The Journal of Supercomputing
Ant algorithms for discrete optimization
Artificial Life
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
Parallelization Strategies for Ant Colony Optimization
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Ant colony system: a cooperative learning approach to the traveling salesman problem
IEEE Transactions on Evolutionary Computation
An analysis of communication policies for homogeneous multi-colony ACO algorithms
Information Sciences: an International Journal
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This paper describes the work of an object oriented framework designed to be used in the parallelization of a set of related algorithms. The idea behind the system we are describing is to have a re-usable framework for running several sequential algorithms in a parallel environment. The algorithms that the framework can be used with have several things in common: they have to run in cycles and the work should be possible to be split between several "processing units". The parallel framework uses the message-passing communication paradigm and is organized as a master-slave system. Two applications are presented: an Ant Colony Optimization (ACO) parallel algorithm for the Travelling Salesman Problem (TSP) and an Image Processing (IP) parallel algorithm for the Symmetrical Neighborhood Filter (SNF). The implementations of these applications by means of the parallel framework prove to have good performances: approximatively linear speedup and low communication cost.