Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14279/30864
Title: Dynamic vehicle routing: A memetic ant colony optimization approach
Authors: Mavrovouniotis, Michalis 
Yang, Shengxiang 
Major Field of Science: Natural Sciences
Field Category: Computer and Information Sciences
Keywords: Dynamic vehicle routing;memetic ant;colony optimization approach
Issue Date: 1-Jan-2013
Source: Studies in Computational Intelligence, 2013, vol. 505, pp. 283 - 301
Volume: 505
Start page: 283
End page: 301
Journal: Studies in Computational Intelligence 
Abstract: Over the years, several variations of the dynamic vehicle routing problem (DVRP) have been considered due to its similarities with many real-world applications. Several methods have been applied to address DVRPs, in which ant colony optimization (ACO) has shown promising results due to its adaptation capabilities. In this chapter, we generate another variation of the DVRP with traffic factor and propose a memetic algorithm based on the ACO framework to address it. Multiple local search operators are used to improve the exploitation capacity and a diversity scheme based on random immigrants is used to improve the exploration capacity of the algorithm. The proposed memetic ACO algorithm is applied on different test cases of the DVRP with traffic factors and is compared with other peer ACO algorithms. The experimental results show that the proposed memetic ACO algorithm shows promising results. © 2013 Springer-Verlag Berlin Heidelberg.
URI: https://hdl.handle.net/20.500.14279/30864
ISBN: 9783642393037
ISSN: 1860949X
DOI: 10.1007/978-3-642-39304-4_11
Rights: © Springer-Verlag Berlin Heidelberg
Type: Article
Affiliation : De Montfort University 
Publication Type: Peer Reviewed
Appears in Collections:Άρθρα/Articles

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