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Ant Colony Optimisation (ACO) is a constructive metaheuristic that uses an analogue of ant trail pheromones to learn about good features of solutions. ACO implementations are typically tailored in an ad hoc manner to suit particular problems. 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 paper, we present a novel system for automatically generating appropriate parsimonious 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 generalised ACO system that may 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 Type:	Refereed Conference Paper
Keywords:	ant colony optimisation, solution representation, search space, optimisation
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:	97258
Year Published:	2003
Web of Science® Times Cited:	11
Deposited By:	Information and Communication Technology
Deposited On:	2014-12-09
Last Modified:	2022-06-27
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