. Sci. & Eng., Central South Univ., Changsha, China;-;-
Venue:
IEEE Transactions on Evolutionary Computation
Year:
2011

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Abstract

Trial vector generation strategies and control parameters have a significant influence on the performance of differential evolution (DE). This paper studies whether the performance of DE can be improved by combining several effective trial vector generation strategies with some suitable control parameter settings. A novel method, called composite DE (CoDE), has been proposed in this paper. This method uses three trial vector generation strategies and three control parameter settings. It randomly combines them to generate trial vectors. CoDE has been tested on all the CEC2005 contest test instances. Experimental results show that CoDE is very competitive.