Comparison of Multi-Objective Optimization Results Using NSGA-Ⅱ Based on Military Wheeled Vehicle Endurance Test Data

This study aims to optimize the endurance test mode for military vehicles to more closely simulate actual operational conditions. To achieve this, a modified bi-objective optimization approach was developed, combining two system-level objectives for the relative damage of the chassis and powertrain with six corresponding component-level constraints. The modified bi-objective optimization employs the NSGA II(Non-dominated Sorting Genetic Algorithm II) to generate a diverse set of optimal solutions. The results, evaluated using the CRM(Coefficient of Residual Mass) and GMRAE(Geometric Mean Relative Absolute Error) metrics, compare this approach with bi-objective optimization focused solely on system-level objectives and six-objective optimization focused solely on component-level objectives. The findings show that the modified bi-objective approach provides a more accurate and reliable endurance test mode compared to both the bi-objective and six-objective methods.