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dc.contributor.authorXin Yaoen
dc.contributor.authorMavrovouniotis, Michalisen
dc.contributor.authorYang, Shengxiangen
dc.date.accessioned2015-05-06T13:22:10Z
dc.date.available2015-05-06T13:22:10Z
dc.date.issued2014-12
dc.identifier.citationMavrovouniotis, 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. 9-16en
dc.identifier.urihttp://hdl.handle.net/2086/10945
dc.description.abstractA 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.en
dc.language.isoen_USen
dc.publisherIEEE Pressen
dc.subjectAnt colony optimizationen
dc.subjectDynamic travelling salesman problemen
dc.titleMulti-colony ant algorithms for the dynamic travelling salesman problemen
dc.typeConferenceen
dc.identifier.doihttp://dx.doi.org/10.1109/CIDUE.2014.7007861
dc.researchgroupCentre for Computational Intelligenceen
dc.peerreviewedYesen
dc.funderEPSRC (Engineering and Physical Sciences Research Council)en
dc.projectidEP/K001310/1en
dc.researchinstituteInstitute of Artificial Intelligence (IAI)en


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