关键词: 蚁群算法, 信息素初始化, 信息素增量, 挥发因子, 旅行商问题

Abstract: Aiming at the problems of slow convergence speed and easy to fall into local optimum in the early stage of ant colony algorithm, ant colony algorithm is improved. In the initialization stage, the suboptimal path is constructed by the greedy strategy and the pheromone on it is increased to realize the initial distribution of pheromones on different paths, so that pheromones can play a guiding role in the early stage of search and make ants tend to the optimal solution faster. In the process of iterative optimization, genetic mutation operation is introduced to mutate the optimal path after each iteration and try to find a better path to adjust the pheromone increment adaptively. While the algorithm inevitably falls into local optimization, pheromone rollback strategy is executed to adjust the volatilization factor dynamically according to the number of rollback to enhance the search ability and make the algorithm easier to jump out of local optimum. The simulation experiments show that the improved algorithm can accelerate the convergence speed and enhance the ability to jump out of the local optimum effectively.

Key words: ant colony algorithm, pheromone initialization, pheromone increment, pheromone volatilization, traveling salesman problem