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|Title:||Optimization of multi-pass milling using parallel genetic algorithm and parallel genetic simulated annealing|
|Authors:||Wang, Z.G. |
Parallel genetic algorithm
|Citation:||Wang, Z.G., Rahman, M., Wong, Y.S., Sun, J. (2005-12). Optimization of multi-pass milling using parallel genetic algorithm and parallel genetic simulated annealing. International Journal of Machine Tools and Manufacture 45 (15) : 1726-1734. ScholarBank@NUS Repository. https://doi.org/10.1016/j.ijmachtools.2005.03.009|
|Abstract:||This paper presents an approach to select the optimal machining parameters for multi-pass milling. It is based on two recent approaches, genetic algorithm (GA) and simulated annealing (SA), which have been applied to many difficult combinatorial optimization problems with certain strengths and weaknesses. In this paper, a hybrid of GA and SA (GSA) is presented to use the strengths of GA and SA and overcome their weaknesses. In order to improve, the performance of GSA further, the parallel genetic simulated annealing (PGSA) has been developed and used to optimize the cutting parameters for multi-pass milling process. For comparison, conventional parallel GA (PGA) is also chosen as another optimization method. An application example that has been solved previously using the geometric programming (GP) and dynamic programming (DP) method is presented. From the given results, PGSA is shown to be more suitable and efficient for optimizing the cutting parameters for milling operation than GP+DP and PGA. © 2005 Elsevier Ltd. All rights reserved.|
|Source Title:||International Journal of Machine Tools and Manufacture|
|Appears in Collections:||Staff Publications|
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