Particle swarm optimisation with differential mutation

Authors:
Tapas Si;Nanda Dulal Jana
Affiliations:
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
Venue:
International Journal of Intelligent Systems Technologies and Applications
Year:
2012

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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.