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

Weichuan Ni1, Zhiming Xu1, Jiajun Zou1

  • 1Guangzhou Xinhua University, Guangzhou, China.

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This study introduces a novel genetic ant colony routing algorithm for IPv6 networks, enhancing network performance and service quality by optimizing paths and reducing congestion.

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Area of Science:

  • Computer Science
  • Network Engineering
  • Artificial Intelligence

Background:

  • Traditional IPv6 routing faces challenges including network congestion, high node energy consumption, and reduced network lifespan.
  • Existing routing algorithms struggle to meet the demands for quality of service (QoS) and efficient network operation.

Purpose of the Study:

  • To propose and evaluate a novel routing optimization algorithm for IPv6 networks.
  • To address limitations of traditional IPv6 routing by integrating genetic and ant colony algorithms.
  • To improve network performance, energy efficiency, and service quality.

Main Methods:

  • Developed a routing optimization algorithm combining genetic algorithm (GA) and ant colony optimization (ACO) for IPv6 environments.
  • Integrated QoS routing constraints and utilized a neural network for initial model building and training.
  • Implemented an anti-congestion reward and punishment mechanism to prevent pheromone accumulation and guide path selection.
  • Employed a BP neural network for training and iterative refinement of the optimal solution.

Main Results:

  • The proposed algorithm effectively optimizes routing paths in IPv6 networks.
  • Demonstrated significant improvements in network service quality and overall performance.
  • Successfully mitigated network congestion and enhanced node energy efficiency.

Conclusions:

  • The genetic ant colony algorithm offers an effective solution for IPv6 routing challenges.
  • The algorithm meets user demands for improved network service quality and performance.
  • This approach provides a robust and adaptive routing strategy for modern networks.