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Many animals use chemical substances known as pheromones to induce behavioural changes in other members of the same species. The use of pheromones by ants in particular has lead to the development of a number of computational analogues of ant colony behaviour including Ant Colony Optimisation. Although many animals use a range of pheromones in their communication, ant algorithms have typically focused on the use of just one, a substance that encourages succeeding generations of (artificial) ants to follow the same path as previous generations. Ant algorithms for multi-objective optimisation and those employing multiple colonies have made use of more than one pheromone, but the interactions between these different pheromones are largely simple extensions of single criterion, single colony ant algorithms. This paper investigates an alternative form of interaction between normal pheromone and anti-pheromone. Three variations of Ant Colony System that apply the anti-pheromone concept in different ways are described and tested against benchmark travelling salesman problems. The results indicate that the use of anti-pheromone can lead to improved performance. However, if anti-pheromone is allowed too great an influence on ants’ decisions, poorer performance may result.

Item Type:	Refereed Conference Paper
Keywords:	ant colony optimisation, solution representation, search space, optimisation
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:	97260
Year Published:	2002
Deposited By:	Information and Communication Technology
Deposited On:	2014-12-09
Last Modified:	2016-01-18
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