Abstract: As a novel bionic evolutionary algorithm, the ant colony algorithm has been widely applicable to many complicated combinatorial optimization problems. However, the standard any colony algorithm often gets stock into premature stagnation after the iteration process. In this paper, through an analysis of the main reason of the premature stagnation phenomenon in the standard ant colony algorithm, we present a modified version of the algorithm by introducing the local pheromone, modifying the pheromones of the best route and the worst route, as well as making the parameter of the algorithm to be variant during its iteration and searching with a local optimization strategy. Experimental results for solving TSP problems with both the standard ant colony algorithm and the proposed one show that averagely speaking, the proposed method is much better in both the quality of the solution and the speed of convergence compared with the standard any colony algorithm.

Key words: ant colony algorithm, combinatorial optimization, TSP