Improving comprehensive learning particle swarm optimiser using generalised opposition-based learning
by Wenjun Wang; Hui Wang; Shahryar Rahnamayan
International Journal of Modelling, Identification and Control (IJMIC), Vol. 14, No. 4, 2011

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.

Online publication date: Sat, 21-Mar-2015

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