On the performance of the particle swarm optimization algorithm with various inertia weight variants for computing optimal control of a class of hybrid systems
This paper presents an alternative and efficient method for solving the optimal control of single-stage hybrid manufacturing systems which are composed with two different categories: continuous dynamics and discrete dynamics. Three different inertia weights, a constant inertia weight ( CIW), time-va...
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| Format: | Article |
| Language: | English |
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2006
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| Online Access: | http://shdl.mmu.edu.my/2039/ http://shdl.mmu.edu.my/2039/1/1383.pdf |
| _version_ | 1848789947472936960 |
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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 presents an alternative and efficient method for solving the optimal control of single-stage hybrid manufacturing systems which are composed with two different categories: continuous dynamics and discrete dynamics. Three different inertia weights, a constant inertia weight ( CIW), time-varying inertia weight ( TVIW), and global-local best inertia weight ( GLbestIW), are considered with the particle swarm optimization ( PSO) algorithm to analyze the impact of inertia weight on the performance of PSO algorithm. The PSO algorithm is simulated individually with the three inertia weights separately to compute the optimal control of the single-stage hybrid manufacturing system, and it is observed that the PSO with the proposed inertia weight yields better result in terms of both optimal solution and faster convergence. Added to this, the optimal control problem is also solved through real coded genetic algorithm ( RCGA) and the results are compared with the PSO algorithms. A typical numerical example is also included in this paper to illustrate the efficacy and betterment of the proposed algorithm. Several statistical analyses are carried out from which can be concluded that the proposed method is superior to all the other methods considered in this paper. |
| first_indexed | 2025-11-14T18:04:49Z |
| format | Article |
| id | mmu-2039 |
| institution | Multimedia University |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T18:04:49Z |
| publishDate | 2006 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | mmu-20392011-08-10T06:48:34Z http://shdl.mmu.edu.my/2039/ On the performance of the particle swarm optimization algorithm with various inertia weight variants for computing optimal control of a class of hybrid systems Arumugam, M. Senthil Rao, M. V. C. Q Science (General) This paper presents an alternative and efficient method for solving the optimal control of single-stage hybrid manufacturing systems which are composed with two different categories: continuous dynamics and discrete dynamics. Three different inertia weights, a constant inertia weight ( CIW), time-varying inertia weight ( TVIW), and global-local best inertia weight ( GLbestIW), are considered with the particle swarm optimization ( PSO) algorithm to analyze the impact of inertia weight on the performance of PSO algorithm. The PSO algorithm is simulated individually with the three inertia weights separately to compute the optimal control of the single-stage hybrid manufacturing system, and it is observed that the PSO with the proposed inertia weight yields better result in terms of both optimal solution and faster convergence. Added to this, the optimal control problem is also solved through real coded genetic algorithm ( RCGA) and the results are compared with the PSO algorithms. A typical numerical example is also included in this paper to illustrate the efficacy and betterment of the proposed algorithm. Several statistical analyses are carried out from which can be concluded that the proposed method is superior to all the other methods considered in this paper. 2006 Article NonPeerReviewed application/pdf en http://shdl.mmu.edu.my/2039/1/1383.pdf Arumugam, M. Senthil and Rao, M. V. C. (2006) On the performance of the particle swarm optimization algorithm with various inertia weight variants for computing optimal control of a class of hybrid systems. Discrete Dynamics in Nature and Society, 2006. p. 1. ISSN 1026-0226 http://dx.doi.org/10.1155/DDNS/2006/79295 doi:10.1155/DDNS/2006/79295 doi:10.1155/DDNS/2006/79295 |
| spellingShingle | Q Science (General) Arumugam, M. Senthil Rao, M. V. C. On the performance of the particle swarm optimization algorithm with various inertia weight variants for computing optimal control of a class of hybrid systems |
| title | On the performance of the particle swarm optimization algorithm with various inertia weight variants for computing optimal control of a class of hybrid systems |
| title_full | On the performance of the particle swarm optimization algorithm with various inertia weight variants for computing optimal control of a class of hybrid systems |
| title_fullStr | On the performance of the particle swarm optimization algorithm with various inertia weight variants for computing optimal control of a class of hybrid systems |
| title_full_unstemmed | On the performance of the particle swarm optimization algorithm with various inertia weight variants for computing optimal control of a class of hybrid systems |
| title_short | On the performance of the particle swarm optimization algorithm with various inertia weight variants for computing optimal control of a class of hybrid systems |
| title_sort | on the performance of the particle swarm optimization algorithm with various inertia weight variants for computing optimal control of a class of hybrid systems |
| topic | Q Science (General) |
| url | http://shdl.mmu.edu.my/2039/ http://shdl.mmu.edu.my/2039/ http://shdl.mmu.edu.my/2039/ http://shdl.mmu.edu.my/2039/1/1383.pdf |