Study on an Improved ACO Algorithm Based on Multi-Strategy in Solving Function Problem

In order to overcome the blindness of chaotic search, improve the convergence speed and global solving ability of the basic ant colony optimization(ACO) algorithm, an improved ACO algorithm based on combining multi-population strategy, adaptive adjustment pheromone strategy, chaotic search method and min-max ant strategy (MPCSMACO)is proposed in this paper. In the proposed MPCSMACO algorithm, the multi-population strategy is introduced to realize the information exchange and cooperation among the various types of ant colony. The chaotic search method with the ergodicity, randomness and regularity by using the logistic mapping is used to overcome too long search time, avoid falling into the local extremum in the initial stage and improve the search accuracy in the late search. The min-max ant strategy is used to avoid the local optimization solution and the stagnation. And the ants with different probability search different area according to the concentration of pheromone, so as to reduce the search number of the blindness of chaotic search method. Several Benchmark functions are selected to testify the performance of the MPCSMACO algorithm. The experiment results show that the MPCSMACO algorithm takes on the better global search ability and convergence performance.