Parallel MATLAB Computing of Multi-objective Optimization of Air Bearing
碩士 === 長庚大學 === 機械工程學系 === 104 === The purpose of this study is to develop a new optimization procedure for many-objective optimization. The particle swarm optimization method is used to solve the multi-factor optimum design. The Fortran and MATLAB programming are combined to perform a parallel flui...
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| Format: | Others |
| Language: | zh-TW |
| Published: |
2016
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| Online Access: | http://ndltd.ncl.edu.tw/handle/6f3767 |
| Summary: | 碩士 === 長庚大學 === 機械工程學系 === 104 === The purpose of this study is to develop a new optimization procedure for many-objective optimization. The particle swarm optimization method is used to solve the multi-factor optimum design. The Fortran and MATLAB programming are combined to perform a parallel fluid-film lubrication analysis for porous air bearing optimization. The MATLAB has real-time display capability which can be used to monitor some useful information in the case of an extended computing process. On the other hand, Fortran has better computing efficiency than the MATLAB when solving the Reynolds equation. In this study, the main optimization procedure is coded in MATLAB program which supplies the values of design variables for Fortran program and monitor the search process. An MATLAB command is used to invoke the Fortran execution program and the search process is repeated until the given stopping criteria are met. The parallel computing is applied to reduce the execution time by using a 16-core workstation.
The developed procedure is applied for air bearing optimal design of two and three objective functions. The performance of nonlinear inertia weight variation for dynamic adaptation applied in particle swarm optimization is discussed. A larger inertia weight facilitates global exploration while a smaller one tends to favor local search. This study presents a computing model which can be easily adapted to other engineering optimization studies.
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