A New Variance-Based Method for Solving Stochastic Graph Optimization Problem Using Learning Automata

In this paper, a new criterion is introduced for solving optimization problems on stochastic graphs- as a model of computer networks-by stochastic learning Automata. This proposed method, because of considering estimated variance of response of environment, can better adaptation to changes of environment. As a result, the proposed method can produce better response to learning Automata actions.
The proposed method, by entering a noise, can avoid learning Automata being stuck at a local optimum point. Our simulation shows that this proposed method can be improve the convergence rate of Automata-based algorithm.