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dc.contributor.authorMavrovouniotis, Michalisen
dc.contributor.authorYang, Shengxiangen
dc.contributor.authorVan, Mienen
dc.contributor.authorLi, Changheen
dc.contributor.authorPolycarpou, Mariosen
dc.creatorMavrovouniotis, Michalisen
dc.creatorYang, Shengxiangen
dc.creatorVan, Mienen
dc.creatorLi, Changheen
dc.creatorPolycarpou, Mariosen
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 traveling 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.sourceIEEE Computational Intelligence Magazineen
dc.titleAnt Colony Optimization Algorithms for Dynamic Optimization: A Case Study of the Dynamic Travelling Salesperson Problem [Research Frontier]en
dc.description.endingpage63Πολυτεχνική Σχολή / Faculty of EngineeringΤμήμα Ηλεκτρολόγων Μηχανικών και Μηχανικών Υπολογιστών / Department of Electrical and Computer Engineering
dc.contributor.orcidPolycarpou, Marios [0000-0001-6495-9171]
dc.contributor.orcidMavrovouniotis, Michalis [0000-0002-5281-4175]

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