For an optimal design of a surface-mounted permanent magnet synchronous motor(SPMSM),many objective functions should be considered.The classical optimization methods,which have been habitually designed based on magnet...For an optimal design of a surface-mounted permanent magnet synchronous motor(SPMSM),many objective functions should be considered.The classical optimization methods,which have been habitually designed based on magnetic circuit law or finite element analysis(FEA),have inaccuracy or calculation time problems when solving the multi-objective problems.To address these problems,the multi-independent-population genetic algorithm(MGA)combined with subdomain(SD)model are proposed to improve the performance of SPMSM such as magnetic field distribution,cost and efficiency.In order to analyze the flux density harmonics accurately,the accurate SD model is first established.Then,the MGA with time-saving SD model are employed to search for solutions which belong to the Pareto optimal set.Finally,for the purpose of validation,the electromagnetic performance of the new design motor are investigated by FEA,comparing with the initial design and conventional GA optimal design to demonstrate the advantage of MGA optimization method.展开更多
A multi-objective improved genetic algorithm is constructed to solve the train operation simulation model of urban rail train and find the optimal operation curve.In the train control system,the conversion point of op...A multi-objective improved genetic algorithm is constructed to solve the train operation simulation model of urban rail train and find the optimal operation curve.In the train control system,the conversion point of operating mode is the basic of gene encoding and the chromosome composed of multiple genes represents a control scheme,and the initial population can be formed by the way.The fitness function can be designed by the design requirements of the train control stop error,time error and energy consumption.the effectiveness of new individual can be ensured by checking the validity of the original individual when its in the process of selection,crossover and mutation,and the optimal algorithm will be joined all the operators to make the new group not eliminate on the best individual of the last generation.The simulation result shows that the proposed genetic algorithm comparing with the optimized multi-particle simulation model can reduce more than 10%energy consumption,it can provide a large amount of sub-optimal solution and has obvious optimization effect.展开更多
针对智慧医疗场景中高密度无线体域网(wireless body area network,WBAN)多优先级数据传输与计算资源受限的挑战,研究提出一种融合动态优先级评估与量子优化的任务卸载策略。首先通过构建医疗物联网(healthcare Internet of Things,H-I...针对智慧医疗场景中高密度无线体域网(wireless body area network,WBAN)多优先级数据传输与计算资源受限的挑战,研究提出一种融合动态优先级评估与量子优化的任务卸载策略。首先通过构建医疗物联网(healthcare Internet of Things,H-IoT)高密度WBAN网络模型,集成任务优先级分层机制与动态信道状态感知模块,建立基于生理数据特征的通信质量评估体系。其次设计多维动态调度框架,利用生理参数偏离度、数据滞留时间及抢占事件等指标实时调整任务优先级权重,结合抢占式调度策略保障急诊数据的低时延传输。再进一步改进量子遗传算法(improved quantum genetic algorithm,IQGA),采用动态量子旋转门角度调整机制优化局部搜索性能,并引入灾变修正函数提升全局收敛效率。仿真实验表明,该策略在任务平均处理时间、系统能耗、高优先级任务时延及收敛速度方面分别实现71.51%、88.21%、89.63%和78.74%的性能优化,系统综合收益提升达114.43%。研究成果为高密度医疗物联网场景下的实时任务调度与资源分配提供了理论支撑与技术路径。展开更多
基金This work was supported in part by the National Natural Science Foundation of China under Grant51507016。
文摘For an optimal design of a surface-mounted permanent magnet synchronous motor(SPMSM),many objective functions should be considered.The classical optimization methods,which have been habitually designed based on magnetic circuit law or finite element analysis(FEA),have inaccuracy or calculation time problems when solving the multi-objective problems.To address these problems,the multi-independent-population genetic algorithm(MGA)combined with subdomain(SD)model are proposed to improve the performance of SPMSM such as magnetic field distribution,cost and efficiency.In order to analyze the flux density harmonics accurately,the accurate SD model is first established.Then,the MGA with time-saving SD model are employed to search for solutions which belong to the Pareto optimal set.Finally,for the purpose of validation,the electromagnetic performance of the new design motor are investigated by FEA,comparing with the initial design and conventional GA optimal design to demonstrate the advantage of MGA optimization method.
基金This work was supported by the Youth Backbone Teachers Training Program of Henan Colleges and Universities under Grant No.2016ggjs-287the Project of Science and Technology of Henan Province under Grant Nos.172102210124 and 202102210269.
文摘A multi-objective improved genetic algorithm is constructed to solve the train operation simulation model of urban rail train and find the optimal operation curve.In the train control system,the conversion point of operating mode is the basic of gene encoding and the chromosome composed of multiple genes represents a control scheme,and the initial population can be formed by the way.The fitness function can be designed by the design requirements of the train control stop error,time error and energy consumption.the effectiveness of new individual can be ensured by checking the validity of the original individual when its in the process of selection,crossover and mutation,and the optimal algorithm will be joined all the operators to make the new group not eliminate on the best individual of the last generation.The simulation result shows that the proposed genetic algorithm comparing with the optimized multi-particle simulation model can reduce more than 10%energy consumption,it can provide a large amount of sub-optimal solution and has obvious optimization effect.
文摘针对智慧医疗场景中高密度无线体域网(wireless body area network,WBAN)多优先级数据传输与计算资源受限的挑战,研究提出一种融合动态优先级评估与量子优化的任务卸载策略。首先通过构建医疗物联网(healthcare Internet of Things,H-IoT)高密度WBAN网络模型,集成任务优先级分层机制与动态信道状态感知模块,建立基于生理数据特征的通信质量评估体系。其次设计多维动态调度框架,利用生理参数偏离度、数据滞留时间及抢占事件等指标实时调整任务优先级权重,结合抢占式调度策略保障急诊数据的低时延传输。再进一步改进量子遗传算法(improved quantum genetic algorithm,IQGA),采用动态量子旋转门角度调整机制优化局部搜索性能,并引入灾变修正函数提升全局收敛效率。仿真实验表明,该策略在任务平均处理时间、系统能耗、高优先级任务时延及收敛速度方面分别实现71.51%、88.21%、89.63%和78.74%的性能优化,系统综合收益提升达114.43%。研究成果为高密度医疗物联网场景下的实时任务调度与资源分配提供了理论支撑与技术路径。
