Sidewall optimization of the subway car body based on bionic topology and MS-MOGWO

To achieve the lightweight design and improved performance of the subway car body, the sidewall was optimized using bionic topology and multi-strategy improved Multi-objective Gray Wolf Optimizer (MS-MOGWO). First, an initial honeycomb-like structure was proposed, and its mechanical performance was validated through quasi-static compressive experiment. Subsequently, a span-variable honeycomb-like bionic structure was proposed and applied to the ribbed plates of the sidewall after topology optimization. Sensitivity analysis was conducted on the modified sidewall components, and an approximate model was developed using a backpropagation (BP) neural network. Then, the MS-MOGWO is proposed, which incorporates Latin hypercubic sampling initialization population, nonlinear convergence factor adjustment strategy, and Levy flight strategy to improve the MOGWO. The superiority of MS-MOGWO was demonstrated through comparisons with MOGWO, IMOGWO, MOPSO, and NSGA-II. Subsequently, multi-objective sizing optimization of the sidewall, aiming at lightweight design and increasing the first-order bending modal frequency under the complete mass state, is conducted using the MS-MOGWO, and the optimal solution is selected by the improve TOPSIS method. Results show the sidewall mass decreased from 1.98 to 1.67 t (−15.66%), while the first-order bending modal frequency rose from 9.79 to 10.145 Hz (+3.63%), meeting the Chinese railway standard (>10 Hz). This method demonstrates significant potential for achieving lightweight design and enhanced performance in subway car body.