Multi-colony ant algorithms for the dynamic travelling salesman problem

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Authors

  • Michalis Mavrovouniotis
  • Shengxiang Yang
  • Xin Yao

Colleges, School and Institutes

External organisations

  • De Montfort University

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.

Details

Original languageEnglish
Title of host publication2014 IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments (CIDUE), Proceedings
Publication statusPublished - 12 Jan 2015
Event2014 IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments, CIDUE 2014 - Orlando, United States
Duration: 9 Dec 201412 Dec 2014

Conference

Conference2014 IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments, CIDUE 2014
CountryUnited States
CityOrlando
Period9/12/1412/12/14