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

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.

Prediction of Urban Scale Expansion Based on Genetic Algorithm Optimized Neural Network Model

“…The algorithm integrates genetic algorithm and ant colony algorithm, rewards or punishes by comparing the smoothness of the search path and the best path, and trains the obtained optimal solution. Experimental results show that the algorithm effectively solves the network quality problem and improves the service quality to users [10].…”

[Retracted] The Problems of “Mental Health Trend” in the Ideological and Political Management of College Students Under the Network Environment

“…Sanjay et al explored and proposed to innovate the leadership mechanism, assessment and incentive mechanism, dynamic mechanism, operation mechanism, restriction mechanism, information feedback mechanism, environment mechanism, goal mechanism, work team construction, and guarantee mechanism in the ideological and political work mechanism of colleges and universities [ 11 ]. First of all, we should establish a static and innovative thinking mechanism, which should break through the rigid and dynamic thinking mechanism of people in colleges and universities; explore the ways of mechanism design from four mechanisms: discipline, curriculum management, teaching coordination, and multiple evaluation coordination [ 12 ].…”

An Ant Colony Optimization-Based Routing Algorithm for Load Balancing in LEO Satellite Networks

“…Currently, ant colony algorithms are widely used in networks, such as avoiding the interflow and intraflow interference or balancing the loads in backbone networks [19], calculating the best route for vehicles in vehicle networks [20,21], designing dynamic source routing algorithm meeting high QoS requirements in ad hoc networks [22], balancing energy efficiency for WSNs and IPv6 networks [23,24], and solving the scheduling (SBS) problem of satellite broadcasting between satellites and ground stations [25]. Wang et al proposed a load balancing scheme based on the ant colony algorithm (LBRA-CP) [18].…”