Heterogeneous Differential Evolution for Numerical Optimization. (5th February 2014)

• Record Type: Journal Article
• Authors: Wang, Hui; Wang, Wenjun; Cui, Zhihua; Sun, Hui; Rahnamayan, Shahryar
• Other Names: Gini G. C. Academic Editor.; Zhang J. Academic Editor.
• Is Part Of: TheScientificWorldjournal. Volume 2014(2014)
• Publication Date: 2014-02-05
• Subjects: Science -- Periodicals; Technology -- Periodicals; Medicine -- Periodicals; 505
• DOI: 10.1155/2014/318063
• Languages: English
• ISSNs: 2356-6140

Abstract

Abstract : 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.