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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| 主要作者: | |
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| 格式: | Thesis |
| 语言: | English |
| 出版: |
2013
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| 主题: | |
| 在线阅读: | http://eprints.utm.my/id/eprint/33817/5/LimTiekYeeMFKE2013.pdf |
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| 总结: | 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. |
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