基于Q学习量子蚁群的微纳卫星路由算法

doi:10.19678/j.issn.1000-3428.0061845

摘要/Abstract

摘要： 在微纳卫星网络中，传统蚁群路由算法不能同时保证数据传输的安全性和网络业务的服务质量，且易陷入局部最优解，收敛速度较慢。为解决上述问题，提出一种实现多目标优化的Q学习量子蚁群路由算法。该算法在选择下一跳节点的转移概率时，将路径的平均信任值和路径的费用作为两个优化目标，构成最优路径的节点性能指标，保证数据传输的安全性和网络业务服务质量。在考虑路径费用函数时，将量子计算引入到状态转移概率计算中，避免陷入局部最优解，并在算法中引入Q学习的思想，将信息素映射成Q学习的Q值，强化算法在动态环境中的学习能力，以提高路由的整体性能。仿真结果表明，与蚁群优化算法和改进的蚁群多约束路由算法相比，Q学习量子蚁群路由算法明显改善包投递率、平均端到端时延和节点平均能耗等性能指标，避免了蚁群算法易陷入局部最优解，提高了收敛速度，可适用于具有高速移动节点的微纳卫星网络。

关键词: 多目标优化, 信任机制, Q学习, 量子计算, 蚁群算法, 微纳卫星网络

Abstract: In micro-nano-satellite networks, the traditional ant colony routing algorithm cannot guarantee data transmission security and the quality of service of network businesses simultaneously.Moreover, it is easy to fall into local optimal solution, and the convergence speed is slow.A Q-learning quantum ant colony routing algorithm for multiobjective optimization is developed in this study to solve these problems.In selecting the transition probability of the next-hop node, this algorithm considers two objectives(the average trust value of the path and the cost function) to ensure data transmission security and network service quality.When considering the path cost function, quantum computation is added to prevent obtaining the local optimal solution.The Q-learning approach is introduced into the algorithm, which maps the pheromone to the Q-value, the learning ability of the algorithm in dynamic environment is strengthened to improve the overall performance of routing.The simulation results show that compared to the chain algorithm, the ant colony optimization algorithm and the improved ant colony multiple-constrained-routine algorithm significantly improve the packet delivery ratio, average end-to-end delay. Moreover, the node average performance metrics, such as energy consumption, prevent the ant colony algorithm from falling into the local optimal solution and speed up the convergence speed.The proposed algorithm can be applied to high-speed mobile node micro-nano-satellite networks.

Key words: multi-objective optimization, trust mechanism, Q-learning, quantum computing, ant colony algorithm, micro-nano-satellite networks

中图分类号:

TN929.52

张然, 高莹雪, 赵钰, 丁元明. 基于Q学习量子蚁群的微纳卫星路由算法[J]. 计算机工程, 2022, 48(3): 162-169,188.

ZHANG Ran, GAO Yingxue, ZHAO Yu, DING Yuanming. Routing Algorithm for Micro-Nano-Satellite Based on Q-Learning Quantum Ant Colony[J]. Computer Engineering, 2022, 48(3): 162-169,188.

http://www.ecice06.com/CN/Y2022/V48/I3/162

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