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Automated selection of appropriate pheromone representations in ant colony optimization


Montgomery, J and Randall, M and Hendtlass, T, Automated selection of appropriate pheromone representations in ant colony optimization, Artificial Life, 11, (3) pp. 269-291. ISSN 1064-5462 (2005) [Refereed Article]


Copyright Statement

Copyright 2005 Massachusetts Institute of Technology

DOI: doi:10.1162/1064546054407149


Ant colony optimization (ACO) is a constructive metaheuristic that uses an analogue of ant trail pheromones to learn about good features of solutions. Critically, the pheromone representation for a particular problem is usually chosen intuitively rather than by following any systematic process. In some representations, distinct solutions appear multiple times, increasing the effective size of the search space and potentially misleading ants as to the true learned value of those solutions. In this article, we present a novel system for automatically generating appropriate pheromone representations, based on the characteristics of the problem model that ensures unique pheromone representation of solutions. This is the first stage in the development of a generalized ACO system that could be applied to a wide range of problems with little or no modification. However, the system we propose may be used in the development of any problem-specific ACO algorithm.

Item Details

Item Type:Refereed Article
Keywords:ant colony optimisation, solution representation, search space
Research Division:Information and Computing Sciences
Research Group:Machine learning
Research Field:Neural networks
Objective Division:Expanding Knowledge
Objective Group:Expanding knowledge
Objective Field:Expanding knowledge in the information and computing sciences
UTAS Author:Montgomery, J (Dr James Montgomery)
ID Code:97248
Year Published:2005
Web of Science® Times Cited:11
Deposited By:Information and Communication Technology
Deposited On:2014-12-09
Last Modified:2018-08-16
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