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The Scientific World Journal
Volume 2014 (2014), Article ID 318063, 7 pages
Research Article

Heterogeneous Differential Evolution for Numerical Optimization

1School of Information Engineering, Nanchang Institute of Technology, Nanchang 330099, China
2School of Business Administration, Nanchang Institute of Technology, Nanchang 330099, China
3Complex System and Computational Intelligent Laboratory, Taiyuan University of Science and Technology, Taiyuan 030024, China
4State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210023, China
5Department of Electrical, Computer and Software Engineering, University of Ontario Institute of Technology, 2000 Simcoe Street North Oshawa, ON, Canada L1H 7K4

Received 28 September 2013; Accepted 23 December 2013; Published 5 February 2014

Academic Editors: G. C. Gini and J. Zhang

Copyright © 2014 Hui Wang 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.


Differential evolution (DE) is a population-based stochastic search algorithm which has shown a good performance in solving many benchmarks and real-world optimization problems. Individuals in the standard DE, and most of its modifications, exhibit the same search characteristics because of the use of the same DE scheme. This paper proposes a simple and effective heterogeneous DE (HDE) to balance exploration and exploitation. In HDE, individuals are allowed to follow different search behaviors randomly selected from a DE scheme pool. Experiments are conducted on a comprehensive set of benchmark functions, including classical problems and shifted large-scale problems. The results show that heterogeneous DE achieves promising performance on a majority of the test problems.