Particle swarm optimisation with differential mutation. (7th March 2013)

• Record Type: Journal Article
• Authors: Si, Tapas; Jana, Nanda Dulal
• Is Part Of: International journal of intelligent systems technologies and applications. Volume 11:Number 3/4(2012)
• Page Start: 212
• Page End: 251
• Publication Date: 2013-03-07
• Subjects: particle swarm optimisation -- PSO -- comprehensive learning particle swarm optimiser -- differential mutation; Artificial intelligence -- Periodicals; Intelligent control systems -- Periodicals; 006.3
• Languages: English
• ISSNs: 1740-8865

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.