Abstract: With the widespread use of active defense methods,dynamic variability has become a prominent feature of network systems.When discussing network system security,it is inevitable to base on dynamic network environment.Path prediction,as a common method of network security assessment,also needs to adapt to dynamic network environment and have the characteristics of continuous and efficient.In order to solve this problem,it is proposed to apply the ant colony optimization algorithm to the continuous path prediction of the network,and to design a simulation experiment to compare it with the completely random algorithm and the greedy algorithm in terms of optimization accuracy and optimization speed.The simulation experiment results show that the optimization accuracy of the original ant colony algorithm is not as good as the completely random algorithm,but due to the guidance of heuristic information,its optimization speed is much better than the completely random algorithm.In order to balance the advantages of the original ant colony algorithm and the completely random algorithm,a new ant colony pheromone update strategy is proposed,and a simulation experiment is designed to verify the efficiency of the algorithm.The final experimental results show that the improved ant colony optimization algorithm can better integrate the advantages of the original ant colony algorithm and the completely random algorithm,and achieve a balance between optimization accuracy and optimization speed.Howe-ver,it is necessary to continue to optimize the algorithm in the next research,so that it can better and more completely inherit the advantages of the original ant colony algorithm and the completely random algorithm,and achieve a high level both in accuracy and speed.

Key words: Ant colony optimization algorithm, Dynamic network, Path prediction, Simulation experiment