Vectorized candidate selection for parallel ant colony optimization

Joshua Peake, Martyn Amos, Paris Yiapanis, Huw Lloyd

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

5 Citations (Scopus)

Abstract

Ant Colony Optimization (ACO) is a well-established nature-inspired heuristic, and parallel versions of the algorithm now exist to take advantage of emerging high-performance computing processors. However, careful attention must be paid to parallel components of such implementations if the full benefit of these platforms is to be obtained. One such component of the ACO algorithm is next node selection, which presents unique challenges in a parallel setting. In this paper, we present a new node selection method for ACO, Vectorized Candidate Set Selection (VCSS), which achieves significant speedup over existing selection methods on a test set of Traveling Salesman Problem instances.
Original languageEnglish
Title of host publicationGECCO '18 Proc. Genetic and Evolutionary Computation Conference Companion (GECCO '18), July 15-19 2018, Kyoto, Japan
Subtitle of host publicationProceedings of the Genetic and Evolutionary Computation Conference Companion
EditorsHernan Aquirre
PublisherACM
Pages1300-1306
Number of pages7
ISBN (Print)9781450357647
DOIs
Publication statusPublished - 6 Jul 2018
EventGenetic and Evolutionary Computation Conference 2018 - Kyoto, Japan
Duration: 15 Jul 201819 Jul 2018

Conference

ConferenceGenetic and Evolutionary Computation Conference 2018
Abbreviated titleGECCO '18
Country/TerritoryJapan
CityKyoto
Period15/07/1819/07/18

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