Title: Top-k elites based oppositional differential evolution

Authors: Jianming Zhang; Weifeng Pan; Jingjing Wu; Jing Wang

Addresses: School of Computer Science and Information Engineering, Zhejiang Gongshang University, Hangzhou 310018, China ' School of Computer Science and Information Engineering, Zhejiang Gongshang University, Hangzhou 310018, China ' School of Computer Science and Information Engineering, Zhejiang Gongshang University, Hangzhou 310018, China ' School of Software and Communication Engineering, Jiangxi University of Finance and Economics, Nanchang 330013, China

Abstract: Opposition-based Differential Evolution (ODE) is a new DE variant with faster convergence speed and more robust search abilities than the classical DE. It utilises the concept of opposition and simultaneously evaluates an estimate and its corresponding opposite estimate. Numerical results have shown that the selection of the symmetry point between the estimate and the opposite estimate affects the performance of ODE variants. To make full use of the information included in the elites, this paper presents a novel DE variant, Top-k elites-based Oppositional Differential Evolution (TEODE), which is based on a new opposition-based learning strategy using the top-k elites (TEOBL) in the current generation and employs similar schemes of ODE for population initialisation and generation jumping with TEOBL. Experiments are conducted on 17 benchmark functions. The results confirm that TEODE outperforms classical DE, ODE and COODE (opposition-based differential evolution using the current optimum).

Keywords: differential evolution; opposition-based learning; function optimisation; top-k elites; convergence speed; search abilities.

DOI: 10.1504/IJWMC.2015.068634

International Journal of Wireless and Mobile Computing, 2015 Vol.8 No.2, pp.166 - 174

Received: 18 Jul 2014
Accepted: 28 Aug 2014

Published online: 28 Mar 2015 *

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