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Traveling salesman problem (TSP) is a very hard and classical optimization problem in the field of operations research, and often-used benchmark for new optimization techniques. This paper will to bring up multi-agent approach for solving the TSP based on data mining algorithm, for the extraction of knowledge from a large set of TSP. The proposed approach supports the distributed solving to the TSP. It divides into three-tier, the first tier is ant colony optimization agent; the second-tier is genetic algorithm agent; and the third tier is fast local searching agent. In using an ant colony algorithm (ACA) for the TSP, an attribute-oriented induction methodology was used to explore the relationship between an operations' sequence and its attributes and a set of rules has been developed. These rules can duplicate the ACA's performance on identical problems. Ultimately, the experimental results have shown that the proposed hybrid approach has good performance with respect to the quality of solution and the speed of computation.