Polynomial modeling in dynamic environment based on a particle swarm optimization
In this chapter, a particle swarm optimization (PSO) is proposed for polynomial modeling in a dynamic environment. The basic operations of the proposed PSO are identical to the ones of the original PSO except that elements of particles represent arithmetic operations and polynomial variables of poly...
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| Other Authors: | |
| Format: | Book Chapter |
| Published: |
World Scientific
2012
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| Subjects: | |
| Online Access: | http://hdl.handle.net/20.500.11937/44754 |
| _version_ | 1848757092113973248 |
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| author | Chan, Kit Yan Dillon, T. |
| author2 | H.K. Lam |
| author_facet | H.K. Lam Chan, Kit Yan Dillon, T. |
| author_sort | Chan, Kit Yan |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | In this chapter, a particle swarm optimization (PSO) is proposed for polynomial modeling in a dynamic environment. The basic operations of the proposed PSO are identical to the ones of the original PSO except that elements of particles represent arithmetic operations and polynomial variables of polynomial models. The performance of the proposed PSO is evaluated by polynomial modeling based on a set of dynamic benchmark functions in which their optima are dynamically moved. Results show that the proposed PSO can find significantly better polynomial models than genetic programming (GP) which is a commonly used method for polynomial modeling. |
| first_indexed | 2025-11-14T09:22:35Z |
| format | Book Chapter |
| id | curtin-20.500.11937-44754 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T09:22:35Z |
| publishDate | 2012 |
| publisher | World Scientific |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-447542023-02-07T08:01:22Z Polynomial modeling in dynamic environment based on a particle swarm optimization Chan, Kit Yan Dillon, T. H.K. Lam S.H. Ling H.T. Nguyen particle swarm optimization evolutionary computation polynomial modeling time-varying modeling time-varying systems In this chapter, a particle swarm optimization (PSO) is proposed for polynomial modeling in a dynamic environment. The basic operations of the proposed PSO are identical to the ones of the original PSO except that elements of particles represent arithmetic operations and polynomial variables of polynomial models. The performance of the proposed PSO is evaluated by polynomial modeling based on a set of dynamic benchmark functions in which their optima are dynamically moved. Results show that the proposed PSO can find significantly better polynomial models than genetic programming (GP) which is a commonly used method for polynomial modeling. 2012 Book Chapter http://hdl.handle.net/20.500.11937/44754 World Scientific restricted |
| spellingShingle | particle swarm optimization evolutionary computation polynomial modeling time-varying modeling time-varying systems Chan, Kit Yan Dillon, T. Polynomial modeling in dynamic environment based on a particle swarm optimization |
| title | Polynomial modeling in dynamic environment based on a particle swarm optimization |
| title_full | Polynomial modeling in dynamic environment based on a particle swarm optimization |
| title_fullStr | Polynomial modeling in dynamic environment based on a particle swarm optimization |
| title_full_unstemmed | Polynomial modeling in dynamic environment based on a particle swarm optimization |
| title_short | Polynomial modeling in dynamic environment based on a particle swarm optimization |
| title_sort | polynomial modeling in dynamic environment based on a particle swarm optimization |
| topic | particle swarm optimization evolutionary computation polynomial modeling time-varying modeling time-varying systems |
| url | http://hdl.handle.net/20.500.11937/44754 |