Title: Hybrid particle swarm optimization algorithm with fine tuning operators

Authors: G. Ramana Murthy, M. Senthil Arumugam, C.K. Loo

Addresses: Faculty of Engineering and Technology, Multimedia University, 75450 Malacca, Malaysia. ' Faculty of Engineering and Technology, Multimedia University, 75450 Malacca, Malaysia. ' Faculty of Engineering and Technology, Multimedia University, 75450 Malacca, Malaysia

Abstract: This paper introduces a new approach called hybrid particle swarm optimisation like algorithm (hybrid PSO) with fine tuning operators to solve optimisation problems. This method combines the merits of the parameter-free PSO (pf-PSO) and the extrapolated particle swarm optimisation like algorithm (ePSO). In order to accelerate the PSO algorithms to obtain the global optimal solution, three fine tuning operators, namely mutation, cross-over and root mean square variants are introduced. The effectiveness of the fine tuning elements with various PSO algorithms is tested through three benchmark functions along with a few recently developed state-of-the-art methods and the results are compared with those obtained without the fine tuning elements. From several comparative analyses, it is clearly seen that the performance of all the three PSO algorithms (pf-PSO, ePSO, and hybrid PSO) is considerably improved with various fine tuning operators and sometimes more competitive than the recently developed PSO algorithms.

Keywords: particle swarm optimisation; hybrid PSO; benchmark problems; inertia weight; acceleration coefficient; mutation operators; cross-over operators; RMS variants; fine tuning operators; root mean square; RMS variants; bio-inspired computation.

DOI: 10.1504/IJBIC.2009.022771

International Journal of Bio-Inspired Computation, 2009 Vol.1 No.1/2, pp.14 - 31

Published online: 26 Jan 2009 *

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