LAUSR

To solve the problems of local optimum, slow convergence speed and low search efficiency in ant colony algorithm, an improved ant colony optimization algorithm is proposed. The unequal allocation initial pheromone is constructed to avoid the blindness search at early planning. A pseudo-random state transition rule is used to select path, the state transition probability is calculated according to the current optimal solution and the number of iterations, and the proportion of determined or random selections is adjusted adaptively. The optimal solution and the worst solution are introduced to improve the global pheromone updating method. Dynamic punishment method is introduced to solve the problem of deadlock. Compared with other ant colony algorithms in different robot mobile simulation environments, the results showed that the global optimal search ability and the convergence speed have been improved greatly and the number of lost ants is less than one-third of others. It is verified the effectiveness and superiority of the improved ant colony algorithm.