为了同时实现应用于步进扫描投影光刻机中的长行程直线电机的高推力密度、低推力波动和低铜损耗,提出了基于多种群遗传算法(multiple population genetic algorithm,MPGA)的环形绕组形式无铁心永磁直线同步电机(air-corepermanent magne...为了同时实现应用于步进扫描投影光刻机中的长行程直线电机的高推力密度、低推力波动和低铜损耗,提出了基于多种群遗传算法(multiple population genetic algorithm,MPGA)的环形绕组形式无铁心永磁直线同步电机(air-corepermanent magnet linear synchronous motor,ACPMLSM)多目标优化设计方法。在建立磁场分析模型的基础上,推导了关键参数的解析表达式。以永磁体、环形绕组的尺寸为变量,以推力体积比、电机常数和推力波动为优化目标,提出了基于权重系数的多目标优化函数,应用搜索能力强、收敛速度快的多种群遗传算法优化电机的结构尺寸。结果表明,在不同的权重系数下,MPGA得到的电机优化设计结果与设计目标具有良好的一致性。有限元仿真和实验结果证明了所提方法的有效性和可行性。展开更多
多种群方法已被证明是提高演化算法动态优化性能的重要方法之一。提出了多种群热力学遗传算法(multi-population based thermodynamic genetic algorithm,MPTDGA)。该算法使用一个概率向量在热力学遗传算法迭代过程中不断演化优化与竞...多种群方法已被证明是提高演化算法动态优化性能的重要方法之一。提出了多种群热力学遗传算法(multi-population based thermodynamic genetic algorithm,MPTDGA)。该算法使用一个概率向量在热力学遗传算法迭代过程中不断演化优化与竞争学习,环境变化时分化成三个概率向量,并分别抽样产生原对偶和随机迁入三个子种群,依据这三个种群和记忆种群最好解的情况,选择新的工作概率向量进入新环境进行学习。在动态背包问题上的实验结果表明,MPTDGA比原对偶遗传算法跟踪最优解的能力更强,有很好的多样性,非常适合求解0-1动态优化问题。展开更多
可扩展有限状态机EFSM(Extended Finite State Machine)是目前常用的一种描述软件状态和行为的模型,研究EFSM模型的测试数据自动生成方法具有重要的意义。针对EFSM模型,本文提出一种面向EFSM路径的测试数据自动生成方法,利用多种群遗传...可扩展有限状态机EFSM(Extended Finite State Machine)是目前常用的一种描述软件状态和行为的模型,研究EFSM模型的测试数据自动生成方法具有重要的意义。针对EFSM模型,本文提出一种面向EFSM路径的测试数据自动生成方法,利用多种群遗传算法MPGA(Multi-Population Genetic Algorithm)实现了EFSM测试数据的自动生成。实验结果表明,基于MPGA的EFSM模型测试数据自动生成是确实可行的,并且其测试数据生成效率优于遗传算法(GA)的测试数据生成效率。同时,通过实验分析了MPGA的种群数量、迁移间隔、迁移率、迁移策略等相关参数对EFSM模型测试数据生成效率的影响,得出一种最优的参数组合,对后续进一步利用MPGA进行测试数据自动生成的研究具有一定的指导意义。展开更多
The resilient modulus(MR)of subgrade soils is usually used to characterize the stiffness of subgrade and is a crucial parameter in pavement design.In order to determine the resilient modulus of compacted subgrade soil...The resilient modulus(MR)of subgrade soils is usually used to characterize the stiffness of subgrade and is a crucial parameter in pavement design.In order to determine the resilient modulus of compacted subgrade soils quickly and accurately,an optimized artificial neural network(ANN)approach based on the multi-population genetic algorithm(MPGA)was proposed in this study.The MPGA overcomes the problems of the traditional ANN such as low efficiency,local optimum and over-fitting.The developed optimized ANN method consists of ten input variables,twenty-one hidden neurons,and one output variable.The physical properties(liquid limit,plastic limit,plasticity index,0.075 mm passing percentage,maximum dry density,optimum moisture content),state variables(degree of compaction,moisture content)and stress variables(confining pressure,deviatoric stress)of subgrade soils were selected as input variables.The MR was directly used as the output variable.Then,adopting a large amount of experimental data from existing literature,the developed optimized ANN method was compared with the existing representative estimation methods.The results show that the developed optimized ANN method has the advantages of fast speed,strong generalization ability and good accuracy in MR estimation.展开更多
文摘为了同时实现应用于步进扫描投影光刻机中的长行程直线电机的高推力密度、低推力波动和低铜损耗,提出了基于多种群遗传算法(multiple population genetic algorithm,MPGA)的环形绕组形式无铁心永磁直线同步电机(air-corepermanent magnet linear synchronous motor,ACPMLSM)多目标优化设计方法。在建立磁场分析模型的基础上,推导了关键参数的解析表达式。以永磁体、环形绕组的尺寸为变量,以推力体积比、电机常数和推力波动为优化目标,提出了基于权重系数的多目标优化函数,应用搜索能力强、收敛速度快的多种群遗传算法优化电机的结构尺寸。结果表明,在不同的权重系数下,MPGA得到的电机优化设计结果与设计目标具有良好的一致性。有限元仿真和实验结果证明了所提方法的有效性和可行性。
基金国家自然科学基金 Grant No.61070009国家高技术研究发展计划(863计划) Grant No.2007AA01Z290~~
文摘多种群方法已被证明是提高演化算法动态优化性能的重要方法之一。提出了多种群热力学遗传算法(multi-population based thermodynamic genetic algorithm,MPTDGA)。该算法使用一个概率向量在热力学遗传算法迭代过程中不断演化优化与竞争学习,环境变化时分化成三个概率向量,并分别抽样产生原对偶和随机迁入三个子种群,依据这三个种群和记忆种群最好解的情况,选择新的工作概率向量进入新环境进行学习。在动态背包问题上的实验结果表明,MPTDGA比原对偶遗传算法跟踪最优解的能力更强,有很好的多样性,非常适合求解0-1动态优化问题。
基金Project(51878078)supported by the National Natural Science Foundation of ChinaProject(2018-025)supported by the Training Program for High-level Technical Personnel in Transportation Industry,ChinaProject(CTKY-PTRC-2018-003)supported by the Design Theory,Method and Demonstration of Durability Asphalt Pavement Based on Heavy-duty Traffic Conditions in Shanghai Area,China。
文摘The resilient modulus(MR)of subgrade soils is usually used to characterize the stiffness of subgrade and is a crucial parameter in pavement design.In order to determine the resilient modulus of compacted subgrade soils quickly and accurately,an optimized artificial neural network(ANN)approach based on the multi-population genetic algorithm(MPGA)was proposed in this study.The MPGA overcomes the problems of the traditional ANN such as low efficiency,local optimum and over-fitting.The developed optimized ANN method consists of ten input variables,twenty-one hidden neurons,and one output variable.The physical properties(liquid limit,plastic limit,plasticity index,0.075 mm passing percentage,maximum dry density,optimum moisture content),state variables(degree of compaction,moisture content)and stress variables(confining pressure,deviatoric stress)of subgrade soils were selected as input variables.The MR was directly used as the output variable.Then,adopting a large amount of experimental data from existing literature,the developed optimized ANN method was compared with the existing representative estimation methods.The results show that the developed optimized ANN method has the advantages of fast speed,strong generalization ability and good accuracy in MR estimation.
