An improved differential evolution algorithm based on adaptive parameter

Authors:
Zhehuang Huang;Yidong Chen
Affiliations:
School of Mathematical Sciences, Huaqiao University, Quanzhou and Cognitive Science Department, Xiamen University, Xiamen, China;Cognitive Science Department, Xiamen University and Fujian Key Laboratory of the Brain-Like Intelligent Systems, Xiamen, China
Venue:
Journal of Control Science and Engineering - Special issue on Advances in Methods for Networked and Cyber-Physical System
Year:
2013

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Abstract

The differential evolution (DE) algorithm is a heuristic global optimization technique based on population which is easy to understand, simple to implement, reliable, and fast. The evolutionary parameters directly influence the performance of differential evolution algorithm. The adjustment of control parameters is a global behavior and has no general research theory to control the parameters in the evolution process at present. In this paper, we propose an adaptive parameter adjustment method which can dynamically adjust control parameters according to the evolution stage. The experiments on high dimensional function optimization showed that the improved algorithm has more powerful global exploration ability and faster convergence speed.