Centroid Opposition-Based Differential Evolution

Shahryar Rahnamayan, Jude Jesuthasan, Farid Bourennani, Greg F. Naterer and Hojjat Salehinejad
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Shahryar Rahnamayan: Electrical, Computer, and Software Engineering Department, University of Ontario Institute of Technology, Oshawa, Ontario, Canada
Jude Jesuthasan: Electrical and Computer Engineering Department, University of Waterloo, Waterloo, Ontario, Canada
Farid Bourennani: Electrical, Computer, and Software Engineering Department, University of Ontario Institute of Technology, Oshawa, Ontario, Canada
Greg F. Naterer: Faculty of Engineering and Applied Science, Memorial University of Newfoundland, St. John's, Canada
Hojjat Salehinejad: Electrical, Computer, and Software Engineering Department, University of Ontario Institute of Technology, Oshawa, Ontario, Canada

International Journal of Applied Metaheuristic Computing (IJAMC), 2014, vol. 5, issue 4, 1-25

Abstract: The capabilities of evolutionary algorithms (EAs) in solving nonlinear and non-convex optimization problems are significant. Differential evolution (DE) is an effective population-based EA, which has emerged as very competitive. Since its inception in 1995, multiple variants of DE have been proposed with higher performance. Among these DE variants, opposition-based differential evolution (ODE) established a novel concept in which individuals must compete with theirs opposites in order to make an entry in the next generation. The generation of opposite points is based on the current extreme points (i.e., maximum and minimum) in the search space. This paper develops a new scheme that utilizes the centroid point of a population to calculate opposite individuals. The classical scheme of an opposite point is modified. Incorporating this new scheme into DE leads to an enhanced ODE that is identified as centroid opposition-based differential evolution (CODE). The accuracy of the CODE algorithm is comprehensively evaluated on well-known complex benchmark functions and compared with the performance of conventional DE, ODE, and other state-of-the-art algorithms. The results for CODE are found to be promising.

Date: 2014
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