Automated selection of appropriate pheromone representations in ant colony optimization

James Montgomery*, Marcus Randall, Tim Hendtlass

*Corresponding author for this work

Research output: Contribution to journalArticleResearchpeer-review

13 Citations (Scopus)


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 line or no modification. However, the system we propose may be used in the development of any problem-specific ACO algorithm.

Original languageEnglish
Pages (from-to)269-291
Number of pages23
JournalArtificial Life
Issue number3
Publication statusPublished - Jun 2005


Dive into the research topics of 'Automated selection of appropriate pheromone representations in ant colony optimization'. Together they form a unique fingerprint.

Cite this