Travelling salesman problem (TSP) is a most popular combinatorial routing problem, belongs to the class of NP-hard problems. Many approacheshave been proposed for TSP.Among them, swarm intelligence (SI) algorithms can effectively achieve optimal tours with the minimum lengths and attempt to avoid trapping in local minima points. The transcendence of each SI is depended on the nature of the problem. In our studies, there has been yet no any article, which had compared the performance of SI algorithms for TSP perfectly. In this paper,four common SI algorithms are used to solve TSP, in order to compare the performance of SI algorithms for the TSP problem. These algorithms include genetic algorithm, particle swarm optimization, ant colony optimization, and artificial bee colony. For each SI, the various parameters and operators were tested, and the best values were selected for it. Experiments oversome benchmarks fromTSPLIBshow that artificial bee colony algorithm is the best one among the fourSI-basedmethods to solverouting problems like TSP.


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    Title :

    A COMPARISON BETWEEN SWARM INTELLIGENCE ALGORITHMS FOR ROUTING PROBLEMS


    Contributors:

    Publication date :

    2016-03-28


    Remarks:

    oai:zenodo.org:3630059
    Electrical & Computer Engineering: An International Journal (ECIJ) 5(1) 17-33



    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Classification :

    DDC:    629




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