Ant Colony Optimization
Fundamentals of Computational Swarm Intelligence
Fundamentals of Computational Swarm Intelligence
An Approach to Collaboration of Growing Self-Organizing Maps and Adaptive Resonance Theory Maps
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
ART-based parallel learning of growing SOMs and its application to TSP
ICONIP'06 Proceedings of the 13 international conference on Neural Information Processing - Volume Part I
Parallel ant colony optimizers with local and global ants
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
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This paper studies a parallel ant colony optimizer and its application to the traveling sales person problems. The parallel processing is based on the adaptive resonance theory map that divide the input space into subspaces. The ants are classified into two types: local ant for local search within either subspace and global ant for search of whole input space. Communication between local and global ants is a key for effective parallel processing. Applying the algorithm to basic bench marks, we can suggest that our algorithm realize fast and reasonable search.