An effective memetic differential evolution algorithm based on chaotic local search

被引:198
|
作者
Jia, Dongli [1 ,2 ]
Zheng, Guoxin [1 ]
Khan, Muhammad Khurram [3 ]
机构
[1] Shanghai Univ, Key Lab Special Fiber Opt & Opt Access Networks, Shanghai 200072, Peoples R China
[2] Hebei Univ Engn, Sch Informat & Elect Engn, Handan 056038, Peoples R China
[3] King Saud Univ, CoEIA, Riyadh 11653, Saudi Arabia
关键词
GENETIC ALGORITHM; OPTIMIZATION;
D O I
10.1016/j.ins.2011.03.018
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
This paper proposes an effective memetic differential evolution (DE) algorithm, or DECLS, that utilizes a chaotic local search (CLS) with a 'shrinking' strategy. The CLS helps to improve the optimizing performance of the canonical DE by exploring a huge search space in the early run phase to avoid premature convergence, and exploiting a small region in the later run phase to refine the final solutions. Moreover, the parameter settings of the DECLS are controlled in an adaptive manner to further enhance the search ability. To evaluate the effectiveness and efficiency of the proposed DECLS algorithm, we compared it with four state-of-the-art DE variants and the IPOP-CMA-ES algorithm on a set of 20 selected benchmark functions. Results show that the DECLS is significantly better than, or at least comparable to, the other optimizers in terms of convergence performance and solution accuracy. Besides, the DECLS has also shown certain advantages in solving high dimensional problems. (C) 2011 Elsevier Inc. All rights reserved.
引用
收藏
页码:3175 / 3187
页数:13
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