Comparing parallelization of an ACO: message passing vs. shared memory

  • Authors:

  • Pierre Delisle;Marc Gravel;Michaël Krajecki;Caroline Gagné;Wilson L. Price

  • Affiliations:

  • Département d'informatique et de mathématique, Université du Québec à Chicoutimi, Chicoutimi, Québec, Canada;Département d'informatique et de mathématique, Université du Québec à Chicoutimi, Chicoutimi, Québec, Canada;Département de Mathématiques et Informatique, Université de Reims Champagne-Ardenne, Reims Cedex 2, France;Département d'informatique et de mathématique, Université du Québec à Chicoutimi, Chicoutimi, Québec, Canada;Faculté des Sciences de l'administration, Université Laval, Québec, Canada

  • Venue:
  • HM'05 Proceedings of the Second international conference on Hybrid Metaheuristics
  • Year:
  • 2005

Quantified Score

Hi-index 0.00

Visualization

Abstract

We present a shared memory approach to the parallelization of the Ant Colony Optimization (ACO) metaheuristic and a performance comparison with an existing message passing implementation. Our aim is to show that the shared memory approach is a competitive strategy for the parallelization of ACO algorithms. The sequential ACO algorithm on which are based both parallelization schemes is first described, followed by the parallelization strategies themselves. Through experiments, we compare speedup and efficiency measures on four TSP problems varying from 318 to 657 cities. We then discuss factors that explain the difference in performance of the two approaches. Further experiments are presented to show the performance of the shared memory implementation when varying numbers of ants are distributed among the available processors. In this last set of experiments, the solution quality obtained is taken into account when analyzing speedup and efficiency measures.