蚁群算法在动态网络持续性路径预测中的运用及仿真

Application and Simulation of Ant Colony Algorithm in Continuous Path Prediction of Dynamic Network

随着主动防御手段的广泛运用,动态多变性成为了网络系统的显著特征,在讨论了网络系统安全性时不可避免地需要以动态网络环境为基础,路径预测作为网络安全评估的常用方法,也需要适应动态网络环境以具备持续高效的特性。为了解决这个问题,提出将蚁群优化算法运用到网络持续性路径预测中,并设计仿真实验,在寻优精度和寻优速度两个方面,将所提方法与完全随机算法和贪婪算法进行比较。仿真实验结果表明,原始蚁群算法的寻优精度不如完全随机算法,但由于启发式信息的引导,其寻优速度远优于完全随机算法。为了均衡原始蚁群算法和完全随机算法各自的优势,提出新的蚁群信息素更新策略,并再次设计仿真实验验证算法的寻优效率。最终的实验结果显示,改进后的蚁群优化算法能够较好地综合原始蚁群算法和完全随机算法的优点,达到寻优精度和寻优速度的均衡。然而,在下一步的研究中还需要继续进行算法优化,使其能够更好、更完全地继承两者的优点,实现精度和速度兼优。

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.