Hybrid particle swarm optimization algorithm with fine tuning operators
This paper introduces a new approach called hybrid particle swarm optimization like algorithm (hybrid PSO) with fine tuning operators to solve optimisation problems. This method combines the merits of the parameter-free PSO (pf-PSO) and the extrapolated particle swarm optimization like algorithm (eP...
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| Format: | Article |
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2009
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| _version_ | 1825719050405150720 |
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| author | Murthy, G.R. Arumugam, M.S. Loo, C.K. |
| author_facet | Murthy, G.R. Arumugam, M.S. Loo, C.K. |
| author_sort | Murthy, G.R. |
| collection | UM |
| description | This paper introduces a new approach called hybrid particle swarm optimization like algorithm (hybrid PSO) with fine tuning operators to solve optimisation problems. This method combines the merits of the parameter-free PSO (pf-PSO) and the extrapolated particle swarm optimization like algorithm (ePSO). In order to accelerate the PSO algorithms to obtain the global optimal solution, three fine tuning operators, namely mutation, cross-over and root mean square variants are introduced. The effectiveness of the fine tuning elements with various PSO algorithms is tested through three benchmark functions along with a few recently developed state-of-the-art methods and the results are compared with those obtained without the fine tuning elements. From several comparative analyses, it is clearly seen that the performance of all the three PSO algorithms (pf-PSO, ePSO, and hybrid PSO) is considerably improved with various fine tuning operators and sometimes more competitive than the recently developed PSO algorithms. |
| first_indexed | 2024-03-06T05:13:44Z |
| format | Article |
| id | um.eprints-5183 |
| institution | Universiti Malaya |
| last_indexed | 2024-03-06T05:13:44Z |
| publishDate | 2009 |
| record_format | dspace |
| spelling | um.eprints-51832013-03-21T01:51:46Z http://eprints.um.edu.my/5183/ Hybrid particle swarm optimization algorithm with fine tuning operators Murthy, G.R. Arumugam, M.S. Loo, C.K. T Technology (General) This paper introduces a new approach called hybrid particle swarm optimization like algorithm (hybrid PSO) with fine tuning operators to solve optimisation problems. This method combines the merits of the parameter-free PSO (pf-PSO) and the extrapolated particle swarm optimization like algorithm (ePSO). In order to accelerate the PSO algorithms to obtain the global optimal solution, three fine tuning operators, namely mutation, cross-over and root mean square variants are introduced. The effectiveness of the fine tuning elements with various PSO algorithms is tested through three benchmark functions along with a few recently developed state-of-the-art methods and the results are compared with those obtained without the fine tuning elements. From several comparative analyses, it is clearly seen that the performance of all the three PSO algorithms (pf-PSO, ePSO, and hybrid PSO) is considerably improved with various fine tuning operators and sometimes more competitive than the recently developed PSO algorithms. 2009 Article PeerReviewed Murthy, G.R. and Arumugam, M.S. and Loo, C.K. (2009) Hybrid particle swarm optimization algorithm with fine tuning operators. International Journal of Bio-Inspired Computation, 1 (1-2). pp. 14-31. ISSN 1758-0366, http://link.springer.com/article/10.1023%2FB%3AJINT.0000039014.41797.dc?LI=true |
| spellingShingle | T Technology (General) Murthy, G.R. Arumugam, M.S. Loo, C.K. Hybrid particle swarm optimization algorithm with fine tuning operators |
| title | Hybrid particle swarm optimization algorithm with fine tuning operators |
| title_full | Hybrid particle swarm optimization algorithm with fine tuning operators |
| title_fullStr | Hybrid particle swarm optimization algorithm with fine tuning operators |
| title_full_unstemmed | Hybrid particle swarm optimization algorithm with fine tuning operators |
| title_short | Hybrid particle swarm optimization algorithm with fine tuning operators |
| title_sort | hybrid particle swarm optimization algorithm with fine tuning operators |
| topic | T Technology (General) |
| work_keys_str_mv | AT murthygr hybridparticleswarmoptimizationalgorithmwithfinetuningoperators AT arumugamms hybridparticleswarmoptimizationalgorithmwithfinetuningoperators AT loock hybridparticleswarmoptimizationalgorithmwithfinetuningoperators |