Research on Optimization of PTN Based on Bilevel Multi-objective Programming Learning

Abstract

The packet transport network (PTN) is becoming increasingly popular in mobile communication as a technology to provide efficient transmission. With the dramatic surge in user numbers, the PTN must be able to carry more. However, the existing PTN has low resource utilization and poor network security, Therefore, optimizing every aspect of the current PTN is essential. For optimizing PTN network, the decision of both carrier users and service product suppliers should be considered. When satisfying the decision of the product supplier, it is necessary to consider a number of evaluation indicators in the PTN, and there may be some correlation between the indicators. Hence, This study introduces a multi-objective optimization approach inspired by the Gray Wolf Algorithm and centered around the optimization of a PTN network. Carrier user is taken as the upper decision maker, and the goal is to pay the supplier with the lowest possible cost. Suppliers play a lower role in decision-making, considering the PTN Optimal performance, mainly including two goals, the first goal is to have the highest evaluation score of the LSPOR, and troubleshoot the label switched path (LSP) anomaly on the network. As a secondary objective, By maximizing chain bandwidth utilization ratio (CBWUR), we can resolve the problem of high committed information rate (CIR) bandwidth usage. Results show that the model allows services to use shorter paths, which improves resource usage and reduces costs for upper-level decision-makers. At the same time, the model can also improve the evaluation scores of LSPOR and CBWUR indicators, which improves the security of the PTN, reduces the cost of additional network resources, and optimizes the performance of the PTN to the greatest extent.

This paper primarily focuses on the following topics:

• 1. A proposed improvement in the multi-objective gray wolf algorithm involves enhancing the convergence factor through implementation of the sine and cosine functions to address the issue of slow convergence in the original algorithm.

• 2. Multi-objective optimization can be used to optimize the two primary indicators in the PTN: the active-standby corouting rate for the label switching path and the link committed information rate corresponds to that of the label switching path. In order to solve these multi-objective problems, the wolf algorithm is used, resulting in Pareto optimal solutions. As a result of the user’s selection of the most important two indicators, the most suitable solution is then selected.