A second-generation fast Non-dominated Sorting Genetic Algorithm product shape multi-objective imagery optimization model based on degradation(DNSGA-II)strategy is proposed to make the product appearance optimization ...A second-generation fast Non-dominated Sorting Genetic Algorithm product shape multi-objective imagery optimization model based on degradation(DNSGA-II)strategy is proposed to make the product appearance optimization scheme meet the complex emotional needs of users for the product.First,the semantic differential method and K-Means cluster analysis are applied to extract the multi-objective imagery of users;then,the product multidimensional scale analysis is applied to classify the research objects,and again the reference samples are screened by the semantic differentialmethod,and the samples are parametrized in two dimensions by using elliptic Fourier analysis;finally,the fuzzy dynamic evaluation function is used as the objective function of the algorithm,and the coordinates of key points of product contours Finally,with the fuzzy dynamic evaluation function as the objective function of the algorithm and the coordinates of key points of the product profile as the decision variables,the optimal product profile solution set is solved by DNSGA-II.The validity of the model is verified by taking the optimization of the shape scheme of the hospital connection site as an example.For comparison with DNSGA-II,other multi-objective optimization algorithms are also presented.To evaluate the performance of each algorithm,the performance evaluation index values of the five multi-objective optimization algorithms are calculated in this paper.The results show that DNSGA-II is superior in improving individual diversity and has better overall performance.展开更多
The preparation process parameters of intercalated meltblown nonwoven materials are complicated, and the relationship between process parameters, structural variables, and product performance needs to be investigated ...The preparation process parameters of intercalated meltblown nonwoven materials are complicated, and the relationship between process parameters, structural variables, and product performance needs to be investigated to establish a good mechanism for product performance regulation. In this study, we first used Wilcoxon test and Pearson correlation analysis to investigate the effect of intercalation rate on structural variables and product performance. Then, regression models were constructed to predict the values of each structural variable under different combinations of process parameters. Finally, we constructed a multi-objective constrained optimization problem based on the stepwise regression model and the product variable conditions. The problem was solved using the NSGA-II algorithm. The optimal was achieved when the acceptance distance was 2.892 cm and the hot air speed was 2000 r/min.展开更多
多跑道机场飞行区运行效率低下会导致空域-跑道系统容流供需失衡,进而造成终端区空域交通拥堵、航班延误现象频发。为提升多跑道机场终端区运行效率,借助全空域与机场模型软件(total airspace and airport modeler,TAAM)建立空域仿真模...多跑道机场飞行区运行效率低下会导致空域-跑道系统容流供需失衡,进而造成终端区空域交通拥堵、航班延误现象频发。为提升多跑道机场终端区运行效率,借助全空域与机场模型软件(total airspace and airport modeler,TAAM)建立空域仿真模型,针对不同运行模式动态转换下对终端区交通流走向、扇区开合等空域时空特性的影响进行分析,提出1种考虑不同运行时段内终端区机场走廊口流量配比和进离港流量分布的动态多跑道使用策略优化方法。首先,使用TAAM综合考虑不同跑道运行模式下各扇区内航班流量、高度变更、移交协调及冲突解脱对管制负荷的影响,拟合得出不同跑道运行模式下基于当量航空器架次的各扇区管制负荷函数。以终端区内航班平均飞行时间、平均延误时间及管制员工作负荷为优化目标,建立了跑道使用策略优化模型。设计了1种基于航空器基本性能数据库(the base aircraft data,BADA)的多目标非支配排序遗传算法(NSGA-Ⅱ),并结合机场实际运行条件在无运行限制、运行方向限制、运行模式限制等5种场景下进行仿真计算。对各场景Pareto最优解集进行评价得出不同场景下最优跑道使用策略,并使用TAAM进行仿真对比验证。结果表明:无运行限制和运行方向限制相较于单一跑道运行模式的航班服务效率提升10.15%,5.01%;管制员工作负荷减少3.91%,3.4%;延误时间减少28.86%,19.46%。展开更多
带拥挤距离排挤机制的非支配排序遗传算法(NSGA-II)在多目标优化领域具有广泛的应用,NSGA-II算法具有个体分布不均匀以及重复个体较多等缺陷.针对这些缺陷提出一种基于向量空间模型的NSGA-II改进算法VSMGA(Vector Space M odel Genetic ...带拥挤距离排挤机制的非支配排序遗传算法(NSGA-II)在多目标优化领域具有广泛的应用,NSGA-II算法具有个体分布不均匀以及重复个体较多等缺陷.针对这些缺陷提出一种基于向量空间模型的NSGA-II改进算法VSMGA(Vector Space M odel Genetic Algorithm),VSM GA算法在NSGA-II算法的基础上引入了向量空间模型,利用目标权重向量之间的余弦距离代替原来的拥挤距离,提出一种距离排挤机制和重复个体排除规则.实验结果表明与NSGA-II算法比较,VSMGA算法具有更好的分布性和稳定性.展开更多
基金supported by National Natural Science Foundation Grant 52065010the Science and Technology Project supported by Guizhou Province of China ZK[2021]341 and[2021]397the transformation Project of Scientific and Technological Achievements in Guiyang,Guizhou Province,China[2021]7-3.
文摘A second-generation fast Non-dominated Sorting Genetic Algorithm product shape multi-objective imagery optimization model based on degradation(DNSGA-II)strategy is proposed to make the product appearance optimization scheme meet the complex emotional needs of users for the product.First,the semantic differential method and K-Means cluster analysis are applied to extract the multi-objective imagery of users;then,the product multidimensional scale analysis is applied to classify the research objects,and again the reference samples are screened by the semantic differentialmethod,and the samples are parametrized in two dimensions by using elliptic Fourier analysis;finally,the fuzzy dynamic evaluation function is used as the objective function of the algorithm,and the coordinates of key points of product contours Finally,with the fuzzy dynamic evaluation function as the objective function of the algorithm and the coordinates of key points of the product profile as the decision variables,the optimal product profile solution set is solved by DNSGA-II.The validity of the model is verified by taking the optimization of the shape scheme of the hospital connection site as an example.For comparison with DNSGA-II,other multi-objective optimization algorithms are also presented.To evaluate the performance of each algorithm,the performance evaluation index values of the five multi-objective optimization algorithms are calculated in this paper.The results show that DNSGA-II is superior in improving individual diversity and has better overall performance.
文摘The preparation process parameters of intercalated meltblown nonwoven materials are complicated, and the relationship between process parameters, structural variables, and product performance needs to be investigated to establish a good mechanism for product performance regulation. In this study, we first used Wilcoxon test and Pearson correlation analysis to investigate the effect of intercalation rate on structural variables and product performance. Then, regression models were constructed to predict the values of each structural variable under different combinations of process parameters. Finally, we constructed a multi-objective constrained optimization problem based on the stepwise regression model and the product variable conditions. The problem was solved using the NSGA-II algorithm. The optimal was achieved when the acceptance distance was 2.892 cm and the hot air speed was 2000 r/min.
文摘多跑道机场飞行区运行效率低下会导致空域-跑道系统容流供需失衡,进而造成终端区空域交通拥堵、航班延误现象频发。为提升多跑道机场终端区运行效率,借助全空域与机场模型软件(total airspace and airport modeler,TAAM)建立空域仿真模型,针对不同运行模式动态转换下对终端区交通流走向、扇区开合等空域时空特性的影响进行分析,提出1种考虑不同运行时段内终端区机场走廊口流量配比和进离港流量分布的动态多跑道使用策略优化方法。首先,使用TAAM综合考虑不同跑道运行模式下各扇区内航班流量、高度变更、移交协调及冲突解脱对管制负荷的影响,拟合得出不同跑道运行模式下基于当量航空器架次的各扇区管制负荷函数。以终端区内航班平均飞行时间、平均延误时间及管制员工作负荷为优化目标,建立了跑道使用策略优化模型。设计了1种基于航空器基本性能数据库(the base aircraft data,BADA)的多目标非支配排序遗传算法(NSGA-Ⅱ),并结合机场实际运行条件在无运行限制、运行方向限制、运行模式限制等5种场景下进行仿真计算。对各场景Pareto最优解集进行评价得出不同场景下最优跑道使用策略,并使用TAAM进行仿真对比验证。结果表明:无运行限制和运行方向限制相较于单一跑道运行模式的航班服务效率提升10.15%,5.01%;管制员工作负荷减少3.91%,3.4%;延误时间减少28.86%,19.46%。
文摘带拥挤距离排挤机制的非支配排序遗传算法(NSGA-II)在多目标优化领域具有广泛的应用,NSGA-II算法具有个体分布不均匀以及重复个体较多等缺陷.针对这些缺陷提出一种基于向量空间模型的NSGA-II改进算法VSMGA(Vector Space M odel Genetic Algorithm),VSM GA算法在NSGA-II算法的基础上引入了向量空间模型,利用目标权重向量之间的余弦距离代替原来的拥挤距离,提出一种距离排挤机制和重复个体排除规则.实验结果表明与NSGA-II算法比较,VSMGA算法具有更好的分布性和稳定性.
