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 |
CORE Recommender
SCOPUSTM
Citations
20
7
checked on Mar 14, 2024
Page view(s)
78
Last Week
0
0
Last month
3
3
checked on Nov 8, 2024
Google ScholarTM
Check
Altmetric
Items in KTISIS are protected by copyright, with all rights reserved, unless otherwise indicated.