Purpose. The traditional ant colony optimization algorithms have been used to solve the NP-hard problem, Traveling Salesman Problem (TSP), which is based on the rule that ants tend to choose high pheromone concentrated path. The max-min ant system (MMAS) most commonly achieves the nearest neighbouring city and always formulates the local optimal solution. Methodology. The "big-small ant colony" algorithm with a kind of "jump pit strategies" has been formulated. Where, the big ants can carry much more pheromones and are prone to making mistakes. Findings. First, the "big-small ant colony" algorithm was employed to accelerate the convergence speed. Then, by using a kind of jump pit concepts, a wider range path searching was provided, where the "small jumping strategy" allowed more than one ant to go along a different path, and the "big jumping strategy" put a barrier on the pheromone convergence path forcing the ants to choose other different paths. The experimental results showed that the modified algorithm always converges to the optimal results unlike the MMAS. Originality. The modified ant colony optimization algorithm was studied and the effectiveness of the idea, which was put forward, was discussed. Practical value. The proposed algorithm may be employed to solve other problems, especially together with some deterministic algorithm to realize quick global optimization.

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