Ant colony optimization is a new bionic intelligent optimization algorithm to mimics the foraging behavior of ants. Aiming at the problems of local optimum and slow convergence speed of ant colony optimization, we propose a bacteria foraging ant colony optimization algorithm by combining the bacterial foraging algorithm with the ant colony algorithm. In the iterative process of ant colony optimization, a reproduction process is introduced to the ant colony optimization to accelerate the convergence speed. A chemo taxis process is introduced to enhance the global searching ability. Simulation experiments on the classic traveling salesman problem and function optimization problem show that, compared with the traditional ant colony optimization and two improved ant colony optimization algorithms, the proposed algorithm is more effective in optimization capability, reliability, convergence efficiency and stability.