| Summary: | 碩士 === 國立臺中技術學院 === 事業經營研究所 === 95 === The combinatorial optimization problem is very important and famous problem in the area of computer science. The feature of this kind of problem is that when the problem scale becomes large, the cost of computing time will be immense. On recent researches, lots of scholars apply the meta-heuristic algorithms to solve the problems. But the related parameters of algorithm are difficult to setting. Parameters will affect the result. However, to solve different problems, the setting of algorithms' parameters will be different. In practice, setting parameters is an important but boring problem to apply heuristic algorithms to solve problems. This research proposes a platform of parallel processing. This platform combines two algorithms, Genetic Algorithm (GA) and Ant Colony Optimization (ACO), and uses Soloman’s VRPTW benchmark problems to test the performance of this platform. ACO is used to solve the VRPTW problems, and GA is used to evolve the parameters of ACO. The empirical results show that our parallel processing platform can reduce the computing time, and simplify the processes of parameters setting.
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