Multi-colony ant algorithms for the dynamic travelling salesman problem
A multi-colony ant colony optimization (ACO) algorithm consists of several colonies of ants. Each colony uses a separate pheromone table in an attempt to maximize the search area explored. Over the years, multi-colony ACO algorithms have been successfully applied on different optimization problems with stationary environments. In this paper, we investigate their performance in dynamic environments. Two types of algorithms are proposed: homogeneous and heterogeneous approaches, where colonies share the same properties and colonies have their own (different) properties, respectively. Experimental results on the dynamic travelling salesman problem show that multi-colony ACO algorithms have promising performance in dynamic environments when compared with single colony ACO algorithms.
Citation : Mavrovouniotis, M., Yang, S. and Yao, X. (2014) Multi-colony ant algorithms for the dynamic travelling salesman problem. Proceedings of the 2014 IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments, pp. 9-16
Research Group : Centre for Computational Intelligence
Research Institute : Institute of Artificial Intelligence (IAI)
Peer Reviewed : Yes