Proximal gradient descent and its accelerated version are resultful methods for solving the sum of smooth and non-smooth problems. When the smooth function can be represented as a sum of multiple functions, the stocha...Proximal gradient descent and its accelerated version are resultful methods for solving the sum of smooth and non-smooth problems. When the smooth function can be represented as a sum of multiple functions, the stochastic proximal gradient method performs well. However, research on its accelerated version remains unclear. This paper proposes a proximal stochastic accelerated gradient (PSAG) method to address problems involving a combination of smooth and non-smooth components, where the smooth part corresponds to the average of multiple block sums. Simultaneously, most of convergence analyses hold in expectation. To this end, under some mind conditions, we present an almost sure convergence of unbiased gradient estimation in the non-smooth setting. Moreover, we establish that the minimum of the squared gradient mapping norm arbitrarily converges to zero with probability one.展开更多
Motor drives form an essential part of the electric compressors,pumps,braking and actuation systems in the More-Electric Aircraft(MEA).In this paper,the application of Machine Learning(ML)in motor-drive design and opt...Motor drives form an essential part of the electric compressors,pumps,braking and actuation systems in the More-Electric Aircraft(MEA).In this paper,the application of Machine Learning(ML)in motor-drive design and optimization process is investigated.The general idea of using ML is to train surrogate models for the optimization.This training process is based on sample data collected from detailed simulation or experiment of motor drives.However,the Surrogate Role(SR)of ML may vary for different applications.This paper first introduces the principles of ML and then proposes two SRs(direct mapping approach and correction approach)of the ML in a motor-drive optimization process.Two different cases are given for the method comparison and validation of ML SRs.The first case is using the sample data from experiments to train the ML surrogate models.For the second case,the joint-simulation data is utilized for a multi-objective motor-drive optimization problem.It is found that both surrogate roles of ML can provide a good mapping model for the cases and in the second case,three feasible design schemes of ML are proposed and validated for the two SRs.Regarding the time consumption in optimizaiton,the proposed ML models can give one motor-drive design point up to 0.044 s while it takes more than 1.5 mins for the used simulation-based models.展开更多
针对思维进化算法中的产生初始种群的盲目随机性和冗余性以及现有搜索方式易陷入局部最优的问题,将混沌优化和思维进化算法结合,提出了一种基于混沌搜索的思维进化算法(Chaos Mind Evaluation Algorithm,CMEA)。该算法在进化的不同阶段...针对思维进化算法中的产生初始种群的盲目随机性和冗余性以及现有搜索方式易陷入局部最优的问题,将混沌优化和思维进化算法结合,提出了一种基于混沌搜索的思维进化算法(Chaos Mind Evaluation Algorithm,CMEA)。该算法在进化的不同阶段引入混沌优化操作,利用混沌的遍历性提高算法的收敛速度,克服了早熟现象,同时利用思维进化算法的记忆特性和当代最优解指导混沌搜索,提高算法的搜索能力。仿真结果表明,与标准思维进化相比,该算法优化能力强,能有效地避免局部收敛,具有更快的收敛速度。展开更多
文摘Proximal gradient descent and its accelerated version are resultful methods for solving the sum of smooth and non-smooth problems. When the smooth function can be represented as a sum of multiple functions, the stochastic proximal gradient method performs well. However, research on its accelerated version remains unclear. This paper proposes a proximal stochastic accelerated gradient (PSAG) method to address problems involving a combination of smooth and non-smooth components, where the smooth part corresponds to the average of multiple block sums. Simultaneously, most of convergence analyses hold in expectation. To this end, under some mind conditions, we present an almost sure convergence of unbiased gradient estimation in the non-smooth setting. Moreover, we establish that the minimum of the squared gradient mapping norm arbitrarily converges to zero with probability one.
基金funding from the Clean Sky 2 Joint Undertaking under the European Union’s Horizon 2020 research and Innovation Programme No.807081。
文摘Motor drives form an essential part of the electric compressors,pumps,braking and actuation systems in the More-Electric Aircraft(MEA).In this paper,the application of Machine Learning(ML)in motor-drive design and optimization process is investigated.The general idea of using ML is to train surrogate models for the optimization.This training process is based on sample data collected from detailed simulation or experiment of motor drives.However,the Surrogate Role(SR)of ML may vary for different applications.This paper first introduces the principles of ML and then proposes two SRs(direct mapping approach and correction approach)of the ML in a motor-drive optimization process.Two different cases are given for the method comparison and validation of ML SRs.The first case is using the sample data from experiments to train the ML surrogate models.For the second case,the joint-simulation data is utilized for a multi-objective motor-drive optimization problem.It is found that both surrogate roles of ML can provide a good mapping model for the cases and in the second case,three feasible design schemes of ML are proposed and validated for the two SRs.Regarding the time consumption in optimizaiton,the proposed ML models can give one motor-drive design point up to 0.044 s while it takes more than 1.5 mins for the used simulation-based models.
文摘针对思维进化算法中的产生初始种群的盲目随机性和冗余性以及现有搜索方式易陷入局部最优的问题,将混沌优化和思维进化算法结合,提出了一种基于混沌搜索的思维进化算法(Chaos Mind Evaluation Algorithm,CMEA)。该算法在进化的不同阶段引入混沌优化操作,利用混沌的遍历性提高算法的收敛速度,克服了早熟现象,同时利用思维进化算法的记忆特性和当代最优解指导混沌搜索,提高算法的搜索能力。仿真结果表明,与标准思维进化相比,该算法优化能力强,能有效地避免局部收敛,具有更快的收敛速度。
