Multi-objective genetic algorithm in reliability-based design optimization with sequential statistical modeling: an application to design of engine mounting

The present study explores reliability-based design optimization (RBDO) using multi-objective genetic algorithm (MOGA) in the context of practical engineering design. The reliability analysis is carried out using a most probable point-based method with first-order sensitivities. As an effective optimization approach, the two methods MOGA and RBDO are integrated to propose a multi-objective reliability-based design optimization (MORBDO). For considering the uncertainty propagation of reliable constraints, sequential statistical modeling is employed to estimate the appropriate distribution of probabilistic design variables. The present study also applies a multiresponse performance index based on the dimension reduction technique with a quality loss function. After verifying the MORBDO results using simulations on numerical problem, these techniques are applied to an automotive engine mounting system in the hierarchically decomposed problem of minimizing both the difference between the torque roll axis and the elastic roll axis and also the vibration transmissibility under mode purity and frequency requirements. The probabilistic design results indicate that the MORBDO results successfully identify more reliable and conservative optimized solutions than the deterministic results for a given reliability, and the failure probability is compared using a Monte Carlo simulation.