A comparative study of three GPU-based metaheuristics

Youssef S.G. Nashed*, Pablo Mesejo, Roberto Ugolotti, Jérémie Dubois-Lacoste, Stefano Cagnoni

*Corresponding author for this work

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

8 Scopus citations

Abstract

In this paper we compare GPU-based implementations of three metaheuristics: Particle Swarm Optimization, Differential Evolution, and Scatter Search. A GPU-based implementation, obviously, does not change the general properties of the algorithms. As well, we give for granted that GPU-based implementation of both algorithm and fitness function produces a significant speed-up with respect to a sequential implementation. Accordingly, the main goal of this work has been to fairly assess the efficiency of the GPU-based implementations of the three metaheuristics, based on the statistical analysis of the results they obtain in optimizing a benchmark of twenty functions within a prefixed limited time.

Original languageEnglish (US)
Title of host publicationParallel Problem Solving from Nature, PPSN XII - 12th International Conference, Proceedings
Pages398-407
Number of pages10
EditionPART 2
DOIs
StatePublished - 2012
Event12th International Conference on Parallel Problem Solving from Nature, PPSN 2012 - Taormina, Italy
Duration: Sep 1 2012Sep 5 2012

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume7492 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other12th International Conference on Parallel Problem Solving from Nature, PPSN 2012
CountryItaly
CityTaormina
Period9/1/129/5/12

Keywords

  • Differential Evolution
  • GPGPU
  • Global Continuous Optimization
  • Particle Swarm Optimization
  • Scatter Search

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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