Improving particle swarm optimization via adaptive switching asynchronous – synchronous update
Particle swarm optimization (PSO) is a population-based metaheuristic optimization algorithm that solves a problem through iterative operations. Traditional PSO iteration strategies can be categorized into two groups: synchronous (S-PSO) or asynchronous (A-PSO) update. In S-PSO, the performance of t...
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| Format: | Article |
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Elsevier
2018
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| _version_ | 1796961693307240448 |
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| author | Ab. Aziz, Nor Azlina Ibrahim, Zuwairie Mubin, Marizan Nawawi, Sophan Wahyudi Mohamad, Mohd Saberi |
| author_facet | Ab. Aziz, Nor Azlina Ibrahim, Zuwairie Mubin, Marizan Nawawi, Sophan Wahyudi Mohamad, Mohd Saberi |
| author_sort | Ab. Aziz, Nor Azlina |
| collection | UM |
| description | Particle swarm optimization (PSO) is a population-based metaheuristic optimization algorithm that solves a problem through iterative operations. Traditional PSO iteration strategies can be categorized into two groups: synchronous (S-PSO) or asynchronous (A-PSO) update. In S-PSO, the performance of the entire swarm is evaluated before the particles’ velocities and positions are updated, whereas in A-PSO, each particle's velocity and position are updated immediately after an individual's performance is evaluated. Previous research claimed that S-PSO is better in exploitation and has fast convergence, whereas A-PSO converges at a slower rate and is stronger at exploration. Exploration and exploitation are important in ensuring good performance for any population-based metaheuristic. In this paper, an adaptive switching PSO (Switch-PSO) algorithm that uses a hybrid update sequence is proposed. The iteration strategy in Switch-PSO is adaptively switched between the two traditional iteration strategies according to the performance of the swarm's best member. The performance of Switch-PSO is compared with existing S-PSO, A-PSO and three state-of-the-art PSO algorithms using CEC2014's benchmark functions. The results show that Switch-PSO achieves superior performance in comparison to the other algorithms. Switch-PSO is then applied for infinite impulse response model identification, where Switch-PSO is found to rank the best among all the algorithms applied. |
| first_indexed | 2024-03-06T05:57:55Z |
| format | Article |
| id | um.eprints-22763 |
| institution | Universiti Malaya |
| last_indexed | 2024-03-06T05:57:55Z |
| publishDate | 2018 |
| publisher | Elsevier |
| record_format | dspace |
| spelling | um.eprints-227632019-10-21T01:51:20Z http://eprints.um.edu.my/22763/ Improving particle swarm optimization via adaptive switching asynchronous – synchronous update Ab. Aziz, Nor Azlina Ibrahim, Zuwairie Mubin, Marizan Nawawi, Sophan Wahyudi Mohamad, Mohd Saberi TK Electrical engineering. Electronics Nuclear engineering Particle swarm optimization (PSO) is a population-based metaheuristic optimization algorithm that solves a problem through iterative operations. Traditional PSO iteration strategies can be categorized into two groups: synchronous (S-PSO) or asynchronous (A-PSO) update. In S-PSO, the performance of the entire swarm is evaluated before the particles’ velocities and positions are updated, whereas in A-PSO, each particle's velocity and position are updated immediately after an individual's performance is evaluated. Previous research claimed that S-PSO is better in exploitation and has fast convergence, whereas A-PSO converges at a slower rate and is stronger at exploration. Exploration and exploitation are important in ensuring good performance for any population-based metaheuristic. In this paper, an adaptive switching PSO (Switch-PSO) algorithm that uses a hybrid update sequence is proposed. The iteration strategy in Switch-PSO is adaptively switched between the two traditional iteration strategies according to the performance of the swarm's best member. The performance of Switch-PSO is compared with existing S-PSO, A-PSO and three state-of-the-art PSO algorithms using CEC2014's benchmark functions. The results show that Switch-PSO achieves superior performance in comparison to the other algorithms. Switch-PSO is then applied for infinite impulse response model identification, where Switch-PSO is found to rank the best among all the algorithms applied. Elsevier 2018 Article PeerReviewed Ab. Aziz, Nor Azlina and Ibrahim, Zuwairie and Mubin, Marizan and Nawawi, Sophan Wahyudi and Mohamad, Mohd Saberi (2018) Improving particle swarm optimization via adaptive switching asynchronous – synchronous update. Applied Soft Computing, 72. pp. 298-311. ISSN 1568-4946, DOI https://doi.org/10.1016/j.asoc.2018.07.047 <https://doi.org/10.1016/j.asoc.2018.07.047>. https://doi.org/10.1016/j.asoc.2018.07.047 doi:10.1016/j.asoc.2018.07.047 |
| spellingShingle | TK Electrical engineering. Electronics Nuclear engineering Ab. Aziz, Nor Azlina Ibrahim, Zuwairie Mubin, Marizan Nawawi, Sophan Wahyudi Mohamad, Mohd Saberi Improving particle swarm optimization via adaptive switching asynchronous – synchronous update |
| title | Improving particle swarm optimization via adaptive switching asynchronous – synchronous update |
| title_full | Improving particle swarm optimization via adaptive switching asynchronous – synchronous update |
| title_fullStr | Improving particle swarm optimization via adaptive switching asynchronous – synchronous update |
| title_full_unstemmed | Improving particle swarm optimization via adaptive switching asynchronous – synchronous update |
| title_short | Improving particle swarm optimization via adaptive switching asynchronous – synchronous update |
| title_sort | improving particle swarm optimization via adaptive switching asynchronous synchronous update |
| topic | TK Electrical engineering. Electronics Nuclear engineering |
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