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.
History
Publication title
Artificial Life
Volume
11
Pagination
269-291
ISSN
1064-5462
Department/School
School of Information and Communication Technology
Publisher
MIT Press
Place of publication
Five Cambridge Center, Cambridge, USA, Ma, 02142
Rights statement
Copyright 2005 Massachusetts Institute of Technology
Repository Status
Open
Socio-economic Objectives
Expanding knowledge in the information and computing sciences