Multi-colony ant algorithms for the dynamic travelling salesman problem

Date

2014-12

Advisors

Journal Title

Journal ISSN

ISSN

Volume Title

Publisher

IEEE Press

Type

Conference

Peer reviewed

Yes

Abstract

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.

Description

Keywords

Ant colony optimization, Dynamic travelling salesman problem

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

Rights

Research Institute

Institute of Artificial Intelligence (IAI)