Optimization of cultural algorithms structure based on pareto ranking

The progress of generation of human being cannot be completely dependent upon genetics changes. Human interactions, social behavior and other factors have important role in optimization process. As social interactions allow rapid adjustment and progress compared to genetics, an optimization algorithm can be including social factors in convergence speed. These attributes can be transferred from one generation to another as genetic code later. Such attributes by the culture and algorithms being used are called cultural algorithms. One of the shortcomings of the algorithms is forming a culture and following all the people of the culture involving the local optimum during the evolution period. To remove this problem in this paper, a method called Pareto ranking was used to select the leaders and increasing the variety in the generations of this algorithm. The results referred to the increase of convergence speed of new combination method to its standard type.