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Advanced Harmony Search with Ant Colony Optimization for Solving the Traveling Salesman Problem

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  • Ho-Yoeng Yun
  • Suk-Jae Jeong
  • Kyung-Sup Kim

Abstract

We propose a novel heuristic algorithm based on the methods of advanced Harmony Search and Ant Colony Optimization (AHS-ACO) to effectively solve the Traveling Salesman Problem (TSP). The TSP, in general, is well known as an NP-complete problem, whose computational complexity increases exponentially by increasing the number of cities. In our algorithm, Ant Colony Optimization (ACO) is used to search the local optimum in the solution space, followed by the use of the Harmony Search to escape the local optimum determined by the ACO and to move towards a global optimum. Experiments were performed to validate the efficiency of our algorithm through a comparison with other algorithms and the optimum solutions presented in the TSPLIB. The results indicate that our algorithm is capable of generating the optimum solution for most instances in the TSPLIB; moreover, our algorithm found better solutions in two cases (kroB100 and pr144) when compared with the optimum solution presented in the TSPLIB.

Suggested Citation

  • Ho-Yoeng Yun & Suk-Jae Jeong & Kyung-Sup Kim, 2013. "Advanced Harmony Search with Ant Colony Optimization for Solving the Traveling Salesman Problem," Journal of Applied Mathematics, Hindawi, vol. 2013, pages 1-8, November.
    • Handle: RePEc:hin:jnljam:123738
    DOI: 10.1155/2013/123738
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    Cited by:

    1. MinJun Kim & JuneSeok Hong & Wooju Kim, 2019. "An Efficient Representation Using Harmony Search for Solving the Virtual Machine Consolidation," Sustainability, MDPI, vol. 11(21), pages 1-20, October.
    2. Raya-Armenta, Jose Maurilio & Bazmohammadi, Najmeh & Avina-Cervantes, Juan Gabriel & Sáez, Doris & Vasquez, Juan C. & Guerrero, Josep M., 2021. "Energy management system optimization in islanded microgrids: An overview and future trends," Renewable and Sustainable Energy Reviews, Elsevier, vol. 149(C).

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