A Synchronous-Asynchronous Particle Swarm Optimisation Algorithm
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. Asyn...
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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2014/123019 |
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| author | Nor Azlina Ab Aziz Marizan Mubin Mohd Saberi Mohamad Kamarulzaman Ab Aziz |
| author_facet | Nor Azlina Ab Aziz Marizan Mubin Mohd Saberi Mohamad Kamarulzaman Ab Aziz |
| author_sort | Nor Azlina Ab Aziz |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-9a9bb82e7ab447dfa38c93cdf726c20a |
| institution | Kabale University |
| issn | 2356-6140 1537-744X |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-9a9bb82e7ab447dfa38c93cdf726c20a2025-02-03T06:07:20ZengWileyThe Scientific World Journal2356-61401537-744X2014-01-01201410.1155/2014/123019123019A Synchronous-Asynchronous Particle Swarm Optimisation AlgorithmNor Azlina Ab Aziz0Marizan Mubin1Mohd Saberi Mohamad2Kamarulzaman Ab Aziz3Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, MalaysiaFaculty of Engineering, University of Malaya, 50603 Kuala Lumpur, MalaysiaFaculty of Computing, Universiti Teknologi Malaysia, 81310 Johor Bahru, MalaysiaMultimedia University, Jalan Ayer Keroh Lama, 75450 Bukit Beruang, Melaka, MalaysiaIn 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.http://dx.doi.org/10.1155/2014/123019 |
| spellingShingle | Nor Azlina Ab Aziz Marizan Mubin Mohd Saberi Mohamad Kamarulzaman Ab Aziz A Synchronous-Asynchronous Particle Swarm Optimisation Algorithm The Scientific World Journal |
| title | A Synchronous-Asynchronous Particle Swarm Optimisation Algorithm |
| title_full | A Synchronous-Asynchronous Particle Swarm Optimisation Algorithm |
| title_fullStr | A Synchronous-Asynchronous Particle Swarm Optimisation Algorithm |
| title_full_unstemmed | A Synchronous-Asynchronous Particle Swarm Optimisation Algorithm |
| title_short | A Synchronous-Asynchronous Particle Swarm Optimisation Algorithm |
| title_sort | synchronous asynchronous particle swarm optimisation algorithm |
| url | http://dx.doi.org/10.1155/2014/123019 |
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