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
With the development of the monitoring technology,it is more and more common that the system is continuously monitored.Therefore,the research on the maintenance optimization of the continuously monitored deterioration...With the development of the monitoring technology,it is more and more common that the system is continuously monitored.Therefore,the research on the maintenance optimization of the continuously monitored deterioration system is important.The deterioration process of the discussed system is described by a Gamma process.The predictive maintenance is considered to be imperfect and formulated.The expected interval of two continuous preventive maintenances is derived.Then,the maintenance optimization model of the continuously monitored deterioration system is presented.In the model,the minimization of the expected operational cost per unit time and the maximization of the system availability are the optimization objectives.The improved ideal point method with the normalized objective functions is employed to solve the proposed model.The validity and sensitivity of the proposed multiobjective maintenance optimization model are analyzed by a numerical example.展开更多
Multi-objective optimization(MOO)for the microwave metamaterial absorber(MMA)normally adopts evolutionary algo-rithms,and these optimization algorithms require many objec-tive function evaluations.To remedy this issue...Multi-objective optimization(MOO)for the microwave metamaterial absorber(MMA)normally adopts evolutionary algo-rithms,and these optimization algorithms require many objec-tive function evaluations.To remedy this issue,a surrogate-based MOO algorithm is proposed in this paper where Kriging models are employed to approximate objective functions.An efficient sampling strategy is presented to sequentially capture promising samples in the design region for exact evaluations.Firstly,new sample points are generated by the MOO on surro-gate models.Then,new samples are captured by exploiting each objective function.Furthermore,a weighted sum of the improvement of hypervolume(IHV)and the distance to sampled points is calculated to select the new sample.Compared with two well-known MOO algorithms,the proposed algorithm is vali-dated by benchmark problems.In addition,two broadband MMAs are applied to verify the feasibility and efficiency of the proposed algorithm.展开更多
吸气式电推进(air-breathing electric propulsion,ABEP)系统使用超低轨道大气作为工质,可突破推进剂携带量对卫星使用寿命的限制瓶颈,是超低轨卫星实现长期驻留的关键技术途径之一.本文采用直接模拟蒙特卡罗(direct simulation Monte C...吸气式电推进(air-breathing electric propulsion,ABEP)系统使用超低轨道大气作为工质,可突破推进剂携带量对卫星使用寿命的限制瓶颈,是超低轨卫星实现长期驻留的关键技术途径之一.本文采用直接模拟蒙特卡罗(direct simulation Monte Carlo,DSMC)计算方法,对二维的ABEP进气道模型进行模拟.设定壁面碰撞模型为完全漫反射,在进气道的进口直径保持定值的前提下,改变进气道的长纵比、出口锥角、栅格长度和栅格层数,以分别探究这些影响因素单一作用下的进气道性能变化规律.在单一影响规律的前提下,利用遗传算法进行多目标优化,得到符合设计要求的高性能进气道设计参数,通过权重分配实现了典型高度下进气道设计中收集效率与压缩比的最优解.本研究对大气收集器产品的工程化应用具有指导意义.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supported by the Fundamental Research Funds for the Central Universities (N090303005)Key National Science and Technology Special Project (2010ZX04014-014)
文摘With the development of the monitoring technology,it is more and more common that the system is continuously monitored.Therefore,the research on the maintenance optimization of the continuously monitored deterioration system is important.The deterioration process of the discussed system is described by a Gamma process.The predictive maintenance is considered to be imperfect and formulated.The expected interval of two continuous preventive maintenances is derived.Then,the maintenance optimization model of the continuously monitored deterioration system is presented.In the model,the minimization of the expected operational cost per unit time and the maximization of the system availability are the optimization objectives.The improved ideal point method with the normalized objective functions is employed to solve the proposed model.The validity and sensitivity of the proposed multiobjective maintenance optimization model are analyzed by a numerical example.
基金supported by the National Key Research and Development Program(2021YFB3502500).
文摘Multi-objective optimization(MOO)for the microwave metamaterial absorber(MMA)normally adopts evolutionary algo-rithms,and these optimization algorithms require many objec-tive function evaluations.To remedy this issue,a surrogate-based MOO algorithm is proposed in this paper where Kriging models are employed to approximate objective functions.An efficient sampling strategy is presented to sequentially capture promising samples in the design region for exact evaluations.Firstly,new sample points are generated by the MOO on surro-gate models.Then,new samples are captured by exploiting each objective function.Furthermore,a weighted sum of the improvement of hypervolume(IHV)and the distance to sampled points is calculated to select the new sample.Compared with two well-known MOO algorithms,the proposed algorithm is vali-dated by benchmark problems.In addition,two broadband MMAs are applied to verify the feasibility and efficiency of the proposed algorithm.
文摘吸气式电推进(air-breathing electric propulsion,ABEP)系统使用超低轨道大气作为工质,可突破推进剂携带量对卫星使用寿命的限制瓶颈,是超低轨卫星实现长期驻留的关键技术途径之一.本文采用直接模拟蒙特卡罗(direct simulation Monte Carlo,DSMC)计算方法,对二维的ABEP进气道模型进行模拟.设定壁面碰撞模型为完全漫反射,在进气道的进口直径保持定值的前提下,改变进气道的长纵比、出口锥角、栅格长度和栅格层数,以分别探究这些影响因素单一作用下的进气道性能变化规律.在单一影响规律的前提下,利用遗传算法进行多目标优化,得到符合设计要求的高性能进气道设计参数,通过权重分配实现了典型高度下进气道设计中收集效率与压缩比的最优解.本研究对大气收集器产品的工程化应用具有指导意义.
