Title: Improving comprehensive learning particle swarm optimiser using generalised opposition-based learning

Authors: Wenjun Wang; Hui Wang; Shahryar Rahnamayan

Addresses: School of Business Administration, Nanchang Institute of Technology, Nanchang 330099, China. ' 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

Abstract: In this paper, we present an improved comprehensive learning particle swarm optimiser (CLPSO) by using a generalised opposition-based learning concept (GOBL). The proposed approach, called GOCLPSO, employs similar schemes of opposition-based differential evolution (ODE) for opposition-based population initialisation and generation jumping with GOBL. Experimental studies on 13 benchmark functions show that GOCLPSO could achieve more accurate solutions than CLPSO for the majority of test cases.

Keywords: particle swarm optimisation; PSO; opposition-based differential evolution; opposition-based learning; OBL; generalised opposition-based learning; GOBL; global optimisation.

DOI: 10.1504/IJMIC.2011.043155

International Journal of Modelling, Identification and Control, 2011 Vol.14 No.4, pp.310 - 316

Published online: 21 Mar 2015 *

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