On the improved performances of the particle swarm optimization algorithms with adaptive parameters, cross-over operators and root mean square (RMS) variants for computing optimal control of a class of hybrid systems
This paper deals with the concept of including the popular genetic algorithm operator, cross-over and root mean square (RMS) variants into particle swarm optimization (PSO) algorithm to make the convergence faster. Two different PSO algorithms are considered in this paper: the first one is the conve...
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| Format: | Article |
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ELSEVIER SCIENCE BV
2008
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| Online Access: | http://shdl.mmu.edu.my/2779/ |
| _version_ | 1848790147582132224 |
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| author | Arumugam, M. Senthil Rao, M.V.C. |
| author_facet | Arumugam, M. Senthil Rao, M.V.C. |
| author_sort | Arumugam, M. Senthil |
| building | MMU Institutional Repository |
| collection | Online Access |
| description | This paper deals with the concept of including the popular genetic algorithm operator, cross-over and root mean square (RMS) variants into particle swarm optimization (PSO) algorithm to make the convergence faster. Two different PSO algorithms are considered in this paper: the first one is the conventional PSO (cPSO) and the second is the global-local best values based PSO (GLbest-PSO). The GLbest-PSO includes global-local best inertia weight (GLbestIW) with global-local best acceleration coefficient (GLbestAC), whereas the cPSO has a time varying inertia weight (TVIW) and either time varying acceleration coefficient (TVAC) or fixed AC (FAC). The effectiveness of the cross-over operator with both PSO algorithms is tested through a constrained optimal control problem of a class of hybrid systems. The experimental results illustrate the advantage of PSO with cross-over operator, which sharpens the convergence and tunes to the best solution. In order to compare and verify the validity and effectiveness of the new approaches for PSO, several statistical analyses are carried out. The results clearly demonstrate that the GLbest-PSO with the cross-over operator is a very promising optimization technique. Similar conclusions can be made for the GLbest-PSO with RMS variants also. (C) 2007 Elsevier B. V. All rights reserved. |
| first_indexed | 2025-11-14T18:08:00Z |
| format | Article |
| id | mmu-2779 |
| institution | Multimedia University |
| institution_category | Local University |
| last_indexed | 2025-11-14T18:08:00Z |
| publishDate | 2008 |
| publisher | ELSEVIER SCIENCE BV |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | mmu-27792011-09-14T02:41:33Z http://shdl.mmu.edu.my/2779/ On the improved performances of the particle swarm optimization algorithms with adaptive parameters, cross-over operators and root mean square (RMS) variants for computing optimal control of a class of hybrid systems Arumugam, M. Senthil Rao, M.V.C. T Technology (General) QA75.5-76.95 Electronic computers. Computer science This paper deals with the concept of including the popular genetic algorithm operator, cross-over and root mean square (RMS) variants into particle swarm optimization (PSO) algorithm to make the convergence faster. Two different PSO algorithms are considered in this paper: the first one is the conventional PSO (cPSO) and the second is the global-local best values based PSO (GLbest-PSO). The GLbest-PSO includes global-local best inertia weight (GLbestIW) with global-local best acceleration coefficient (GLbestAC), whereas the cPSO has a time varying inertia weight (TVIW) and either time varying acceleration coefficient (TVAC) or fixed AC (FAC). The effectiveness of the cross-over operator with both PSO algorithms is tested through a constrained optimal control problem of a class of hybrid systems. The experimental results illustrate the advantage of PSO with cross-over operator, which sharpens the convergence and tunes to the best solution. In order to compare and verify the validity and effectiveness of the new approaches for PSO, several statistical analyses are carried out. The results clearly demonstrate that the GLbest-PSO with the cross-over operator is a very promising optimization technique. Similar conclusions can be made for the GLbest-PSO with RMS variants also. (C) 2007 Elsevier B. V. All rights reserved. ELSEVIER SCIENCE BV 2008-01 Article NonPeerReviewed Arumugam, M. Senthil and Rao, M.V.C. (2008) On the improved performances of the particle swarm optimization algorithms with adaptive parameters, cross-over operators and root mean square (RMS) variants for computing optimal control of a class of hybrid systems. Applied Soft Computing, 8 (1). pp. 324-336. ISSN 15684946 http://dx.doi.org/10.1016/j.asoc.2007.01.010 doi:10.1016/j.asoc.2007.01.010 doi:10.1016/j.asoc.2007.01.010 |
| spellingShingle | T Technology (General) QA75.5-76.95 Electronic computers. Computer science Arumugam, M. Senthil Rao, M.V.C. On the improved performances of the particle swarm optimization algorithms with adaptive parameters, cross-over operators and root mean square (RMS) variants for computing optimal control of a class of hybrid systems |
| title | On the improved performances of the particle swarm optimization algorithms with adaptive parameters, cross-over operators and root mean square (RMS) variants for computing optimal control of a class of hybrid systems |
| title_full | On the improved performances of the particle swarm optimization algorithms with adaptive parameters, cross-over operators and root mean square (RMS) variants for computing optimal control of a class of hybrid systems |
| title_fullStr | On the improved performances of the particle swarm optimization algorithms with adaptive parameters, cross-over operators and root mean square (RMS) variants for computing optimal control of a class of hybrid systems |
| title_full_unstemmed | On the improved performances of the particle swarm optimization algorithms with adaptive parameters, cross-over operators and root mean square (RMS) variants for computing optimal control of a class of hybrid systems |
| title_short | On the improved performances of the particle swarm optimization algorithms with adaptive parameters, cross-over operators and root mean square (RMS) variants for computing optimal control of a class of hybrid systems |
| title_sort | on the improved performances of the particle swarm optimization algorithms with adaptive parameters, cross-over operators and root mean square (rms) variants for computing optimal control of a class of hybrid systems |
| topic | T Technology (General) QA75.5-76.95 Electronic computers. Computer science |
| url | http://shdl.mmu.edu.my/2779/ http://shdl.mmu.edu.my/2779/ http://shdl.mmu.edu.my/2779/ |