Particle swarm optimization & gravitational search algorithm in sequential process planning
The purpose of this study is to investigate the application of particle swarm optimization (PSO) and gravitational search algorithm (GSA) in assembly sequence planning problem, to look for the sequence which require the least assembly time. The problem model is an assembly process with 25 parts, whi...
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2013
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my-utm-ep.338172017-09-12T08:19:06Z Particle swarm optimization & gravitational search algorithm in sequential process planning 2013-01 Lim, Teik Yee TK Electrical engineering. Electronics Nuclear engineering The purpose of this study is to investigate the application of particle swarm optimization (PSO) and gravitational search algorithm (GSA) in assembly sequence planning problem, to look for the sequence which require the least assembly time. The problem model is an assembly process with 25 parts, which is a high dimension and also NP-hard problem. The study is focused on the comparison between both algorithms and investigation on which method perform better in term of convergence rate and the ability to escape local solution. In this study, the PSO are improved in term of random mechanism and GSA algorithms are improved in term of algorithm in order to improve convergence rate and overcome weak convergence respectively. The quality of randomness is also discussed. The simulation results show that PSO can find better optimum sequence than GSA does. 2013-01 Thesis http://eprints.utm.my/id/eprint/33817/ http://eprints.utm.my/id/eprint/33817/5/LimTiekYeeMFKE2013.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:69726?site_name=Restricted Repository masters Universiti Teknologi Malaysia, Faculty of Electrical Engineering Faculty of Electrical Engineering |
| institution |
Universiti Teknologi Malaysia |
| collection |
UTM Institutional Repository |
| language |
English |
| topic |
TK Electrical engineering Electronics Nuclear engineering |
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TK Electrical engineering Electronics Nuclear engineering Lim, Teik Yee Particle swarm optimization & gravitational search algorithm in sequential process planning |
| description |
The purpose of this study is to investigate the application of particle swarm optimization (PSO) and gravitational search algorithm (GSA) in assembly sequence planning problem, to look for the sequence which require the least assembly time. The problem model is an assembly process with 25 parts, which is a high dimension and also NP-hard problem. The study is focused on the comparison between both algorithms and investigation on which method perform better in term of convergence rate and the ability to escape local solution. In this study, the PSO are improved in term of random mechanism and GSA algorithms are improved in term of algorithm in order to improve convergence rate and overcome weak convergence respectively. The quality of randomness is also discussed. The simulation results show that PSO can find better optimum sequence than GSA does. |
| format |
Thesis |
| qualification_level |
Master's degree |
| author |
Lim, Teik Yee |
| author_facet |
Lim, Teik Yee |
| author_sort |
Lim, Teik Yee |
| title |
Particle swarm optimization & gravitational search algorithm in sequential process planning |
| title_short |
Particle swarm optimization & gravitational search algorithm in sequential process planning |
| title_full |
Particle swarm optimization & gravitational search algorithm in sequential process planning |
| title_fullStr |
Particle swarm optimization & gravitational search algorithm in sequential process planning |
| title_full_unstemmed |
Particle swarm optimization & gravitational search algorithm in sequential process planning |
| title_sort |
particle swarm optimization & gravitational search algorithm in sequential process planning |
| granting_institution |
Universiti Teknologi Malaysia, Faculty of Electrical Engineering |
| granting_department |
Faculty of Electrical Engineering |
| publishDate |
2013 |
| url |
http://eprints.utm.my/id/eprint/33817/5/LimTiekYeeMFKE2013.pdf |
| _version_ |
1747816192405405696 |