Orbital Maneuver Optimization of Earth Observation Satellites Using an Adaptive Differential Evolution Algorithm

Earth observation satellite (EOS) systems often encounter emergency observation tasks oriented to sudden disasters (e.g., earthquake, tsunami, and mud-rock flow). However, EOS systems may not be able to provide feasible coverage time windows for emergencies, which requires that an appropriately selected satellite transfers its orbit for better observation. In this context, we investigate the orbit maneuver optimization problem. First, by analyzing the orbit coverage and dynamics, we construct three models for describing the orbit maneuver optimization problem. These models, respectively, consider the response time, ground resolution, and fuel consumption as optimization objectives to satisfy diverse user requirements. Second, we employ an adaptive differential evolution (DE) integrating ant colony optimization (ACO) to solve the optimization models, which is named ACODE. In ACODE, key components (i.e., genetic operations and control parameters) of DE are formed into a directed acyclic graph and an ACO is appropriately embedded into an algorithm framework to find reasonable combinations of the components from the graph. Third, we conduct extensive experimental studies to show the superiority of ACODE. Compared with three existing algorithms (i.e., EPSDE, CSO, and SLPSO), ACODE can achieve the best performances in terms of response time, ground resolution, and fuel consumption, respectively.

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