A new multiple attribute decision making method based on preference and projection pursuit clustering model
Xiangyong Kong, Ruoping Li, Liqun Gao, Da Feng · Chinese Control Conference · 2011
A new combination assigning weight approach based on decision maker's preference and projection pursuit clustering model is proposed to overcome the shortages of subjective and objective assigning weight approaches. The multidimensional data are easily transformed into low dimensional space and the structural feature of multidimensional data can be revealed through applying projection pursuit clustering model in multiple attribute decision making problems. The optimum projection and the value of projection function can be obtained by the adaptive clustering differential evolution algorithm raised in this paper. The simulation results verify the validity and efficiency of this approach.