TY - JFULL AU - M. R. Aghaebrahimi and S. H. Zahiri and M. Amiri PY - 2009/2/ TI - A New Method for Multiobjective Optimization Based on Learning Automata T2 - International Journal of Computer and Information Engineering SP - 27 EP - 31 VL - 3 SN - 1307-6892 UR - https://publications.waset.org/pdf/2077 PU - World Academy of Science, Engineering and Technology NX - Open Science Index 25, 2009 N2 - The necessity of solving multi dimensional complicated scientific problems beside the necessity of several objective functions optimization are the most motive reason of born of artificial intelligence and heuristic methods. In this paper, we introduce a new method for multiobjective optimization based on learning automata. In the proposed method, search space divides into separate hyper-cubes and each cube is considered as an action. After gathering of all objective functions with separate weights, the cumulative function is considered as the fitness function. By the application of all the cubes to the cumulative function, we calculate the amount of amplification of each action and the algorithm continues its way to find the best solutions. In this Method, a lateral memory is used to gather the significant points of each iteration of the algorithm. Finally, by considering the domination factor, pareto front is estimated. Results of several experiments show the effectiveness of this method in comparison with genetic algorithm based method. ER -