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Automated selection of appropriate pheromone representations in ant colony optimisation
conference contribution
posted on 2023-05-23, 09:34 authored by Erin MontgomeryErin Montgomery, Randall, M, Hendtlass, TAnt 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.
History
Publication title
Proceedings of the 1st Australian Conference on Artificial Life (ACAL 2003)Pagination
170-184ISBN
0975152807Department/School
School of Information and Communication TechnologyPublisher
University of New South WalesPlace of publication
NSW, AustraliaEvent title
1st Australian Conference on Artificial Life (ACAL 2003)Event Venue
Canberra, ACTDate of Event (Start Date)
2003-12-06Date of Event (End Date)
2003-12-07Rights statement
Copyright unknownRepository Status
- Restricted