A parallel reinforcement computing model for function optimization problems

Learning Automaton is a learning model with outstanding learning ability, autonomy and guaranteed convergence in the learning process. We propose a parallel computing model with learning automata for function optimization problems and implement it as a sparse distributed parallel computing system. The problems with the traditional reinforcement method using learning automata are: increased difficulty of the adjustment of learning parameters and that of convergence time, with an increased output number. To overcome these problems, we introduce a genetic algorithm (GA) to construct a search space with reduced dimension to search for the optimal output from the entire output space, and provide an efficient way of searching for the smaller-sized search space for the optimal solution. The results of computer simulations verify the usefulness of the proposed method for multivariable function optimization problems.