Abstract
The traveling salesman problem concerns the best way to visit a set of customers located in some metric space and to minimize the length of the tour over all customer subsets. The problem is a typical NP-hard combinatorial optimization problem, which is of major importance in real world applications. An effective hybrid genetic algorithm-ECOGA is proposed for the problem in this paper, which combines 2-exchange crossover heuristic operator and improved 2OPT of neighbor search algorithm absorbing K-Nearest Neighbor. What’s more, the rule of 5 is applied to the proposed algorithm to guide the search direction. On a set of standard test problems with symmetric distances, the proposed ECOGA found the solutions that were optimal in every case and some of them are superior to the optimality found in TSPLIB. The ECOGA is completive with other genetic algorithm published to date in both solution quality and computation time.
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Li, L., Zhang, Y. (2007). An Improved Genetic Algorithm for the Traveling Salesman Problem. In: Huang, DS., Heutte, L., Loog, M. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Contemporary Intelligent Computing Techniques. ICIC 2007. Communications in Computer and Information Science, vol 2. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74282-1_24
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DOI: https://doi.org/10.1007/978-3-540-74282-1_24
Publisher Name: Springer, Berlin, Heidelberg
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