An approach for scalable parallel execution of ant algorithms

This paper presents an approach for the efficient parallel/distributed execution of ant algorithms, based on multi-agent systems. A very popular clustering problem, i.e., the spatially sorting of items belonging to a number of predefined classes, is taken as a use case. The approach consists in partitioning the problem space to a number of parallel nodes. Data consistency and conflict issues, which may arise when multiple agents concurrently access shared data, are transparently handled using a purposely developed notion of logical time. The developer remains in charge only of defining the behavior of the agents modeling the ants, without coping with issues related to parallel/distributed programming and performance optimization. Experimental results show that the approach is scalable and can be adopted to speed up the ant algorithm execution when the problem size is large, as may be in the case of massive data analysis and clustering.