Q-Learning Quantum Ant Colony Routing Algorithm for Micro-Nano Satellite Network

In order to solve the problem that the existing routing ant colony algorithm for micro-nano satellite network is easy to fall into local optimal solution and slow convergence speed, an improved quantum ant colony QoS (Quality of Service) routing algorithm using Q-learning is proposed in this paper. First, a qubit heuristic factor is added to the transfer probability of the ant colony algorithm to avoid falling into the local optimal solution. Then, the thought of Q-learning is introduced into the algorithm, and the pheromones of ant colony algorithm are mapped to the Q value of Q-learning, which accelerates the convergence speed of the algorithm. Simulation results show that the proposed routing algorithm can improve the packet delivery rate, reduce the average end-to-end delay and the average node energy consumption. The proposed Q-learning quantum ant colony routing algorithm is suitable for micro-nano satellite network with high speed mobile nodes.

[1]  G. G. Stokes.  "J." , 1890, The New Yale Book of Quotations.

[2]  Kashinath Basu,et al.  Modelling and Simulation of QoS-Aware Service Selection in Cloud Computing , 2020, Simul. Model. Pract. Theory.

[3]  Qin Yong,et al.  A Novel Quantum Ant Colony Algorithm Used for Campus Path , 2017, 22017 IEEE International Conference on Computational Science and Engineering (CSE) and IEEE International Conference on Embedded and Ubiquitous Computing (EUC).

[4]  Faezeh Farivar,et al.  A multi-objective ant colony optimization algorithm for community detection in complex networks , 2018, Journal of Ambient Intelligence and Humanized Computing.

[5]  Guoqing Xia,et al.  Global Path Planning for Unmanned Surface Vehicle Based on Improved Quantum Ant Colony Algorithm , 2019, Mathematical Problems in Engineering.

[6]  Jin Zhang,et al.  Inter-satellite routing algorithm by searching the global neighborhood for dynamic inter-satellite networks , 2018, 2018 Tenth International Conference on Advanced Computational Intelligence (ICACI).

[7]  Abhisek Ukil,et al.  Q-learning and Dynamic Fuzzy Q-learning Based Intelligent Controllers for Wind Energy Conversion Systems , 2018, 2018 IEEE Innovative Smart Grid Technologies - Asia (ISGT Asia).

[8]  Hassan Mathkour,et al.  Enhancement of Ant Colony Optimization for QoS-Aware Web Service Selection , 2019, IEEE Access.

[9]  Ruofei Ma,et al.  Dynamic Satellite Network Routing Algorithm Based on Link Scheduling , 2018, CSPS.

[10]  Qin Wang,et al.  Design of Trusted Security Routing in Wireless Sensor Networks Based on Quantum Ant Colony Algorithm , 2017, Int. J. Online Eng..

[11]  Miss A.O. Penney.  (b) , 1974, The New Yale Book of Quotations.

[12]  Yang Liu,et al.  Communication System Fast Reconstruction Strategy and Efficiency Simulation Based on Micro-Nano Satellites , 2018, 2018 Eighth International Conference on Instrumentation & Measurement, Computer, Communication and Control (IMCCC).

[13]  Jia Hao,et al.  Research on Construction of Batch Intelligent Production Line for Micro/Nano Satellite , 2020 .

[14]  Jiang Wu,et al.  The QoS routing mechanism based on key frame and improved ant colony optimization algorithm for wireless multimedia sensor networks , 2018, J. Comput. Methods Sci. Eng..