A mixed branch-and-bound and neural network approach for the broadcast scheduling problem

Authors:
Haixiang Shi;Lipo Wang
Affiliations:
School of Electrical and Electronic Engineering, Nanyang Technological University, Block S2, Nanyang Avenue, Singapore;School of Electrical and Electronic Engineering, Nanyang Technological University, Block S2, Nanyang Avenue, Singapore
Venue:
Design and application of hybrid intelligent systems
Year:
2003

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Abstract

In this paper we proposed a mixed method to solve the broadcast scheduling problem in packet radio networks. Due to the two objectives of this problem, a two-stage optimization process is adopted. In order to obtain a optimal time slot number, we use an exact method, branch-and-bound algorithm to search the whole solution space in the first stage and obtain the minimal TDMA cycle length. In the second stage, we use stochastic chaotic neural network to find the maximum node transmissions based on the fixed time slots obtained in previous stage. Results show that this mixed method outperforms previous approaches like Mean Filed Annealing, HNNGA, Sequential Vertex Coloring algorithm (SVC) and Gradually Neural Networks.