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Computational Intelligence and Neuroscience
Volume 2017 (2017), Article ID 1395025, 12 pages
https://doi.org/10.1155/2017/1395025
Research Article

Memetic Differential Evolution with an Improved Contraction Criterion

1School of Computer Science, China University of Geosciences, No. 388 Lumo Road, Hongshan District, Wuhan, China
2Hubei Key Laboratory of Intelligent Geo-Information Processing, China University of Geosciences, Wuhan 430074, China

Correspondence should be addressed to Lei Peng; nc.ude.guc@gnep.iel

Received 1 November 2016; Revised 4 March 2017; Accepted 13 March 2017; Published 4 April 2017

Academic Editor: Manuel Graña

Copyright © 2017 Lei Peng 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

Memetic algorithms with an appropriate trade-off between the exploration and exploitation can obtain very good results in continuous optimization. In this paper, we present an improved memetic differential evolution algorithm for solving global optimization problems. The proposed approach, called memetic DE (MDE), hybridizes differential evolution (DE) with a local search (LS) operator and periodic reinitialization to balance the exploration and exploitation. A new contraction criterion, which is based on the improved maximum distance in objective space, is proposed to decide when the local search starts. The proposed algorithm is compared with six well-known evolutionary algorithms on twenty-one benchmark functions, and the experimental results are analyzed with two kinds of nonparametric statistical tests. Moreover, sensitivity analyses for parameters in MDE are also made. Experimental results have demonstrated the competitive performance of the proposed method with respect to the six compared algorithms.