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A statistical analysis of parameter values for the rank-based ant colony optimization algorithm for the traveling salesperson problem

Author

Listed:
  • F J Vasko

    (Kutztown University)

  • J D Bobeck

    (Kutztown University)

  • M A Governale

    (Kutztown University)

  • D J Rieksts

    (Kutztown University)

  • J D Keffer

    (University of Delaware)

Abstract

Ant colony optimization (ACO) is a metaheuristic for solving combinatorial optimization problems that is based on the foraging behavior of biological ant colonies. Starting with the 1996 seminal paper by Dorigo, Maniezzo and Colorni, ACO techniques have been used to solve the traveling salesperson problem (TSP). In this paper, we focus on a particular type of the ACO algorithm, namely, the rank-based ACO algorithm for the TSP. In particular, this paper identifies an optimal set of key parameters by statistical analysis applied to results of the rank-based ACO for the TSP. Specifically, for six frequently used TSPs available on the World Wide Web, we will solve a total of 27 000 instances for each problem.

Suggested Citation

  • F J Vasko & J D Bobeck & M A Governale & D J Rieksts & J D Keffer, 2011. "A statistical analysis of parameter values for the rank-based ant colony optimization algorithm for the traveling salesperson problem," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(6), pages 1169-1176, June.
    • Handle: RePEc:pal:jorsoc:v:62:y:2011:i:6:d:10.1057_jors.2010.74
    DOI: 10.1057/jors.2010.74
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    References listed on IDEAS

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    1. Gerhard Reinelt, 1991. "TSPLIB—A Traveling Salesman Problem Library," INFORMS Journal on Computing, INFORMS, vol. 3(4), pages 376-384, November.
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