Abstract
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.
Highlights
With the rapid development of the Internet and the continuous increase in the number of network users, users’ requirements for network service quality and network performance are increasing
In the IPv6 network environment, users have higher requirements for network security, network quality of service (QoS), and so on. is requires routing algorithms running in the IPv6 protocol environment to have better security, reliability, and higher performance. e routing protocols used in IPv6 networks are mainly the routing protocol based on distance vector and the routing protocol based on link state [5, 6]
We propose a genetic ant colony routing optimization selection algorithm in the IPv6 environment. e routing algorithm can comprehensively use the constraints of QoS routing to solve the optimal route and systematically analyze the genetic algorithm and the ant colony algorithm, effectively integrate and maximize their respective advantages, and select the best route in the IPv6 network
Summary
With the rapid development of the Internet and the continuous increase in the number of network users, users’ requirements for network service quality and network performance are increasing. We have systematically analyzed and researched the principles of the genetic algorithm and ant colony algorithm [7,8,9,10] Based on it to improve and optimize, we use neural network. We propose a genetic ant colony routing optimization selection algorithm in the IPv6 environment. E routing algorithm can comprehensively use the constraints of QoS routing to solve the optimal route and systematically analyze the genetic algorithm and the ant colony algorithm, effectively integrate and maximize their respective advantages, and select the best route in the IPv6 network. When the search path in the search algorithm is relatively smooth, we will reward it 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. Speed up the search speed of the algorithm while reducing unnecessary searches, so that the algorithm can still maintain a high optimization ability even in an environment where there is a “dead road” in the link
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