Table of Contents Author Guidelines Submit a Manuscript
The Scientific World Journal
Volume 2014, Article ID 123019, 17 pages
http://dx.doi.org/10.1155/2014/123019
Research Article

A Synchronous-Asynchronous Particle Swarm Optimisation Algorithm

1Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia
2Multimedia University, Jalan Ayer Keroh Lama, 75450 Bukit Beruang, Melaka, Malaysia
3Faculty of Computing, Universiti Teknologi Malaysia, 81310 Johor Bahru, Malaysia

Received 23 April 2014; Accepted 20 June 2014; Published 10 July 2014

Academic Editor: T. O. Ting

Copyright © 2014 Nor Azlina Ab Aziz et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

In the original particle swarm optimisation (PSO) algorithm, the particles’ velocities and positions are updated after the whole swarm performance is evaluated. This algorithm is also known as synchronous PSO (S-PSO). The strength of this update method is in the exploitation of the information. Asynchronous update PSO (A-PSO) has been proposed as an alternative to S-PSO. A particle in A-PSO updates its velocity and position as soon as its own performance has been evaluated. Hence, particles are updated using partial information, leading to stronger exploration. In this paper, we attempt to improve PSO by merging both update methods to utilise the strengths of both methods. The proposed synchronous-asynchronous PSO (SA-PSO) algorithm divides the particles into smaller groups. The best member of a group and the swarm’s best are chosen to lead the search. Members within a group are updated synchronously, while the groups themselves are asynchronously updated. Five well-known unimodal functions, four multimodal functions, and a real world optimisation problem are used to study the performance of SA-PSO, which is compared with the performances of S-PSO and A-PSO. The results are statistically analysed and show that the proposed SA-PSO has performed consistently well.