Two models of parallel ACO algorithms for the minimum tardy task problem

  • Authors:

  • Enrique Alba;Guillermo Leguizamon;Guillermo Ordonez

  • Affiliations:

  • Universidad de Malaga, Complejo Tecnologico, Campus de Teatinos, Malaga 29071, Spain.;Universidad Nacional de San Luis, Ejercito de Los Andes 950, San Luis 5700, Argentina.;Universidad Nacional de San Luis, Ejercito de Los Andes 950, San Luis 5700, Argentina

  • Venue:
  • International Journal of High Performance Systems Architecture
  • Year:
  • 2007

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Abstract

Ant Colony Optimisation (ACO) algorithms are intrinsically distributed algorithms where independent agents are in charge of building solutions collaboratively. Stigmergy or indirect communication is the way in which each agent learns from the experience of the whole colony. In this sense, explicit communication models of ACO can be defined directly giving birth to parallel algorithms of high numerical and real time efficiency. We do so in this work and apply the resulting algorithms to the Minimum Tardy Task Problem (MTTP), a scheduling problem that has been faced with other metaheuristics in the past. The aim of this paper is to report experimental results on the behaviour of two types of parallel ACO algorithms on large instances of the mentioned problem with the goal of improving existing solutions significantly.