Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14279/30863
Title: Ant colony optimization algorithms with immigrants schemes for the dynamic travelling salesman problem
Authors: Mavrovouniotis, Michalis 
Yang, Shengxiang 
Major Field of Science: Natural Sciences
Field Category: Computer and Information Sciences
Keywords: Ant colony;immigrants schemes;salesman problem
Issue Date: 1-Jan-2013
Source: Studies in Computational Intelligence, 2013, vol. 490, pp. 317 - 341
Volume: 490
Start page: 317
End page: 341
Journal: Studies in Computational Intelligence 
Abstract: Ant colony optimization (ACO) algorithms have proved to be powerful methods to address dynamic optimization problems (DOPs). However, once the population converges to a solution and a dynamic change occurs, it is difficult for the population to adapt to the new environment since high levels of pheromone will be generated to a single trail and force the ants to follow it even after a dynamic change. A good solution is to maintain the diversity via transferring knowledge from previous environments to the pheromone trails using immigrants. In this chapter, we investigate ACO algorithms with different immigrants schemes for two types of dynamic travelling salesman problems (DTSPs) with traffic factor, i.e., under random and cyclic dynamic changes. The experimental results based on different DTSP test cases show that the investigated algorithms outperform other peer ACO algorithms and that different immigrants schemes are beneficial on different environmental cases. © 2013 Springer-Verlag Berlin Heidelberg.
URI: https://hdl.handle.net/20.500.14279/30863
ISBN: 9783642384158
ISSN: 1860949X
DOI: 10.1007/978-3-642-38416-5_13
Rights: © Springer-Verlag Berlin Heidelberg
Type: Article
Affiliation : De Montfort University 
Publication Type: Peer Reviewed
Appears in Collections:Άρθρα/Articles

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