Polynomial modeling for time-varying systems based on a particle swarm optimization algorithm
In this paper, an effective particle swarm optimization (PSO) is proposed for polynomial models for time varying systems. The basic operations of the proposed PSO are similar to those of the classical PSO except that elements of particles represent arithmetic operations and variables of time-varying...
| Main Authors: | , , |
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| Format: | Journal Article |
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Elsevier Inc
2011
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| Online Access: | http://hdl.handle.net/20.500.11937/28194 |
| _version_ | 1848752470623256576 |
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| author | Chan, Kit Yan Dillon, Tharam Kwong, C. |
| author_facet | Chan, Kit Yan Dillon, Tharam Kwong, C. |
| author_sort | Chan, Kit Yan |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | In this paper, an effective particle swarm optimization (PSO) is proposed for polynomial models for time varying systems. The basic operations of the proposed PSO are similar to those of the classical PSO except that elements of particles represent arithmetic operations and variables of time-varying models. The performance of the proposed PSO is evaluated by polynomial modeling based on various sets of time-invariant and time-varying data. Results of polynomial modeling in time-varying systems show that the proposed PSO outperforms commonly used modeling methods which have been developed for solving dynamic optimization problems including genetic programming (GP) and dynamic GP. An analysis of the diversity of individuals of populations in the proposed PSO and GP reveals why the proposed PSO obtains better results than those obtained by GP. |
| first_indexed | 2025-11-14T08:09:08Z |
| format | Journal Article |
| id | curtin-20.500.11937-28194 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T08:09:08Z |
| publishDate | 2011 |
| publisher | Elsevier Inc |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-281942019-02-19T04:27:29Z Polynomial modeling for time-varying systems based on a particle swarm optimization algorithm Chan, Kit Yan Dillon, Tharam Kwong, C. polynomial modeling genetic programming time-varying systems Particle swarm optimization In this paper, an effective particle swarm optimization (PSO) is proposed for polynomial models for time varying systems. The basic operations of the proposed PSO are similar to those of the classical PSO except that elements of particles represent arithmetic operations and variables of time-varying models. The performance of the proposed PSO is evaluated by polynomial modeling based on various sets of time-invariant and time-varying data. Results of polynomial modeling in time-varying systems show that the proposed PSO outperforms commonly used modeling methods which have been developed for solving dynamic optimization problems including genetic programming (GP) and dynamic GP. An analysis of the diversity of individuals of populations in the proposed PSO and GP reveals why the proposed PSO obtains better results than those obtained by GP. 2011 Journal Article http://hdl.handle.net/20.500.11937/28194 10.1016/j.ins.2011.01.006 Elsevier Inc fulltext |
| spellingShingle | polynomial modeling genetic programming time-varying systems Particle swarm optimization Chan, Kit Yan Dillon, Tharam Kwong, C. Polynomial modeling for time-varying systems based on a particle swarm optimization algorithm |
| title | Polynomial modeling for time-varying systems based on a particle swarm optimization algorithm |
| title_full | Polynomial modeling for time-varying systems based on a particle swarm optimization algorithm |
| title_fullStr | Polynomial modeling for time-varying systems based on a particle swarm optimization algorithm |
| title_full_unstemmed | Polynomial modeling for time-varying systems based on a particle swarm optimization algorithm |
| title_short | Polynomial modeling for time-varying systems based on a particle swarm optimization algorithm |
| title_sort | polynomial modeling for time-varying systems based on a particle swarm optimization algorithm |
| topic | polynomial modeling genetic programming time-varying systems Particle swarm optimization |
| url | http://hdl.handle.net/20.500.11937/28194 |