基于改进蝠鲼觅食算法的汽车前桥轻量化优化

LIGHTWEIGHT OPTIMIZATION OF THE AUTOMOBILE FRONT AXLE BASED ON THE IMPROVED MANTA RAY FORAGING ALGORITHM

汽车前桥结构是汽车的核心部件之一,在汽车设计中具有举足轻重的地位。为提高汽车前桥轻量化优化的收敛速度和精度,提出一种基于蝠鲼自身防卫策略改进的蝠鲼算法。采用六个经典的测试函数对改进蝠鲼算法进行性能测试验证,结果表明改进的蝠鲼算法具有良好的收敛速度和收敛精度。在此基础上,运用改进的蝠鲼算法对汽车前桥进行轻量化优化设计,优化结果表明经过94次迭代之后可以获得最优解,汽车前桥优化后的总质量从51.95 kg降低为43.24 kg,降低了16.75%。通过分析经典测试函数和汽车前桥案例的结果可知,改进的蝠鲼算法是一种高效的优化算法,对以后的工程优化问题和算法改进具有参考意义。

Automobile front axle structure is one of the core components of an automobile,which plays a decisive role in the automobile design.In order to improve the convergence speed and accuracy of the automobile front axle lightweight,an improved manta ray algorithm based on manta ray self-defense strategy is proposed.Six classical benchmark functions are used to verify the performance of the improved manta ray algorithm.Results show that the improved manta ray algorithm has competitive convergence speed and convergence accuracy.On this basis,the improved manta ray algorithm is used to optimize the lightweight design of the automobile front axle.The optimization results show that the optimal solution can be obtained after 94 iterations,and the total mass of the automobile front axle after optimization is reduced from 51.95 kg to 43.24 kg,a decrease of 16.75%.By analyzing the results of classical benchmark functions and automobile front axle cases,it can be seen that the improved manta ray algorithm is an efficient optimization algorithm,which provides reference for future engineering optimization problems and algorithm improvements.