Differential evolution with improved mutation strategy

Authors:
Shuzhen Wan;Shengwu Xiong;Jialiang Kou;Yi Liu
Affiliations:
School of Computer Science and Technology, Wuhan University of Technology, Wuhan, China;School of Computer Science and Technology, Wuhan University of Technology, Wuhan, China;School of Computer Science and Technology, Wuhan University of Technology, Wuhan, China;School of Computer Science and Technology, Wuhan University of Technology, Wuhan, China
Venue:
ICSI'11 Proceedings of the Second international conference on Advances in swarm intelligence - Volume Part I
Year:
2011

Citing 7
Cited 0

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Abstract

Differential evolution is a powerful evolution algorithm for optimization of real valued and multimodal functions. To accelerate its convergence rate and enhance its performance, this paper introduces a top-p-best trigonometric mutation strategy and a self-adaptation method for controlling the crossover rate (CR). The performance of the proposed algorithm is investigated on a comprehensive set of 13 benchmark functions. Numerical results and statistical analysis show that the proposed algorithm boosts the convergence rate yet maintaining the robustness of the DE algorithm.