Earth observation satellite system （EOSS） is the main space platform to collect ground information. Op- timization of EOSS is still a difficult problem, as it is a complex system concerning a great deal of design va...Earth observation satellite system （EOSS） is the main space platform to collect ground information. Op- timization of EOSS is still a difficult problem, as it is a complex system concerning a great deal of design variables and uncertain factors. To solve the problem, an optimization framework based on parallel system and computational experi- ments is proposed. An artificial system for EOSS is firstly constructed, which is the integration of resource data, task data, environment data and related operation rules. Real EOSS together with artificial EOSS constitute the parallel systems for EOSS. Based on the parallel systems, concept of computational experiments is detailed. Moreover, surrogate models are built to approximate real EOSS. Genetic algorithm and improved general pattern search method are adopted to optimize the model. According to the framework, a case study is carried out. Through the results, we illustrated the proposed framework to be useful and effective for EOSS optimization problem.展开更多
基金supported by the National Natural Science Foundation of China(Nos.71071156,70971131)
文摘Earth observation satellite system （EOSS） is the main space platform to collect ground information. Op- timization of EOSS is still a difficult problem, as it is a complex system concerning a great deal of design variables and uncertain factors. To solve the problem, an optimization framework based on parallel system and computational experi- ments is proposed. An artificial system for EOSS is firstly constructed, which is the integration of resource data, task data, environment data and related operation rules. Real EOSS together with artificial EOSS constitute the parallel systems for EOSS. Based on the parallel systems, concept of computational experiments is detailed. Moreover, surrogate models are built to approximate real EOSS. Genetic algorithm and improved general pattern search method are adopted to optimize the model. According to the framework, a case study is carried out. Through the results, we illustrated the proposed framework to be useful and effective for EOSS optimization problem.
