Finding the Shortest Path in Stochastic Graphs Using Learning Automata and Adaptive Stochastic Petri Nets

TLDR: This paper proposes a novel learning automata-based algorithm for this problem which can speed up the process of finding the shortest path using parallelism.

Abstract: Shortest path problem in stochastic graphs has been recently studied in the literature and a number of algorithms has been provided to find it using varieties of learning automata models However, all these algorithms suffer from two common drawbacks: low speed and lack of a clear termination condition In this paper, we propose a novel learning automata-based algorithm for this problem which can speed up the process of finding the shortest path using parallelism For this parallelism, several traverses are initiated, in parallel, from the source node towards the destination node in the graph During each traverse, required times for traversing from the source node up to any visited node are estimated The time estimation at each visited node is then given to the learning automaton residing in that node Using different time estimations provided by different traverses, this learning automaton gradually learns which neighbor of the node is on the shortest path To set a condition for the termination of the