关键词: 蚁群算法, 聚度, 混沌优化, 自适应信息素更新, 旅行商问题

Abstract: Aiming at the problem that the ant colony algorithm is slow in convergence and easily falls into a local optimum, an adaptive dynamic chaotic ant colony algorithm（A_ACS） based on the degree of aggregation is proposed. In the early iterations, the degree of aggregation is used to measure the diversity of solutions and self-adaptively adjust the local pheromone distribution, and chaos operators are introduced to increase the diversity of the population to avoid the algorithm falling into a local optimum, thereby improving the accuracy of the solution. In the later iterations, the chaotic operator is removed to reduce the chaotic disturbance and increase the convergence speed of the algorithm. The A_ACS is used for the TSP problem. The simulation results show that the proposed algorithm reduces the search time and improves the quality of solution compared with the ACS and MMAS algorithm. It balances the contradiction between diversity and convergence, and the overall performance is better than the other two algorithms.

Key words: ant colony algorithm, degree of aggregation, chaotic optimization method, adaptive method of updating pheromone, traveling salesman problem