Solving the Minimum Spanning Tree Problem in Stochastic Graphs Using Learning Automata

Authors:
J. Akbari Torkestani;M. R. Meybodi
Affiliations:
-;-
Venue:
ICIME '09 Proceedings of the 2009 International Conference on Information Management and Engineering
Year:
2009

Citing 0
Cited 5

Mobility-based multicast routing algorithm for wireless mobile Ad-hoc networks: A learning automata approach

Computer Communications
Clustering the wireless Ad Hoc networks: A distributed learning automata approach

Journal of Parallel and Distributed Computing
Clustering the wireless Ad Hoc networks: A distributed learning automata approach

Journal of Parallel and Distributed Computing
An intelligent backbone formation algorithm for wireless ad hoc networks based on distributed learning automata

Computer Networks: The International Journal of Computer and Telecommunications Networking
Weighted Steiner Connected Dominating Set and its Application to Multicast Routing in Wireless MANETs

Wireless Personal Communications: An International Journal

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, we propose some learning automata-based algorithms to solve the minimum spanning tree problem in stochastic graphs when the probability distribution function of the edge's weight is unknown. In these algorithms, at each stage a set of learning automata determines which edges to be sampled. This sampling method may result in decreasing unnecessary samples and hence decreasing the running time of algorithms. The proposed algorithm reduces the number of samples needs to be taken by a sample average approximation method from the edges of the stochastic graph. It is shown that by proper choice of the parameter of the proposed algorithms, the probability that the algorithms find the optimal solution can be made as close to unity as possible.