Please use this identifier to cite or link to this item: http://buratest.brunel.ac.uk/handle/2438/5878
Title: Ant colony optimization with direct communication for the traveling salesman problem
Authors: Mavrovouniotis, M
Yang, S
Keywords: Algorithm design and analysis;Approximation algorithms;Cities and towns;Convergence;Lead;Runtime;Traveling salesman problems
Issue Date: 2010
Publisher: IEEE
Citation: The 2010 UK Workshop on Computational Intelligence, Colchester: 1 - 6, 08 - 10 Sep 2010
Abstract: Ants in conventional ant colony optimization (ACO) algorithms use pheromone to communicate. Usually, this indirect communication leads the algorithm to a stagnation behaviour, where the ants follow the same path from early stages. This occurs because high levels of pheromone are developed, which force the ants to follow the same corresponding trails. As a result, the population gets trapped into a local optimum solution which is difficult to escape from it. In this paper, a direct communication (DC) scheme is proposed where ants are able to exchange cities with other ants that belong to their communication range. Experiments show that the DC scheme delays convergence and improves the solution quality of conventional ACO algorithms regarding the traveling salesman problem, since it guides the population towards the global optimum solution. The ACO algorithm with the proposed DC scheme has better performance, especially on large problem instances, even though it increases the computational time in comparison with a conventional ACO algorithm.
Description: This article is posted here with permission from IEEE - Copyright @ 2010 IEEE
URI: http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5625608&tag=1
http://bura.brunel.ac.uk/handle/2438/5878
DOI: http://dx.doi.org/10.1109/UKCI.2010.5625608
ISBN: 978-1-4244-8774-5
Appears in Collections:Publications
Computer Science
Dept of Computer Science Research Papers

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