Selection of initial designs for multi-objective optimization using classification and regression tree

  • Authors:

  • Lei Shi;Yan Fu;Ren-Jye Yang;Bo-Ping Wang;Ping Zhu

  • Affiliations:

  • State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, People's Republic of China 200240;Research and Advanced Engineering, Ford Motor Company, Dearborn, USA 48121;Research and Advanced Engineering, Ford Motor Company, Dearborn, USA 48121;Department of Mechanical and Aerospace Engineering, University of Texas at Arlington, Arlington, USA 76019;State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, People's Republic of China 200240

  • Venue:
  • Structural and Multidisciplinary Optimization
  • Year:
  • 2013

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Abstract

One of the major challenges for solving large-scale multi-objective optimization design problems is to find the Pareto set effectively. Data mining techniques such as classification, association, and clustering are common used in computer community to extract useful information from a large database. In this paper, a data mining technique, namely, Classification and Regression Tree method, is exploited to extract a set of reduced feasible design domains from the original design space. Within the reduced feasible domains, the first generation of designs can be selected for multi-objective optimization to identify the Pareto set. A mathematical example is used to illustrate the proposed method. Two industrial applications are used to demonstrate the proposed methodology that can achieve better performances in terms of both accuracy and efficiency.