Improving exploration in Ant Colony Optimisation with antennation

Date

2012-06

Authors

Beer, Christopher
Hendtlass, Tim
Montgomery, James

Journal Title

Journal ISSN

Volume Title

Publisher

IEEE

Abstract

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.

Description

Keywords

Ant Colony Optimisation, optimisation, Travelling Salesman Problem, antennation, mid-range heuristic, adaptive arrays, convergence, educational institutions, equations, insects, optimization

Citation

Beer, C, Hendtlass, T. & Montgomery, J. (2012, June). Improving exploration in Ant Colony Optimisation with antennation. Paper presented at the 2012 IEEE Congress on Evolutionary Computation (CEC), Brisbane, Australia, June 10-15, 2012 (pp. 2926-2933) [and] 2012 IEEE World Congress on Computational Intelligence. Piscataway, NJ: IEEE CEC

Source

2012 IEEE Congress on Evolutionary Computation Proceedings

Type

Conference paper

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DOI

10.1109/CEC.2012.6252923

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