Aiming at the problem that ant colony optimization（ ACO） algorithm is easy to be trapped in local optimal and convergence speed is slow while solving traveling salesman problem（ TSP）

propose an adaptive ACO fuses an adaptive ant colony optimization algorithm based on hierarchical induction strategy（ HIACO）. Firstly

the classical ACO algorithm is run to find the local convergence iteration point

and the adaptive balance factor N was set according to this point to facilitate the population diversity and convergence speed of the adaptive balance algorithm. Then

classical ACO algorithm is used in the early stage of HIACO algorithm（ before N generations）. In the later stage of HIACO algorithm（ af

ter N generations）

a hierarchical induction strategy is introduced to increase the population diversity of the algorithm and accelerate the convergence speed of the algorithm. Finally

several groups of TSP problems in TSPLIB standard library are selected for simulation experiments to analyze algorithm performance. The experimental results show that this algorithm is not easy to fall into the local optimum

and has stronger optimization ability and faster convergence speed than the classical ACO

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