Abstract
In this paper, we present a fast scalable method to reduce the computation time of genetic algorithms for traveling salesman problem, called the Parallel Pattern Reduction Enhanced Genetic Algorithm (PPREGA). The general idea behind the proposed algorithm is twofold: (1) Eliminate the redundant computations of GA on its convergence process by pattern reduction and (2) Minimize the completion time of GA by parallel computing. Our simulation result shows that the proposed algorithm can significantly reduce not only the computation time but also the maximum completion time of GA. Moreover, our simulation result shows further that the loss of the quality of the end result is small.
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Tsai, CW., Tseng, SP., Chiang, MC., Yang, CS. (2010). A Fast Parallel Genetic Algorithm for Traveling Salesman Problem. In: Hsu, CH., Malyshkin, V. (eds) Methods and Tools of Parallel Programming Multicomputers. MTPP 2010. Lecture Notes in Computer Science, vol 6083. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14822-4_27
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DOI: https://doi.org/10.1007/978-3-642-14822-4_27
Publisher Name: Springer, Berlin, Heidelberg
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