%0 Journal Article
%T An Improved Differential Evolution Algorithm Based on Adaptive Parameter
%A Zhehuang Huang
%A Yidong Chen
%J Journal of Control Science and Engineering
%D 2013
%I Hindawi Publishing Corporation
%R 10.1155/2013/462706
%X 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. 1. Introduction In recent years, intelligent optimization algorithms [1] are considered as practical tools for nonlinear optimization problems. Differential evolution algorithm [2, 3] is a novel evolutionary algorithm on the basis of genetic algorithms first introduced by Storn and Price in 1997. The algorithm is a bionic intelligent algorithm by simulation of natural biological evolution mechanism. Its main idea is to generate a temporary individual based on individual differences within populations and then randomly restructure population evolutionary. The algorithm has better global convergence and robustness, very suitable for solving a variety of numerical optimization problems, quickly making the algorithm a hot topic in the current optimization field. Because it is simple in principle and robust, DE has been applied successfully to all kinds of optimization problems such as constrained global optimization [4], image classification [5], neural network [6], linear array [7], monopoles antenna [8], images segmentation [9], and other areas [10¨C14]. However, DE algorithm can easily fall into local optimal solution in the course of the treatment of the multipeak and the large search space function optimization problems. In order to improve the optimization performance of the DE, many scholars have proposed many control parameters methods [15, 16]. Although all the methods can improve the standard DE performance to some extent, they still cannot get satisfactory results for some of the functions. In this paper, we propose an adaptive parameter adjustment method according to the evolution stage. This paper is organized as follows. Related work is described in Section 2. In Section 3 the background of DE is presented. The improved algorithm is presented in Section 4. In Section 5 some experimental tests, results, and
%U http://www.hindawi.com/journals/jcse/2013/462706/