Optimal Design of Automobile Suspension Control Arm Using Metaheuristic Algorithms

Suspension systems in automobiles are composed of several key components, including control arms, springs, dampers, and stabilizer bars, all of which contribute to vehicle stability, ride comfort, and handling performance. Among these, the control arm is a critical structural element that connects the wheel hub to the chassis, allowing for controlled motion and load transfer. In this research, an advanced optimization methodology was employed to enhance the design of a control arm by reducing its weight without compromising its mechanical integrity. Both topology optimization and shape optimization techniques were applied to achieve a lightweight yet structurally robust design. To perform the optimization process effectively, the recently proposed Geyser Inspired Optimizer (GIO), a novel metaheuristic algorithm introduced in the literature, was utilized due to its superior exploration and exploitation capabilities. The primary objective was to minimize the mass of the control arm while strictly satisfying predefined stress constraints under operational loading conditions. In order to formulate accurate objective and constraint functions, Latin Hypercube Sampling (LHS) was used to generate a diverse set of design points. A surrogate modelling strategy based on the Radial Basis Function (RBF) was then employed to approximate the response surfaces, thereby reducing the computational cost of the optimization process. The optimal design obtained through this integrated approach successfully fulfilled all design constraints and achieved a 14.71% reduction in weight compared to the initial design. These promising results validate the efficiency and reliability of the proposed method and highlight its potential applicability in lightweight structural optimization of automotive components.