A method to improve the low-velocity impact performance of composite laminate is proposed, and a multi-island genetic algorithm is used for the optimization of composite laminate stacking sequence under low-velocity i...A method to improve the low-velocity impact performance of composite laminate is proposed, and a multi-island genetic algorithm is used for the optimization of composite laminate stacking sequence under low-velocity impact loads based on a 2D dynamic impact finite element analysis. Low-velocity impact tests and compression-after impact（CAI） tests have been conducted to verify the effectiveness of optimization method. Experimental results show that the impact damage areas of the optimized laminate have been reduced by 42.1% compared to the baseline specimen, and the residual compression strength has been increased by 10.79%, from baseline specimen 156.97 MPa to optimized 173.91 MPa. The tests result shows that optimization method can effectively enhance the impact performances of the laminate.展开更多
In this paper a hybrid parallel multi-objective genetic algorithm is proposed for solving 0/1 knapsack problem. Multi-objective problems with non-convex and discrete Pareto front can take enormous computation time to ...In this paper a hybrid parallel multi-objective genetic algorithm is proposed for solving 0/1 knapsack problem. Multi-objective problems with non-convex and discrete Pareto front can take enormous computation time to converge to the true Pareto front. Hence, the classical multi-objective genetic algorithms (MOGAs) (i.e., non- Parallel MOGAs) may fail to solve such intractable problem in a reasonable amount of time. The proposed hybrid model will combine the best attribute of island and Jakobovic master slave models. We conduct an extensive experimental study in a multi-core system by varying the different size of processors and the result is compared with basic parallel model i.e., master-slave model which is used to parallelize NSGA-II. The experimental results confirm that the hybrid model is showing a clear edge over master-slave model in terms of processing time and approximation to the true Pareto front.展开更多
高效率的设计出大消声量的消声器一直是车辆排气噪声控制中面临的难题。考虑到消声器优化过程中涉及参数较多,在消声器传递损失数值建模的基础上,采用试验设计(DOE)中的拉丁超立方设计对消声器参数进行分析,结合多岛遗传算法(MIGA)和传...高效率的设计出大消声量的消声器一直是车辆排气噪声控制中面临的难题。考虑到消声器优化过程中涉及参数较多,在消声器传递损失数值建模的基础上,采用试验设计(DOE)中的拉丁超立方设计对消声器参数进行分析,结合多岛遗传算法(MIGA)和传统遗传算法(GA)分别建立消声器在排气噪声单峰值频率和多峰值频率处的传递损失为目标的优化模型,开展消声器传递损失优化设计研究。结果表明:DOE方法能有效的辨识出各参数对消声器传递损失影响的大小,简化了消声器的优化模型。MIGA对消声器在单峰值频率和多峰值频率的优化都优于GA,且多峰值频率的优化好于单峰值频率的优化,能使排气噪声最大降低20.98 d B。展开更多
基金Funded by the National Natural Science Foundation of China(No.51275393)the Fundamental Research Funds for the Central Universities(No.xjj2017160)
文摘A method to improve the low-velocity impact performance of composite laminate is proposed, and a multi-island genetic algorithm is used for the optimization of composite laminate stacking sequence under low-velocity impact loads based on a 2D dynamic impact finite element analysis. Low-velocity impact tests and compression-after impact（CAI） tests have been conducted to verify the effectiveness of optimization method. Experimental results show that the impact damage areas of the optimized laminate have been reduced by 42.1% compared to the baseline specimen, and the residual compression strength has been increased by 10.79%, from baseline specimen 156.97 MPa to optimized 173.91 MPa. The tests result shows that optimization method can effectively enhance the impact performances of the laminate.
文摘In this paper a hybrid parallel multi-objective genetic algorithm is proposed for solving 0/1 knapsack problem. Multi-objective problems with non-convex and discrete Pareto front can take enormous computation time to converge to the true Pareto front. Hence, the classical multi-objective genetic algorithms (MOGAs) (i.e., non- Parallel MOGAs) may fail to solve such intractable problem in a reasonable amount of time. The proposed hybrid model will combine the best attribute of island and Jakobovic master slave models. We conduct an extensive experimental study in a multi-core system by varying the different size of processors and the result is compared with basic parallel model i.e., master-slave model which is used to parallelize NSGA-II. The experimental results confirm that the hybrid model is showing a clear edge over master-slave model in terms of processing time and approximation to the true Pareto front.
文摘高效率的设计出大消声量的消声器一直是车辆排气噪声控制中面临的难题。考虑到消声器优化过程中涉及参数较多,在消声器传递损失数值建模的基础上,采用试验设计(DOE)中的拉丁超立方设计对消声器参数进行分析,结合多岛遗传算法(MIGA)和传统遗传算法(GA)分别建立消声器在排气噪声单峰值频率和多峰值频率处的传递损失为目标的优化模型,开展消声器传递损失优化设计研究。结果表明:DOE方法能有效的辨识出各参数对消声器传递损失影响的大小,简化了消声器的优化模型。MIGA对消声器在单峰值频率和多峰值频率的优化都优于GA,且多峰值频率的优化好于单峰值频率的优化,能使排气噪声最大降低20.98 d B。
