Ant colony algorithm is a kind of heuristic search algorithms. It has been widely used to solve complex combinatorial optimization problems. Basic ant colony algorithm has some disadvantages, such as slow convergence and premature stagnation. In order to overcome these problems, we propose an improved ant colony algorithm, which is based on search concentration and dynamic pheromone updating. Specifically, by introducing the “Search Concentration” factor in the selection strategy, the algorithm can adaptively adjust the range of cities selected by the ants. In addition, increments of pheromone are dynamically changed and a kind of pheromone rollback mechanism is used. As a result, the search time are shortened and the algorithm is more easy to jump out of the local extremum. Simulation experimental results show that the improved algorithm has a faster convergence speed, improves the global understanding, and effectively avoids the algorithm falling into local optimum.