Welcome to the InfoSci Platform
Reference Hub9
Centroid Opposition-Based Differential Evolution

Centroid Opposition-Based Differential Evolution

Shahryar Rahnamayan, Jude Jesuthasan, Farid Bourennani, Greg F. Naterer, Hojjat Salehinejad
Copyright: © 2014 |Volume: 5 |Issue: 4 |Pages: 25
ISSN: 1947-8283|EISSN: 1947-8291|EISBN13: 9781466652644|DOI: 10.4018/ijamc.2014100101
Cite Article Cite Article

MLA

Rahnamayan, Shahryar, et al. "Centroid Opposition-Based Differential Evolution." IJAMC vol.5, no.4 2014: pp.1-25. http://doi.org/10.4018/ijamc.2014100101

APA

Rahnamayan, S., Jesuthasan, J., Bourennani, F., Naterer, G. F., & Salehinejad, H. (2014). Centroid Opposition-Based Differential Evolution. International Journal of Applied Metaheuristic Computing (IJAMC), 5(4), 1-25. http://doi.org/10.4018/ijamc.2014100101

Chicago

Rahnamayan, Shahryar, et al. "Centroid Opposition-Based Differential Evolution," International Journal of Applied Metaheuristic Computing (IJAMC) 5, no.4: 1-25. http://doi.org/10.4018/ijamc.2014100101

Export Reference

Mendeley
Favorite Full-Issue Download

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.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.