A synchronous-asynchronous particle swarm optimisation algorithm

Nor Azlina Ab Aziz, Marizan Mubin, Mohd Saberi Mohamad, Kamarulzaman Ab Aziz

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)

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.

Original languageEnglish
Article number123019
JournalScientific World Journal
Volume2014
DOIs
Publication statusPublished - 2014
Externally publishedYes

ASJC Scopus subject areas

  • General Biochemistry,Genetics and Molecular Biology
  • General Environmental Science

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