New hybrid NSGA-III&SPEA/R to multi-object optimization in a half-car dynamic model

摘要

In this article, we conducted a new hybrid method between Non-dominated Sorting Genetic Algorithm II (NSGA-III) and SPEA/R (HNSGA-III&SPEA/R). This method is implemented to find the optimal values of the powertrain mount system stiffness parameters. This is the task of finding multi-objective optimization involving six simultaneous optimization goals: mean square acceleration and mean square displacement of the powertrain mount system. A hybrid HNSGA-III&SPEA/R has proposed with the integration of Strength Pareto evolutionary algorithm-based reference direction for Multi-objective (SPEA/R) and Many-objective optimization genetic algorithm (NSGA-III). Several benchmark functions are tested, and results reveal that the HNSGA-III&SPEA/R is more efficient than the typical SPEA/R and NSGA-III. Powertrain mount system stiffness parameters optimization with HNSGA-III&SPEA/R is simulated. It proved the potential of the HNSGA-III&SPEA/R for powertrain mount system stiffness parameter optimization problem.