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.

中文翻译：

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IPv6环境下基于遗传蚁群的神经网络最优路由算法

传统的IPv6路由算法存在网络拥塞、节点能量消耗过多、网络生命周期缩短等问题。针对这一现象，我们提出了一种基于遗传蚁群的IPv6环境下的路由优化算法。算法对遗传算法和蚁群算法进行了系统的分析和研究。我们使用神经网络构建初始模型并结合QoS路由的约束。我们有效地将遗传算法和蚁群算法结合起来，最大限度地发挥各自的优势，并将其应用到IPv6网络中。同时，为了避免网络后期蚁群算法积累大量信息素，我们引入了抗拥塞奖惩机制。通过将搜索路径与最优路径进行比较，根据网络路径是否平滑进行奖励和惩罚。最后判断结果是否满足条件，将得到的最优解传递给BP神经网络进行训练；否则，需要迭代迭代，直到满足最优解。实验结果表明，该算法能够有效适应IPv6路由需求，能够有效解决用户对网络服务质量、网络性能等方面的需求。