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Computational Intelligence and Neuroscience
Volume 2015, Article ID 583759, 11 pages
http://dx.doi.org/10.1155/2015/583759
Research Article

An Enhanced Differential Evolution with Elite Chaotic Local Search

1Institute of Medical Informatics and Engineering, School of Science, Jiangxi University of Science and Technology, Ganzhou 341000, China
2School of Literature and Law, Jiangxi University of Science and Technology, Ganzhou 341000, China
3School of Information Science and Technology, Jiujiang University, Jiujiang 332005, China
4State Key Laboratory of Software Engineering, Wuhan University, Wuhan 430072, China

Received 8 October 2014; Accepted 27 April 2015

Academic Editor: Rafik Aliyev

Copyright © 2015 Zhaolu Guo et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Differential evolution (DE) is a simple yet efficient evolutionary algorithm for real-world engineering problems. However, its search ability should be further enhanced to obtain better solutions when DE is applied to solve complex optimization problems. This paper presents an enhanced differential evolution with elite chaotic local search (DEECL). In DEECL, it utilizes a chaotic search strategy based on the heuristic information from the elite individuals to promote the exploitation power. Moreover, DEECL employs a simple and effective parameter adaptation mechanism to enhance the robustness. Experiments are conducted on a set of classical test functions. The experimental results show that DEECL is very competitive on the majority of the test functions.