Authors: Hui Wang; Shahryar Rahnamayan; Sanyou Zeng
Addresses: School of Information Engineering, Nanchang Institute of Technology, Nanchang 330099, China. ' Faculty of Engineering and Applied Science, University of Ontario Institute of Technology (UOIT), 2000 Simcoe Street North, Oshawa, ON L1H 7K4, Canada. ' School of Computer Sciences, China University of Geosciences, Wuhan 430074, China
Abstract: This paper presents an experimental study of generalised opposition-based differential evolution (GODE). A comprehensive set of experiments on 13 benchmark functions is conducted, including opposite points versus random points, diversity analysis, and comparisons of GODE with four other recent variants of differential evolution (DE). Experimental results show that GODE obtains better performance when compared with other involved algorithms.
Keywords: differential evolution; generalised OBL; opposition-based learning; diversity; global optimisation.
International Journal of Computer Applications in Technology, 2012 Vol.43 No.4, pp.311 - 319
Received: 08 May 2021
Accepted: 12 May 2021
Published online: 31 May 2012 *