Multi-colony ant algorithms for the dynamic travelling salesman problem

Michalis MAVROVOUNIOTIS, Shengxiang YANG, Xin YAO

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Researchpeer-review

28 Citations (Scopus)

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. © 2014 IEEE.
Original languageEnglish
Title of host publicationIEEE SSCI 2014: 2014 IEEE Symposium Series on Computational Intelligence - CIDUE 2014: 2014 IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments, Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages9-16
Number of pages8
ISBN (Print)9781479945160
DOIs
Publication statusPublished - Dec 2014
Externally publishedYes

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