Bilevel Multi-Objective Gray Wolf Algorithm Based on Packet Transport Network Optimization - Chunzhi Wang; Xing Li; Zaoning Wang (IEEE Access, 2021)
Packet transport network (PTN), as an efficient transmission network technology in mobile communications in the big data era, is used by more and more communication operators. The existing PTN resource utilization rate is low, the network security is poor, so the existing PTN needs to be optimized in all aspects. For the optimization of the PTN, it is necessary to consider the decision of both the operator user and the service product supplier. Therefore, this paper proposes a bilevel multi-objective gray wolf algorithm based on PTN optimization problem. The operator user is the upper-level decision maker, and the objective function is to pay the product supplier the lowest cost. The product supplier is the lower-level decision maker, it mainly includes two major objective functions. The first objective function is to maximize the Label switching path overlap rate(LSPOR) evaluation score to solve the abnormal Label Switching Path (LSP) problem in the network, and the second is to maximize the committed bandwidth with utilizing rate(CBWUR) evaluation score to solve the problem of excessive Committed Information Rate(CIR) bandwidth usage in the network. According to the three scale network situation in Hubei, China, the improved multi-objective gray wolf algorithm is used to solve the PTN bilevel programming problem. The experimental results show that compared with the initial network, the optimized network size dropped by 125314 hops on average, the LSPOR increased by 13.64%, and the CBWUR increased by 3.7%. This model not only improves the utilization of network resources, but also reduces the cost to be paid by superior decision makers.