A Data Flow Implementation of Agent-based Distributed Graph Search

I. Hamchi, M. Hoarau, A. Fillinger, N. Crouzier, L. Diduch, M. Michel, and V. Stanford (USA)


NIST Data Flow System, Ant Colony Optimization(ACO), Parallel Distributed Processing, combinatorialoptimization, parallel graph search.


Biological ants organize themselves into forager groups that converge to shortest paths to and from food sources. This has motivated development of a large class of biologically inspired agent-based graph search techniques, called Ant Colony Optimization, to solve diverse combinatorial problems. Our approach to parallel graph search uses multiple ant agent populations distributed across processors and clustered computers to solve large scale graph search problems. We discuss our implementation using the NIST Data Flow System II, and show good scalability of our parallel search algorithm.

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