Multiobjective discrete optimization algorithm based on Entropy-AHP weighted TOPSIS for pedestrian lower limb protection

Globally, the tibia, femur, and knee joints of the lower limbs frequently sustain severe injuries in vehicle-to-vulnerable road user (VRU) accidents. The design of vehicle structures is crucial for absorbing impact energy and reducing VRU injuries. The advanced pedestrian legform impactor (APLI) offers more precise biomechanical performance for evaluating lower limb injuries compared to other legform impactors. Traditional structural optimization methods often overlook the discrete nature of vehicle structures and the complexity of biomechanical models, where injury responses are highly nonlinear. This study investigates a multiobjective discrete optimization (MODO) algorithm tailored for vehicle structures in APLI impacts. The proposed MODO algorithm integrates a multiple attribute decision making (MADM) model, using the TOPSIS method, with weight coefficients derived from Entropy and AHP methods. Iterative refinement through successive orthogonal experiments efficiently manages numerous design variables and levels. The results demonstrate that the MODO algorithm achieves optimal design by integrating seamlessly with the MADM model. Additionally, this algorithm shows significant potential for addressing other complex engineering challenges.