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
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.