An evolutionary approach to combinatorial optimization problems
CSC '94 Proceedings of the 22nd annual ACM computer science conference on Scaling up : meeting the challenge of complexity in real-world computing applications: meeting the challenge of complexity in real-world computing applications
New ideas in optimization
MACS-VRPTW: a multiple ant colony system for vehicle routing problems with time windows
New ideas in optimization
Analyzing synchronous and asynchronous parallel distributed genetic algorithms
Future Generation Computer Systems - Special issue on bio-impaired solutions to parallel processing problems
Journal of Heuristics
A parallel implementation of ant colony optimization
Journal of Parallel and Distributed Computing - Problems in parallel and distributed computing: Solutions based on evolutionary paradigms
Applying Evolutionary Algorithms to Combinatorial Optimization Problems
ICCS '01 Proceedings of the International Conference on Computational Science-Part II
Parallel Ant Colonies for Combinatorial Optimization Problems
Proceedings of the 11 IPPS/SPDP'99 Workshops Held in Conjunction with the 13th International Parallel Processing Symposium and 10th Symposium on Parallel and Distributed Processing
Information Exchange in Multi Colony Ant Algorithms
IPDPS '00 Proceedings of the 15 IPDPS 2000 Workshops on Parallel and Distributed Processing
An Island Model Based Ant System with Lookahead for the Shortest Supersequence Problem
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
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Ant Colony Optimization 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 meta-heuristics in the past. The aim of this article is to report experimental results on the behavior of three types of parallel ACO algorithms on large instances of the mentioned problems with the goal of improving existing solutions significantly.