Neural Network Optimal Routing Algorithm Based on Genetic Ant Colony in IPv6 Environment

• Published in: Computational intelligence and neuroscience Vol. 2021; no. 1; p. 3115704
• Main Authors: Ni, Weichuan, Xu, Zhiming, Zou, Jiajun, Wan, Zhiping, Zhao, Xiaolei
• Format: Journal Article
• Language: English
• Published: United States Hindawi 2021; John Wiley & Sons, Inc
• Subjects: Algorithms; Analysis; Ant colony optimization; Back propagation networks; Chromosomes; Constraint modelling; Cycling; Energy consumption; Genetic algorithms; Genetic research; IP (Internet Protocol); Iterative methods; Life cycles; Mathematical optimization; Neural networks; Neural Networks, Computer; Optimization; Pheromones; Population; Protocol; Punishment; Quality of service; Quality of service architectures; Reinforcement; Traffic congestion; Transmission Control Protocol/Internet Protocol (Computer network protocol); Wireless networks
• ISSN: 1687-5265; 1687-5273; 1687-5273
• DOI: 10.1155/2021/3115704

The traditional IPv6 routing algorithm has problems such as network congestion, excessive energy consumption of nodes, and shortening the life cycle of the network. In response to this phenomenon, we proposed a routing optimization algorithm based on genetic ant colony in IPv6 environment. The algorithm analyzes and studies the genetic algorithm and the ant colony algorithm systematically. We use neural network to build the initial model and combine the constraints of QoS routing. We effectively integrate the genetic algorithm and ant colony algorithm that maximize their respective advantages and apply them to the IPv6 network. At the same time, in order to avoid the accumulation of a lot of pheromones by the ant colony algorithm in the later stage of the network, we have introduced an anticongestion reward and punishment mechanism. By comparing the search path with the optimal path, rewards and punishments are based on whether the network path is smooth or not. Finally, it is judged whether the result meets the condition, and the optimal solution obtained is passed to the BP neural network for training; otherwise, iterative iterations are required until the optimal solution is satisfied. The experimental results show that the algorithm can effectively adapt to the IPv6 routing requirements and can effectively solve the user’s needs for network service quality, network performance, and other aspects.