Selecting design variables and determining optimal hard-point coordinates are subjective in the traditional multiobjective optimization of geometric design of vehicle suspension, thereby usually resulting in poor overall suspension kinematic performance. To eliminate the subjectivity of selection, a method transferring multiobjective optimization function into a single-objective one through the integrated use of grey relational analysis ( GRA ) and improved entropy weight method ( IEWM ) is proposed. First, a comprehensive evaluation index of sensitivities was formulated to facilitate the objective selection of design variables by using GRA, in which IEWM was used to determine the weight of each subindex. Second, approximate models between the variations of the front wheel alignment parameters and the design variables were developed on the basis of support vector regression ( SVR ) and the fruit fly optimization algorithm ( FOA ) . Subsequently, to eliminate the subjectivity and improve the computational efficiency of multiobjective optimization ( MOO) of hard-point coordinates, the MOO functions were transformed into a single-objective optimization ( SOO) function by using the GRA-IEWM method again. Finally, the SOO problem was solved by the self-adaptive differential evolution ( jDE) algorithm. Simulation results indicate that the GRA-IEWM method outperforms the traditional multiobjective optimization method and the original coordinate scheme remarkably in terms of kinematic performance.

Qin Shi;Fei Zhang;Yikai Chen;Zongpin Hu

School of Automotive and Transportation Engineering,Hefei University of Technology,Hefei 230009,China;Anhui Domain Compute Co.,Ltd,Hefei 230000,China

哈尔滨工业大学学报（英文版）

[1]Qin Shi;Fei Zhang;Yikai Chen;Zongpin Hu-.Optimal Kinematics Design of MacPherson Suspension:Integrated Use of Grey Relational Analysis and Improved Entropy Weight Method)[J].哈尔滨工业大学学报（英文版）,2022(02):41-51

MacPherson,IEWM,subindex,SOO,jDE

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