Abstract:
Considering the traditional ant colony algorithm in generating pheromone concentration, because the path generated by the algorithm may have redundant components, the pheromone concentration may not be able to correctly reflect the advantages and disadvantages of each node of the path, so that the ant cannot quickly find the optimal path according to the pheromone concentration, which makes the algorithm search slowly. Based on the idea of traditional ant colony algorithm, a new improved ant colony algorithm is proposed, that is, by introducing the concept of terminal distance index to replace the marking function of pheromone concentration, so that ants can rely on the index to make decisions and select good nodes. A 20×20 grid environment map is used to simulate the new improved ant colony algorithm. The experimental results show that the traditional ant colony algorithm and other improved ant colony algorithms need 43 generations and 34 generations to converge to the optimal value respectively, while the new improved ant colony algorithm only needs 3 generations to converge to the optimal solution, which proves the advantage of the algorithm in the speed of optimization. In order to further verify the superiority of this algorithm, for the more complex 30×30 grid model, the simulation results show that the convergence algebra of the traditional ant colony algorithm and other improved ant colony algorithm need 52 and 28 generations to converge to the optimal value respectively, while the new improved ant colony algorithm only needs 4 generations to converge to the optimal solution. In addition, in order to further verify the stability of the improved algorithm, the 30×30 environment model is simulated for 30 times, and the average convergence algebra is only 4.97 generations.