@article{(Open Science Index):https://publications.waset.org/pdf/2077,
	  title     = {A New Method for Multiobjective Optimization Based on Learning Automata},
	  author    = {M. R. Aghaebrahimi and  S. H. Zahiri and  M. Amiri},
	  country	= {},
	  institution	= {},
	  abstract     = {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.},
	    journal   = {International Journal of Computer and Information Engineering},
	  volume    = {3},
	  number    = {1},
	  year      = {2009},
	  pages     = {28 - 31},
	  ee        = {https://publications.waset.org/pdf/2077},
	  url   	= {https://publications.waset.org/vol/25},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 25, 2009},
	}