Ant colony optimization with direct communication for the traveling salesman problem.
Ants in conventional ant colony optimization (ACO) algorithms use pheromone to communicate. Usually, this indirect communication leads the algorithm to a stagnation behaviour, where the ants follow the same path from early stages. This occurs because high levels of pheromone are developed, which force the ants to follow the same corresponding trails. As a result, the population gets trapped into a local optimum solution which is difficult to escape from it. In thispaper, a direct communication (DC) scheme is proposed where ants are able to exchange cities with other ants that belong to their communication range. Experiments show that the DCscheme delays convergence and improves the solution quality of conventional ACO algorithms regarding the traveling salesman problem, since it guides the population towards the globaloptimum solution. The ACO algorithm with the proposed DCscheme has better performance, especially on large probleminstances, even though it increases the computational time in comparison with a conventional ACO algorithm.
Citation : Mavrovouniotis, M. and Yang, S. (2010) Ant colony optimization with direct communication for the traveling salesman problem. In: 2010 UK Workshop on Computational Intelligence (UKCI), Colchester, September 2010. New York: IEEE.
ISBN : 978-1-4244-8775-2
Research Group : Centre for Computational Intelligence
Research Institute : Institute of Artificial Intelligence (IAI)
Peer Reviewed : Yes