摘要
搭建了前围声学包多层级目标分解架构,提出GAPSO-RBFNN(genetic algorithm particle swarm optimization-radial basis function neural network)预测模型,并将其应用于多层级目标分解架构。将材料数据库、覆盖率、泄漏量作为优化的变量范围,以PBNR(power based noise reduction)均值作为约束,以质量和成本作为优化目标,采用非支配排序遗传算法(nondominated sorting genetic algorithm II,NSGA-II)进行多目标优化,得到Pareto多目标解集。并从中选取满足设计目标的最佳组合方案(材料组合、覆盖率、前围过孔密封方案选型)。结果显示,该模型最终的优化结果与实测结果接近,误差分别为0.35%,1.47%,1.82%,相较于初始声学包方案,优化后的结果显示,PBNR均值提升3.05%,其质量降低52.38%,成本降低15.15%,验证了所提方法的有效性和准确性。
Here,a multi-level target decomposition architecture for front wall acoustic package was built,a genetic algorithm particle swarm optimization-radial basis function neural network(GAPSO-RBFNN)prediction model was proposed,and it was used in multi-level target decomposition architecture.Material database,coverage,and leakage were taken as optimization variable ranges,PBNR(power-based noise reduction)mean was taken as constraints,weight and cost were taken as optimization objectives,and NSGA-II(nondominated sorting genetic algorithm II)was used to perform multi-objective optimization,and obtain Pareto multi-objective solution set,from which the best combination scheme including material combination,coverage and front wall through-hole sealing scheme selection was chosen to satisfy design objectives.The results showed that the final optimized results of the proposed model are close to the measured results,errors are 0.35%,1.47%and 1.82%,respectively;compared with the initial acoustic package scheme,after optimization,the average PBNR for front wall acoustic package increases by 3.05%,its weight decreases by 52.38%,and its cost decreases by 15.15%;the effectiveness and correctness of the proposed method are verified.
作者
杨帅
吴宪
薛顺达
YANG Shuai;WU Xian;XUE Shunda(School of Automotive Studies,Tongji University,Shanghai 201804,China;SINETAC Auto(Shanghai)Co.,Ltd.,Shanghai 201100,China)
出处
《振动与冲击》
北大核心
2025年第3期267-277,共11页
Journal of Vibration and Shock
关键词
GAPSO-RBFNN
声学包
PBNR
NSGA-II
Pareto多目标解集
genetic algorithm particle swarm optimization-radial basis function neural network(GAPSO-RBFNN)
acoustic package
power-based noise reduction(PBNR)
non-dominated sorting genetic algorithm II(NSGA-II)
pareto multi-objective solution set
作者简介
第一作者:杨帅,男,硕士生,1995年生;通信作者:吴宪,男,博士,副研究员,1971年生,E-mail:wuxian@tongji.edu.cn。
