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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References
Blum, C., Roli, A.: Metaheuristics in combinatorial optimization: Overview and conceptual comparison. ACM Computing Surveys 35(3), 268–308 (2003)
MartÃ, R.: Multi-start methods. In: Glover, F.W., Kochenberger, G.A. (eds.) Handbook of Metaheuristics, pp. 355–368. Kluwer Academic Publishers, Boston (1993)
Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolution Programs. Springer, Berlin (1996)
Lee, J.S., Oh, I.S., Moon, B.R.: Hybrid genetic algorithms for feature selection. IEEE Transactions on Pattern Analysis and Machine Intelligence 26(11), 1424–1437 (2004)
Tsai, C.W.: On the Study of Efficient Metaheuristics via Pattern Reduction. PhD thesis, National Sun Yat-sen University, Taiwan, R.O.C (2009)
Cantú-Paz, E.: A survey of parallel genetic algorithms. Calculateurs Paralleles, Reseaux et Systems Repartis 10(2), 141–171 (1998)
Mühlenbein, H.: Parallel genetic algorithms, population genetics and combinatorial optimization. In: Proceedings of the Third International Conference on Genetic Algorithms, San Francisco, CA, USA, pp. 416–421 (1989)
Kohlmorgen, U., Schmeck, H., Haase, K.: Experiences with fine–grained parallel genetic algorithms. In: Annals of Operations Research (1996)
Maeda, Y., Ishita, M., Li, Q.: Fuzzy adaptive search method for parallel genetic algorithm with island combination process. International of Journal of Approximate Reasoning 41, 59–73 (2005)
Wang, L., Maciejewski, A.A., Siegel, H.J., Roychowdhury, V.P., Eldridge, B.D.: A study of five parallel approaches to a genetic algorithm for the traveling salesman problem. Intelligent Automation and Soft Computing 11(4), 217–234 (2005)
Cantú-Paz, E., Goldberg, D.E.: Efficient parallel genetic algorithms: theory and practice. Computer Methods in Applied Mechanics and Engineering 186(2-4), 221–238 (2000)
Cantú-Paz, E.: Efficient and Accurate Parallel Genetic Algorithms. Kluwer Academic Publishers, Norwell (2000)
Tseng, S.P., Tsai, C.W., Chiang, M.C., Yang, C.S.: Fast genetic algorithm based on pattern reduction. In: Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, pp. 214–219 (2008)
TSPLIB: (2009), http://www.iwr.uni-heidelberg.de/groups/comopt/software/TSPLIB95/tsp/ .
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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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