Hybrid evolutionary optimization algorithm MPSO-SA
This paper proposes a new method for a modified particle swarm optimization algorithm (MPSO) combined with a simulated annealing algorithm (SA). MPSO is known as an efficient approach with a high performance of solving optimization problems in many research fields. It is a population intelligence al...
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| Format: | Article |
| Language: | English |
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EDP Sciences
2010-01-01
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| Series: | International Journal for Simulation and Multidisciplinary Design Optimization |
| Subjects: | |
| Online Access: | https://www.ijsmdo.org/articles/smdo/pdf/2010/01/smdo2010004.pdf |
| _version_ | 1818917098996367360 |
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| author | El Hami N. Ellaia R. Itmi M. |
| author_facet | El Hami N. Ellaia R. Itmi M. |
| author_sort | El Hami N. |
| collection | DOAJ |
| description | This paper proposes a new method for a modified particle swarm optimization algorithm (MPSO) combined with a simulated annealing algorithm (SA). MPSO is known as an efficient approach with a high performance of solving optimization problems in many research fields. It is a population intelligence algorithm inspired by social behavior simulations of bird flocking. Considerable research work on classical method PSO (Particle Swarm Optimization) has been done to improve the performance of this method. Therefore, the proposed hybrid optimization algorithms MPSO-SA use the combination of MPSO and simulated annealing SA. In this matter, a benchmark of eighteen well-known functions is given. These functions present different situations of finding the global minimum with gradual difficulties. Numerical results presented, in this paper, show the robustness of the MPSO-SA algorithm. Numerical comparisons with these three algorithms : Simulated Annealing, Modified Particle swarm optimization and MPSO-SA prove that the hybrid algorithm offers best results. |
| first_indexed | 2024-12-20T00:28:40Z |
| format | Article |
| id | doaj.art-57227246f35249ca96b8eb949d8775b5 |
| institution | Directory Open Access Journal |
| issn | 1779-627X 1779-6288 |
| language | English |
| last_indexed | 2024-12-20T00:28:40Z |
| publishDate | 2010-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | International Journal for Simulation and Multidisciplinary Design Optimization |
| spelling | doaj.art-57227246f35249ca96b8eb949d8775b52022-12-21T20:00:00ZengEDP SciencesInternational Journal for Simulation and Multidisciplinary Design Optimization1779-627X1779-62882010-01-0141273210.1051/ijsmdo/2010004smdo2010004Hybrid evolutionary optimization algorithm MPSO-SAEl Hami N.Ellaia R.Itmi M.This paper proposes a new method for a modified particle swarm optimization algorithm (MPSO) combined with a simulated annealing algorithm (SA). MPSO is known as an efficient approach with a high performance of solving optimization problems in many research fields. It is a population intelligence algorithm inspired by social behavior simulations of bird flocking. Considerable research work on classical method PSO (Particle Swarm Optimization) has been done to improve the performance of this method. Therefore, the proposed hybrid optimization algorithms MPSO-SA use the combination of MPSO and simulated annealing SA. In this matter, a benchmark of eighteen well-known functions is given. These functions present different situations of finding the global minimum with gradual difficulties. Numerical results presented, in this paper, show the robustness of the MPSO-SA algorithm. Numerical comparisons with these three algorithms : Simulated Annealing, Modified Particle swarm optimization and MPSO-SA prove that the hybrid algorithm offers best results.https://www.ijsmdo.org/articles/smdo/pdf/2010/01/smdo2010004.pdfglobal optimizationpsosaevolutionary algorithmhybrid methods |
| spellingShingle | El Hami N. Ellaia R. Itmi M. Hybrid evolutionary optimization algorithm MPSO-SA International Journal for Simulation and Multidisciplinary Design Optimization global optimization pso sa evolutionary algorithm hybrid methods |
| title | Hybrid evolutionary optimization algorithm MPSO-SA |
| title_full | Hybrid evolutionary optimization algorithm MPSO-SA |
| title_fullStr | Hybrid evolutionary optimization algorithm MPSO-SA |
| title_full_unstemmed | Hybrid evolutionary optimization algorithm MPSO-SA |
| title_short | Hybrid evolutionary optimization algorithm MPSO-SA |
| title_sort | hybrid evolutionary optimization algorithm mpso sa |
| topic | global optimization pso sa evolutionary algorithm hybrid methods |
| url | https://www.ijsmdo.org/articles/smdo/pdf/2010/01/smdo2010004.pdf |
| work_keys_str_mv | AT elhamin hybridevolutionaryoptimizationalgorithmmpsosa AT ellaiar hybridevolutionaryoptimizationalgorithmmpsosa AT itmim hybridevolutionaryoptimizationalgorithmmpsosa |