Abstract
In this paper, we have presented the learning automata solution to the NP-hard problem, the graph partitioining problems (GPP), which involves partitioning the nodes of a graph G into K sets of equal size so as to minimize the sum of the costs of the edges having endpoints in different sets. We proposed a method for this problem, a parallel learning automata (LA) method. This method aimed at the parallel and solution accuracy of the GPP and applied local evaluation function. The simulation results show that, our learning automata based method has good performance on computing time and has some superiority with the increase in the number of nodes.
