Title: Enhanced differential evolution with generalised opposition-based learning and orientation neighbourhood mining

Authors: Jing Wang

Addresses: School of Software and Communication Engineering, Jiangxi University of Finance and Economics, Nanchang, Jiangxi, 330013, China

Abstract: This paper presents an enhanced differential evolution (DE) algorithm which employs two strategies including generalised opposition-based learning (GOBL) and orientation neighbourhood mining (ONM). The two strategies are very helpful to balance global search ability and local search ability of algorithm. To verify the performance of our approaches, seven famous benchmark functions are utilised. Conducted experiments indicate that the enhanced algorithm performs significantly better than, or at least comparable to, several state-of-the-art DE variants.

Keywords: orientation neighbourhood mining; ONM; differential evolution; opposition-based learning; OBL.

DOI: 10.1504/IJCSM.2015.067541

International Journal of Computing Science and Mathematics, 2015 Vol.6 No.1, pp.49 - 58

Received: 14 Jul 2014
Accepted: 26 Aug 2014

Published online: 19 Feb 2015 *

Full-text access for editors Access for subscribers Purchase this article Comment on this article