Two models of parallel ACO algorithms for the minimum tardy task problem. (20th April 2007)

• Record Type: Journal Article
• Authors: Alba, Enrique; Leguizamon, Guillermo; Ordonez, Guillermo
• Is Part Of: International journal of high performance systems architecture. Volume 1:Number 1(2007)
• Page Start: 50
• Page End: 59
• Publication Date: 2007-04-20
• Subjects: ant colony optimisation -- ACO -- parallel ant models -- minimum tardy task problem -- MTTP -- scheduling -- parallel algorithms; Computer architecture -- Periodicals; Computer systems -- Periodicals; High performance computing -- Periodicals; 004.205
• Languages: English
• ISSNs: 1751-6528

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.