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    Multi-colony ant algorithms for the dynamic travelling salesman problem

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    Main article (706.1Kb)
    Date
    2014-12
    Author
    Xin Yao;
    Mavrovouniotis, Michalis;
    Yang, Shengxiang
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    Abstract
    A multi-colony ant colony optimization (ACO) algorithm consists of several colonies of ants. Each colony uses a separate pheromone table in an attempt to maximize the search area explored. Over the years, multi-colony ACO algorithms have been successfully applied on different optimization problems with stationary environments. In this paper, we investigate their performance in dynamic environments. Two types of algorithms are proposed: homogeneous and heterogeneous approaches, where colonies share the same properties and colonies have their own (different) properties, respectively. Experimental results on the dynamic travelling salesman problem show that multi-colony ACO algorithms have promising performance in dynamic environments when compared with single colony ACO algorithms.
    Description
    Citation : Mavrovouniotis, M., Yang, S. and Yao, X. (2014) Multi-colony ant algorithms for the dynamic travelling salesman problem. Proceedings of the 2014 IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments, pp. 9-16
    URI
    http://hdl.handle.net/2086/10945
    DOI
    http://dx.doi.org/10.1109/CIDUE.2014.7007861
    Research Group : Centre for Computational Intelligence
    Research Institute : Institute of Artificial Intelligence (IAI)
    Peer Reviewed : Yes
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    • School of Computer Science and Informatics [2978]

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