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Automated selection of appropriate pheromone representations in ant colony optimization

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posted on 2023-05-18, 05:45 authored by James MontgomeryJames Montgomery, Randall, M, Hendtlass, T
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

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