Resource allocation （RA） is the problem of allocating resources among various artifacts or business units to meet one or more expected goals, such a.s maximizing the profits, minimizing the costs, or achieving the b...Resource allocation （RA） is the problem of allocating resources among various artifacts or business units to meet one or more expected goals, such a.s maximizing the profits, minimizing the costs, or achieving the best qualities. A complex multiobjective RA is addressed, and a multiobjective mathematical model is used to find solutions efficiently. Then, all improved particie swarm algorithm （mO_PSO） is proposed combined with a new particle diversity controller policies and dissipation operation. Meanwhile, a modified Pareto methods used in PSO to deal with multiobjectives optimization is presented. The effectiveness of the provided algorithm is validated by its application to some illustrative example dealing with multiobjective RA problems and with the comparative experiment with other algorithm.展开更多
A lifetime prediction method for high-reliability tantalum （Ta） capacitors was proposed, based on multiple degradation measures and grey model （GM）. For analyzing performance degradation data, a two-parameter mode...A lifetime prediction method for high-reliability tantalum （Ta） capacitors was proposed, based on multiple degradation measures and grey model （GM）. For analyzing performance degradation data, a two-parameter model based on GM was developed. In order to improve the prediction accuracy of the two-parameter model, parameter selection based on particle swarm optimization （PSO） was used. Then, the new PSO-GM（1, 2, co） optimization model was constructed, which was validated experimentally by conducting an accelerated testing on the Ta capacitors. The experiments were conducted at three different stress levels of 85, 120, and 145℃. The results of two experiments were used in estimating the parameters. And the reliability of the Ta capacitors was estimated at the same stress conditions of the third experiment. The results indicate that the proposed method is valid and accurate.展开更多
This paper presents a path planning approach for rotary unmanned aerial vehicles(R-UAVs)in a known static rough terrain environment.This approach aims to find collision-free and feasible paths with minimum altitude,le...This paper presents a path planning approach for rotary unmanned aerial vehicles(R-UAVs)in a known static rough terrain environment.This approach aims to find collision-free and feasible paths with minimum altitude,length and angle variable rate.First,a three-dimensional(3D)modeling method is proposed to reduce the computation burden of the dynamic models of R-UAVs.Considering the length,height and tuning angle of a path,the path planning of R-UAVs is described as a tri-objective optimization problem.Then,an improved multi-objective particle swarm optimization algorithm is developed.To render the algorithm more effective in dealing with this problem,a vibration function is introduced into the collided solutions to improve the algorithm efficiency.Meanwhile,the selection of the global best position is taken into account by the reference point method.Finally,the experimental environment is built with the help of the Google map and the 3D terrain generator World Machine.Experimental results under two different rough terrains from Guilin and Lanzhou of China demonstrate the capabilities of the proposed algorithm in finding Pareto optimal paths.展开更多
针对无人机在复杂环境下的三维路径规划问题,集成传统的粒子群优化(particle swarm optimization,PSO)算法和灰狼优化(grey wolf optimization,GWO)算法,提出了一种PSO-GWO复合算法。首先,采用了非线性控制参数和加权自适应的个体位置...针对无人机在复杂环境下的三维路径规划问题,集成传统的粒子群优化(particle swarm optimization,PSO)算法和灰狼优化(grey wolf optimization,GWO)算法,提出了一种PSO-GWO复合算法。首先,采用了非线性控制参数和加权自适应的个体位置更新策略来平衡算法的全局搜索能力和局部搜索能力;然后,使用随机指导策略来增加解的多样性;最后,使用B样条曲线平滑所生成的飞行路径,使路径更适用于无人机。实验结果表明,PSO-GWO复合算法可以生成一条安全可行的路径,其性能明显优于GWO算法和其他改进GWO算法。展开更多
Most of the existing multivariable grey models are based on the 1-order derivative and 1-order accumulation, which makes the parameters unable to be adjusted according to the data characteristics of the actual problem...Most of the existing multivariable grey models are based on the 1-order derivative and 1-order accumulation, which makes the parameters unable to be adjusted according to the data characteristics of the actual problems. The results about fractional derivative multivariable grey models are very few at present. In this paper, a multivariable Caputo fractional derivative grey model with convolution integral CFGMC(q, N) is proposed. First, the Caputo fractional difference is used to discretize the model, and the least square method is used to solve the parameters. The orders of accumulations and differential equations are determined by using particle swarm optimization(PSO). Then, the analytical solution of the model is obtained by using the Laplace transform, and the convergence and divergence of series in analytical solutions are also discussed. Finally, the CFGMC(q, N) model is used to predict the municipal solid waste(MSW). Compared with other competition models, the model has the best prediction effect. This study enriches the model form of the multivariable grey model, expands the scope of application, and provides a new idea for the development of fractional derivative grey model.展开更多
针对分布式电源并网引起的双向潮流导致网损增大以及分布式电源、负荷的波动导致节点电压波动等问题,文章基于固态变压器(Solid State Transformer,SST)两侧电力电子变换器的脉冲宽度调制技术,提出了一种控制潮流的方法。该方法首先建...针对分布式电源并网引起的双向潮流导致网损增大以及分布式电源、负荷的波动导致节点电压波动等问题,文章基于固态变压器(Solid State Transformer,SST)两侧电力电子变换器的脉冲宽度调制技术,提出了一种控制潮流的方法。该方法首先建立了含SST的有源配电网动态无功优化模型;然后以多时刻的有功网损和电压波动为优化目标,采用改进多目标粒子群算法对SST的一、二次侧的电力电子变换器的调制角和调制系数等多个控制变量进行求解;最后建立仿真模型并与基于有载调压变压器的有源配电网动态无功优化方法进行比较。结果证明了所提方法在降低配电网网损和维持节点电压稳定方面的优越性。展开更多
基金the National Natural Science Foundation of China (60573159)
文摘Resource allocation （RA） is the problem of allocating resources among various artifacts or business units to meet one or more expected goals, such a.s maximizing the profits, minimizing the costs, or achieving the best qualities. A complex multiobjective RA is addressed, and a multiobjective mathematical model is used to find solutions efficiently. Then, all improved particie swarm algorithm （mO_PSO） is proposed combined with a new particle diversity controller policies and dissipation operation. Meanwhile, a modified Pareto methods used in PSO to deal with multiobjectives optimization is presented. The effectiveness of the provided algorithm is validated by its application to some illustrative example dealing with multiobjective RA problems and with the comparative experiment with other algorithm.
基金Project(Z132012) supported by the Second Five Technology-based Fund in Science and Industry Bureau of ChinaProject(1004GK0032) supported by General Armament Department for the Common Issues of Military Electronic Components,China
文摘A lifetime prediction method for high-reliability tantalum （Ta） capacitors was proposed, based on multiple degradation measures and grey model （GM）. For analyzing performance degradation data, a two-parameter model based on GM was developed. In order to improve the prediction accuracy of the two-parameter model, parameter selection based on particle swarm optimization （PSO） was used. Then, the new PSO-GM（1, 2, co） optimization model was constructed, which was validated experimentally by conducting an accelerated testing on the Ta capacitors. The experiments were conducted at three different stress levels of 85, 120, and 145℃. The results of two experiments were used in estimating the parameters. And the reliability of the Ta capacitors was estimated at the same stress conditions of the third experiment. The results indicate that the proposed method is valid and accurate.
基金supported by the National Natural Science Foundation of China(6167321461673217+2 种基金61673219)the Natural Science Foundation of the Jiangsu Higher Education Institutions of China(18KJB120011)the Postgraduate Research and Practice Innovation Program of Jiangsu Province(KYCX19_0299)
文摘This paper presents a path planning approach for rotary unmanned aerial vehicles(R-UAVs)in a known static rough terrain environment.This approach aims to find collision-free and feasible paths with minimum altitude,length and angle variable rate.First,a three-dimensional(3D)modeling method is proposed to reduce the computation burden of the dynamic models of R-UAVs.Considering the length,height and tuning angle of a path,the path planning of R-UAVs is described as a tri-objective optimization problem.Then,an improved multi-objective particle swarm optimization algorithm is developed.To render the algorithm more effective in dealing with this problem,a vibration function is introduced into the collided solutions to improve the algorithm efficiency.Meanwhile,the selection of the global best position is taken into account by the reference point method.Finally,the experimental environment is built with the help of the Google map and the 3D terrain generator World Machine.Experimental results under two different rough terrains from Guilin and Lanzhou of China demonstrate the capabilities of the proposed algorithm in finding Pareto optimal paths.
文摘针对无人机在复杂环境下的三维路径规划问题,集成传统的粒子群优化(particle swarm optimization,PSO)算法和灰狼优化(grey wolf optimization,GWO)算法,提出了一种PSO-GWO复合算法。首先,采用了非线性控制参数和加权自适应的个体位置更新策略来平衡算法的全局搜索能力和局部搜索能力;然后,使用随机指导策略来增加解的多样性;最后,使用B样条曲线平滑所生成的飞行路径,使路径更适用于无人机。实验结果表明,PSO-GWO复合算法可以生成一条安全可行的路径,其性能明显优于GWO算法和其他改进GWO算法。
基金supported by the National Natural Science Foundation of China (51479151,61403288)。
文摘Most of the existing multivariable grey models are based on the 1-order derivative and 1-order accumulation, which makes the parameters unable to be adjusted according to the data characteristics of the actual problems. The results about fractional derivative multivariable grey models are very few at present. In this paper, a multivariable Caputo fractional derivative grey model with convolution integral CFGMC(q, N) is proposed. First, the Caputo fractional difference is used to discretize the model, and the least square method is used to solve the parameters. The orders of accumulations and differential equations are determined by using particle swarm optimization(PSO). Then, the analytical solution of the model is obtained by using the Laplace transform, and the convergence and divergence of series in analytical solutions are also discussed. Finally, the CFGMC(q, N) model is used to predict the municipal solid waste(MSW). Compared with other competition models, the model has the best prediction effect. This study enriches the model form of the multivariable grey model, expands the scope of application, and provides a new idea for the development of fractional derivative grey model.
文摘针对分布式电源并网引起的双向潮流导致网损增大以及分布式电源、负荷的波动导致节点电压波动等问题,文章基于固态变压器(Solid State Transformer,SST)两侧电力电子变换器的脉冲宽度调制技术,提出了一种控制潮流的方法。该方法首先建立了含SST的有源配电网动态无功优化模型;然后以多时刻的有功网损和电压波动为优化目标,采用改进多目标粒子群算法对SST的一、二次侧的电力电子变换器的调制角和调制系数等多个控制变量进行求解;最后建立仿真模型并与基于有载调压变压器的有源配电网动态无功优化方法进行比较。结果证明了所提方法在降低配电网网损和维持节点电压稳定方面的优越性。
