Understanding the pheromone system within ant colony optimization

Stephen Gilmour*, Mark Dras

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

12 Citations (Scopus)

Abstract

Ant Colony Optimization (ACO) is a collection of metaheuristics inspired by foraging in ant colonies, whose aim is to solve combinatorial optimization problems. We identify some principles behind the metaheuristics' rules; and we show that ensuring their application, as a correction to a published algorithm for the vertex cover problem, leads to a statistically significant improvement in empirical results.

Original languageEnglish
Title of host publicationAI 2005: Advances in Artificial Intelligence
Subtitle of host publication18th Australian Joint Conference on Artificial Intelligence, Sydney, Australia, December 5-9, 2005. Proceedings
EditorsShichao Zhang, Ray Jarvis
Place of PublicationBerlin; Heidelberg
PublisherSpringer, Springer Nature
Pages786-789
Number of pages4
Volume3809 LNAI
ISBN (Electronic)9783540316527
ISBN (Print)3540304622, 9783540304623
DOIs
Publication statusPublished - 2005
Event18th Australian Joint Conference on Artificial Intelligence, AI 2005: Advances in Artificial Intelligence - Sydney, Australia
Duration: 5 Dec 20059 Dec 2005

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3809 LNAI
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other18th Australian Joint Conference on Artificial Intelligence, AI 2005: Advances in Artificial Intelligence
CountryAustralia
CitySydney
Period5/12/059/12/05

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