Helical compression springs are widely utilized in numerous applications, such as automotive suspension systems, due to their remarkable features; the designers focus on three parameters: the wire diameter, mean coil diameter, and number of active coils due to their impact on spring performance. This research proposes a novel approach offering unused optimization algorithms. In contrast, previous works focusing on solely one or multiobjectives, such as minimizing weight and enhancing the fatigue life using traditional algorithms, besides ANSYS, inventor, and experimental validation, the present work highlights a multiobjectives approach for unexplored targets utilizing SolidWorks simulation for validation. Therefore, it is aimed at minimizing the spring weight of a Sedan vehicle, besides enhancing fatigue life and coil clearance using MATLAB algorithms for multiobjectives optimization including grey wolf (GWO), red fox (RFO), modified grey wolf, and hybrid‐modified grey wolf–red fox algorithms along with their validation to confirm the stress and deformation properties besides the required objectives under nonlinear loading constraints. The results indicate that the reduction in weight increased from 37% to 47%, besides achieving 1.556 as a safety factor for hybrid MGWO with 1.735 in the FEM study; furthermore, coil clearance improved by 103%–236% for RFO and hybrid MGWO, respectively. The optimized parameters improved and were within the design range. Other results, such as buckling risk and spring index, were validated, and they found that stable and within the acceptable range, respectively. The proposed design methodology is successful in terms of integration between simulation and optimization study.
Mahmood et al. (Thu,) studied this question.