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This paper studies the ant colony optimizer with parallel processing function based on adaptive resonance theory map. The optimizer has two groups of ants: local ants that is assigned to search in a subspace and global ants for global search. Effective communication between local and global ants is key to realize desired optimization. Applying the algorithm to typical bench marks, we can suggest that the optimizer can realize adaptive and fast search of solutions.
Date of Conference: 14-19 June 2009