Abstract
This paper introduces a new evolutionary algorithm based on the local search strategy and uses it to solve the Traveling Salesman Problem. The algorithm incorporates speediness of local search methods in neighborhood search with robustness of evolutionary methods in global search in order to obtain global optimum. The experimental results show that the algorithm is of potential to obtain global optimum or more accurate solutions than other evolutionary methods for the TSP.
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References
Litke, J.D.: An Improved Solution to the Traveling Salesman Problem with Thousands of Nodes. Communications of the ACM 27(12), 1227–1236 (1984)
Korte, B.: Applications of Combinatorial Optimization. Talk at the 13th International Mathematical Programming Symposium, Tokyo (1988)
Johnson, D.S.: Local Optimization and the Traveling Salesman Problem. In: Paterson, M. (ed.) ICALP 1990. LNCS, vol. 443, pp. 446–461. Springer, Heidelberg (1990)
Lin, S., Kernighan, B.W.: An Effective Heuristic Algorithm for the Traveling Salesman Problem. Operations Research 21, 498–516 (1973)
Gerfenstette, J.J., Gopal, R., Rosmaita, B., Van Gucht, D.: Genetic Algorithm for the TSP. In: Proceedings of the First International Conference on Genetic Algorithms, pp. 160–168. Lawrence Erlbaum Associates, Hillsdale (1985)
Goldberg, D.E., Lingle, R.: Alleles, Loci, and the TSP. In: Proceedings of the first International Conference on Genetic Algorithms, pp. 154–159. Lawrence Erlbaum Associates, Hillsdale (1985)
Fox, B.R., McMahon, M.B.: Genetic Operators for Sequencing Problems. Foundations of Genetic Algorithms, pp. 284–300. Morgan Kaufmann, San Mateo (1990)
Seniw, D.: A Genetic Algorithm for Traveling Salesman Problem. MSc Thesis, University of North Carolina at Charlotte, NC (1991)
Herdy, M.: Application of the Evolution Strategy to Discrete Optimization Problems. In: Proceedings of the 1st Conference on Parallel Problem Solving from Nature. LNCS, pp. 188–192. Springer, Heidelberg (1990)
Davis, L.: Applying Adaptive Algorithms to Epistatic Domains. In: Proceedings of the International Joint Conference on Artificial Intelligence, pp. 162–164 (1995)
Fogel, D.B.: Applying Evolutionary Programming to Selected Traveling Salesman Problem. Cybernetics and System 24(1), 27–36 (1993)
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Wang, X., Zhang, Gn., Li, Yx. (2009). Solving Traveling Salesman Problem by Using an Evolutionary Algorithm Based on the Local Search Strategy. In: Yu, W., He, H., Zhang, N. (eds) Advances in Neural Networks – ISNN 2009. ISNN 2009. Lecture Notes in Computer Science, vol 5552. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01510-6_64
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DOI: https://doi.org/10.1007/978-3-642-01510-6_64
Publisher Name: Springer, Berlin, Heidelberg
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