A New Approach for Optimal Clustering of Distributed Program's Call Flow Graph

Optimal clustering of call flow graph for reaching maximum concurrency in execution of distributable components is one of the NP-Complete problems. Both learning automatas (LAs) and genetic algorithms (GAs) are search tools which are used for solving many NP-Complete problems. In this paper a hybrid algorithm is proposed to optimal clustering of call flow graph and appropriate distributing of programs in network level. The algorithm uses both GAs and LAs simultaneously to search in state space. It has been shown that the speed of reaching to solution increases remarkably using LA and GA simultaneously in search process, and it also prevents algorithm from being trapped in local minimums. Experimental results show the superiority of proposed algorithm over others.