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dc.contributor.authorMavrovouniotis, Michalis
dc.contributor.authorYang, Shengxiang
dc.contributor.authorVan, Mien
dc.contributor.authorLi, Changhe
dc.contributor.authorMarios, Polycarpou
dc.date.accessioned2019-10-25T10:04:14Z
dc.date.available2019-10-25T10:04:14Z
dc.date.issued2019-10
dc.identifier.citationMavrovouniotis, M., Yang, S., Van, M., Li, C. and Polycarpou, M. (2019) Ant colony optimization algorithms for dynamic optimization: A case study of the dynamic travelling salesperson problem. IEEE Computational Intelligence Magazine,en
dc.identifier.urihttps://dora.dmu.ac.uk/handle/2086/18661
dc.descriptionThe file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.en
dc.description.abstractAnt colony optimization is a swarm intelligence metaheuristic inspired by the foraging behavior of some ant species. Ant colony optimization has been successfully applied to challenging optimization problems. This article investigates existing ant colony optimization algorithms specifically designed for combinatorial optimization problems with a dynamic environment. The investigated algorithms are classified into two frameworks: evaporation-based and population-based. A case study of using these algorithms to solve the dynamic travelling salesperson problem is described. Experiments are systematically conducted using a proposed dynamic benchmark framework to analyze the effect of important ant colony optimization features on numerous test cases. Different performance measures are used to evaluate the adaptation capabilities of the investigated algorithms, indicating which features are the most important when designing ant colony optimization algorithms in dynamic environments.en
dc.language.isoen_USen
dc.publisherIEEE Pressen
dc.subjectAnt colony optimizationen
dc.subjectDynamic optimizationen
dc.subjectDynamic travelling salesperson problemen
dc.titleAnt colony optimization algorithms for dynamic optimization: A case study of the dynamic travelling salesperson problemen
dc.typeArticleen
dc.peerreviewedYesen
dc.funderOther external funder (please detail below)en
dc.projectid61673331 and 61673355en
dc.cclicenceCC-BY-NC-NDen
dc.date.acceptance2019-10-12
dc.researchinstituteInstitute of Artificial Intelligence (IAI)en
dc.funder.otherNational Natural Science Foundation of Chinaen


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