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Improving exploration in ant colony optimisation with antennation


Beer, C and Hendtlass, T and Montgomery, J, Improving exploration in ant colony optimisation with antennation, Proceedings of the 2012 IEEE Congress on Evolutionary Computation, 10-15 June 2012, Brisbane, Australia, pp. 2926-2933. ISBN 978-1-4673-1510-4 (2012) [Refereed Conference Paper]

Copyright Statement

Copyright 2012 IEEE

DOI: doi:10.1109/CEC.2012.6252923


Ant Colony Optimisation (ACO) algorithms use two heuristics to solve computational problems: one long-term (pheromone) and the other short-term (local heuristic). This paper details the development of antennation, a mid-term heuristic based on an analogous process in real ants. This is incorporated into ACO for the Travelling Salesman Problem (TSP). Antennation involves sharing information of the previous paths taken by ants, including information gained from previous meetings. Antennation was added to the Ant System (AS), Ant Colony System (ACS) and Ant Multi-Tour System (AMTS) algorithms. Tests were conducted on symmetric TSPs of varying size. Antennation provides an advantage when incorporated into algorithms without an inbuilt exploration mechanism and a disadvantage to those that do. AS and AMTS with antennation have superior performance when compared to their canonical form, with the effect increasing as problem size increases.

Item Details

Item Type:Refereed Conference Paper
Keywords:ant colony optimization, travelling salesman problem, mid-range heuristic
Research Division:Information and Computing Sciences
Research Group:Machine learning
Research Field:Neural networks
Objective Division:Expanding Knowledge
Objective Group:Expanding knowledge
Objective Field:Expanding knowledge in the information and computing sciences
UTAS Author:Montgomery, J (Dr James Montgomery)
ID Code:91929
Year Published:2012
Deposited By:Information and Communication Technology
Deposited On:2014-06-03
Last Modified:2017-11-06

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