A review of ant algorithms

Mullen, R. J.; Monekosso, Dorothy; Barman, S.; Remagnino, P.

Abstract

Ant algorithms are optimisation algorithms inspired by the foraging behaviour of real ants in the wild. Introduced in the early 1990s, ant algorithms aim at finding approximate solutions to optimisation problems through the use of artificial ants and their indirect communication via synthetic pheromones. The first ant algorithms and their development into the Ant Colony Optimisation (ACO) metaheuristic is described herein. An overview of past and present typical applications as well as more specialised and novel applications is given. The use of ant algorithms alongside more traditional machine learning techniques to produce robust, hybrid, optimisation algorithms is addressed, with a look towards future developments in this area of study. © 2009 Elsevier Ltd. All rights reserved.

• Journal Article Type: Review
• Publication Date: Aug 1, 2009
• Journal: Expert Systems with Applications
• Print ISSN: 0957-4174
• Publisher: Elsevier
• Peer Reviewed: Not Peer Reviewed
• Volume: 36
• Issue: 6
• Pages: 9608-9617
• DOI: https://doi.org/10.1016/j.eswa.2009.01.020
• Keywords: ant algorithms
• Public URL: https://uwe-repository.worktribe.com/output/1004639
• Publisher URL: http://dx.doi.org/10.1016/j.eswa.2009.01.020