An optimizing method of observation scheduling based on time-division multiplexing is proposed in this paper,and its efficiency is verified by outdoor experiments. The initial observation scheduling is first obtained ...An optimizing method of observation scheduling based on time-division multiplexing is proposed in this paper,and its efficiency is verified by outdoor experiments. The initial observation scheduling is first obtained by using a semi-random search algorithm,and secondly the connection time pair（ CTP） between adjacent objects is optimized by using a genetic algorithm. After obtaining these two parameters,the final observation scheduling can be obtained. According to pre-designed tracks between each adjacent objects in observation order,the seamless observation of neighboring targets is derived by automatically steering the antenna beam,so the observation efficiency is improved.展开更多
With the development of smart grid, residents have the opportunity to schedule their household appliances （HA） for the purpose of reducing electricity expenses and alleviating the pressure of the smart grid. In this...With the development of smart grid, residents have the opportunity to schedule their household appliances （HA） for the purpose of reducing electricity expenses and alleviating the pressure of the smart grid. In this paper, we introduce the structure of home energy management system （EMS） and then propose a power optimization strategy based on household load model and electric vehicle （EV） model for home power usage. In this strategy, the electric vehicles are charged when the price is low, and otherwise, are discharged. By adopting this combined system model under the time-of-use electricity price （TOUP）, the proposed scheduling strategy would effectively minimize the electricity cost and reduce the pressure of the smart grid at the same time. Finally, simulation experiments are carried out to show the feasibility of the proposed strategy. The results show that crossover genetic particle swarm optimization algorithm has better convergence properties than traditional particle swarm algorithm and better adaptability than genetic algorithm.展开更多
Abstract Satellite range scheduling with the priority constraint is one of the most important prob lems in the field of satellite operation. This paper proposes a station coding based genetic algorithm to solve this p...Abstract Satellite range scheduling with the priority constraint is one of the most important prob lems in the field of satellite operation. This paper proposes a station coding based genetic algorithm to solve this problem, which adopts a new chromosome encoding method that arranges tasks according to the ground station ID. The new encoding method contributes to reducing the complex ity in conflict checking and resolving, and helps to improve the ability to find optimal resolutions. Three different selection operators are designed to match the new encoding strategy, namely ran dom selection, greedy selection, and roulette selection. To demonstrate the benefits of the improved genetic algorithm, a basic genetic algorithm is designed in which two cross operators are presented, a singlepoint crossover and a multipoint crossover. For the purpose of algorithm test and analysis, a problemgenerating program is designed, which can simulate problems by modeling features encountered in realworld problems. Based on the problem generator, computational results and analysis are made and illustrated for the scheduling of multiple ground stations.展开更多
基金Supported by the National Natural Science Foundation of China(61271373,61571043)111 Project of China(B14010)
文摘An optimizing method of observation scheduling based on time-division multiplexing is proposed in this paper,and its efficiency is verified by outdoor experiments. The initial observation scheduling is first obtained by using a semi-random search algorithm,and secondly the connection time pair（ CTP） between adjacent objects is optimized by using a genetic algorithm. After obtaining these two parameters,the final observation scheduling can be obtained. According to pre-designed tracks between each adjacent objects in observation order,the seamless observation of neighboring targets is derived by automatically steering the antenna beam,so the observation efficiency is improved.
基金supported by the National Basic Research Program of China(973 Program)under Grant No.2012CB215202the National Natural Science Foundation of China under Grant No.51205046 and No.61450010
文摘With the development of smart grid, residents have the opportunity to schedule their household appliances （HA） for the purpose of reducing electricity expenses and alleviating the pressure of the smart grid. In this paper, we introduce the structure of home energy management system （EMS） and then propose a power optimization strategy based on household load model and electric vehicle （EV） model for home power usage. In this strategy, the electric vehicles are charged when the price is low, and otherwise, are discharged. By adopting this combined system model under the time-of-use electricity price （TOUP）, the proposed scheduling strategy would effectively minimize the electricity cost and reduce the pressure of the smart grid at the same time. Finally, simulation experiments are carried out to show the feasibility of the proposed strategy. The results show that crossover genetic particle swarm optimization algorithm has better convergence properties than traditional particle swarm algorithm and better adaptability than genetic algorithm.
文摘Abstract Satellite range scheduling with the priority constraint is one of the most important prob lems in the field of satellite operation. This paper proposes a station coding based genetic algorithm to solve this problem, which adopts a new chromosome encoding method that arranges tasks according to the ground station ID. The new encoding method contributes to reducing the complex ity in conflict checking and resolving, and helps to improve the ability to find optimal resolutions. Three different selection operators are designed to match the new encoding strategy, namely ran dom selection, greedy selection, and roulette selection. To demonstrate the benefits of the improved genetic algorithm, a basic genetic algorithm is designed in which two cross operators are presented, a singlepoint crossover and a multipoint crossover. For the purpose of algorithm test and analysis, a problemgenerating program is designed, which can simulate problems by modeling features encountered in realworld problems. Based on the problem generator, computational results and analysis are made and illustrated for the scheduling of multiple ground stations.
