STOC '87 Proceedings of the nineteenth annual ACM symposium on Theory of computing
A hierarchical system of learning automata that can learn the globally optimal path
Information Sciences: an International Journal
Learning automata with changing number of actions
IEEE Transactions on Systems, Man and Cybernetics
Learning automata: an introduction
Learning automata: an introduction
Interval elimination method for stochastic spanning tree problem
Applied Mathematics and Computation
A SubLinear Time Distributed Algorithm for Minimum-Weight Spanning Trees
SIAM Journal on Computing
Improvements in the time complexity of two message-optimal election algorithms
Proceedings of the fourth annual ACM symposium on Principles of distributed computing
A Distributed Algorithm for Minimum-Weight Spanning Trees
ACM Transactions on Programming Languages and Systems (TOPLAS)
STOC '04 Proceedings of the thirty-sixth annual ACM symposium on Theory of computing
An Evolutionary Approach to Solve Minimum Spanning Tree Problem with Fuzzy Parameters
CIMCA '05 Proceedings of the International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce Vol-2 (CIMCA-IAWTIC'06) - Volume 02
A Minimum Spanning Tree Approach to Line Image Analysis
ICPR '96 Proceedings of the 13th International Conference on Pattern Recognition - Volume 2
A near-optimal multicast scheme for mobile ad hoc networks using a hybrid genetic algorithm
Expert Systems with Applications: An International Journal
SFCS '85 Proceedings of the 26th Annual Symposium on Foundations of Computer Science
A Model and Algorithm for Minimum Spanning Tree Problems in Uncertain Networks
ICICIC '08 Proceedings of the 2008 3rd International Conference on Innovative Computing Information and Control
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
Computer Networks: The International Journal of Computer and Telecommunications Networking
Journal of Network and Computer Applications
A Learning Automata-Based Cognitive Radio for Clustered Wireless Ad-Hoc Networks
Journal of Network and Systems Management
A cellular learning automata-based algorithm for solving the vertex coloring problem
Expert Systems with Applications: An International Journal
A link stability-based multicast routing protocol for wireless mobile ad hoc networks
Journal of Network and Computer Applications
Wireless Personal Communications: An International Journal
The Journal of Supercomputing
A fast distributed approximation algorithm for minimum spanning trees
DISC'06 Proceedings of the 20th international conference on Distributed Computing
Learning in multilevel games with incomplete information. II
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A link stability-based multicast routing protocol for wireless mobile ad hoc networks
Journal of Network and Computer Applications
LLACA: An adaptive localized clustering algorithm for wireless ad hoc networks
Computers and Electrical Engineering
An adaptive learning automata-based ranking function discovery algorithm
Journal of Intelligent Information Systems
A learning automata-based solution to the target coverage problem in wireless sensor networks
Proceedings of International Conference on Advances in Mobile Computing & Multimedia
ELACCA: Efficient Learning Automata Based Cell Clustering Algorithm for Wireless Sensor Networks
Wireless Personal Communications: An International Journal
Wireless Personal Communications: An International Journal
Hi-index | 0.00 |
Due to the hardness of solving the minimum spanning tree (MST) problem in stochastic environments, the stochastic MST (SMST) problem has not received the attention it merits, specifically when the probability distribution function (PDF) of the edge weight is not a priori known. In this paper, we first propose a learning automata-based sampling algorithm (Algorithm 1) to solve the MST problem in stochastic graphs where the PDF of the edge weight is assumed to be unknown. At each stage of the proposed algorithm, a set of learning automata is randomly activated and determines the graph edges that must be sampled in that stage. As the proposed algorithm proceeds, the sampling process focuses on the spanning tree with the minimum expected weight. Therefore, the proposed sampling method is capable of decreasing the rate of unnecessary samplings and shortening the time required for finding the SMST. The convergence of this algorithm is theoretically proved and it is shown that by a proper choice of the learning rate the spanning tree with the minimum expected weight can be found with a probability close enough to unity. Numerical results show that Algorithm 1 outperforms the standard sampling method. Selecting a proper learning rate is the most challenging issue in learning automata theory by which a good trade off can be achieved between the cost and efficiency of algorithm. To improve the efficiency (i.e., the convergence speed and convergence rate) of Algorithm 1, we also propose four methods to adjust the learning rate in Algorithm 1 and the resultant algorithms are called as Algorithm 2 through Algorithm 5. In these algorithms, the probabilistic distribution parameters of the edge weight are taken into consideration for adjusting the learning rate. Simulation experiments show the superiority of Algorithm 5 over the others. To show the efficiency of Algorithm 5, its results are compared with those of the multiple edge sensitivity method (MESM). The obtained results show that Algorithm 5 performs better than MESM both in terms of the running time and sampling rate.