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Τίτλος: Ant Colony Optimization with Local Search for Dynamic Traveling Salesman Problems
Συγγραφείς: Mavrovouniotis, Michalis 
Müller, Felipe M. 
Yang, Shengxiang 
Major Field of Science: Natural Sciences
Field Category: Computer and Information Sciences
Λέξεις-κλειδιά: Ant colony optimization (ACO);dynamic traveling salesman problem (DTSP);local search;memetic algorithm
Ημερομηνία Έκδοσης: 1-Ιου-2017
Πηγή: IEEE Transactions on Cybernetics, vol. 47, iss. 7, pp. 1743 - 1756
Volume: 47
Issue: 7
Start page: 1743
End page: 1756
Περιοδικό: IEEE Transactions on Cybernetics 
Περίληψη: For a dynamic traveling salesman problem (DTSP), the weights (or traveling times) between two cities (or nodes) may be subject to changes. Ant colony optimization (ACO) algorithms have proved to be powerful methods to tackle such problems due to their adaptation capabilities. It has been shown that the integration of local search operators can significantly improve the performance of ACO. In this paper, a memetic ACO algorithm, where a local search operator (called unstring and string) is integrated into ACO, is proposed to address DTSPs. The best solution from ACO is passed to the local search operator, which removes and inserts cities in such a way that improves the solution quality. The proposed memetic ACO algorithm is designed to address both symmetric and asymmetric DTSPs. The experimental results show the efficiency of the proposed memetic algorithm for addressing DTSPs in comparison with other state-of-the-art algorithms.
URI: https://hdl.handle.net/20.500.14279/30839
ISSN: 21682267
DOI: 10.1109/TCYB.2016.2556742
Rights: © IEEE
Type: Article
Affiliation: De Montfort University 
Federal University 
Publication Type: Peer Reviewed
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