Multi-objective Ant Colony Algorithm Based on Pheromone Weight

This paper proposed multi-objective ant colony algorithm based on pheromone weight, which is used to solve multi-objective optimization problems. The algorithm introduces the weight of distance-related in the initialization of pheromones, which is beneficial to the ant speed up the path selection, improving the efficiency of ant search. At the same time, the adaptive variation operator that dynamically adjusts the number of ant neighbors with the number of iterations and the weight Tchebycheff aggregation method are also introduced, which are beneficial to improve the convergence speed and the quality of the algorithm. The algorithm has been compared with other related algorithms using Hypervolume and other indicators in the standard dual Traveling Salesman Problem (TSP), and has been proven that the improved algorithm has better results.