Title: Particle swarm optimisation with differential mutation

Authors: Tapas Si; Nanda Dulal Jana

Addresses: Department of Computer Science and Engineering, Bankura Unnayani Institute of Engineering, Bankura, West Bengal, India ' Department of Information Technology, National Institute of Technology, Durgapur, West Bengal, India

Abstract: Particle swarm optimisation (PSO) is population-based optimisation algorithm having stochastic in nature. PSO has quick convergence speed but often gets stuck into local optima due to lacks of diversity. In this work, first mutation operator adopted from Differential Evolution (DE) algorithm is applied in PSO with decreasing inertia weight (PSO-DMLB). In second method, DE mutation is applied in another PSO variant, namely Comprehensive Learning PSO (CLPSO). The second method is termed as CLPSO-DMLB. Local best position of each particle is muted by a predefined mutation probability with the scaled difference of two randomly selected particle's local best position to increase the diversity in the population to achieve better quality of solutions. The proposed methods are applied on well-known benchmark unconstrained functions and obtained results are compared to show the effectiveness of the proposed methods.

Keywords: particle swarm optimisation; PSO; comprehensive learning particle swarm optimiser; differential mutation.

DOI: 10.1504/IJISTA.2012.052501

International Journal of Intelligent Systems Technologies and Applications, 2012 Vol.11 No.3/4, pp.212 - 251

Received: 10 Mar 2012
Accepted: 28 Sep 2012
Published online: 06 Mar 2013 *

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