Adaptive-Strategies-Based Quantum Genetic Algorithm for Agile Earth Observation Satellite Scheduling Problem

The agile Earth observation satellite scheduling problem (AEOSSP) has been the subject of more research in recent years. Still, numerous researchers ignore the complex constraints of the scheduling model and the enormous complexity of solving the scheduling model. This article is grounded in practical engineering applications and utilizes an adaptive-strategies-based quantum genetic algorithm (ASQGA) to settle complex constraints and solution space. This research includes the development of a refined satellite scheduling model that integrates a variety of constraints, considering the attitude maneuvering capability of an agile satellite, the continuous working capability of the payload, the energy balance, and the storage space balance of the entire satellite system. Inspired by the genetic algorithm and quantum genetic algorithm (QGA), an improved QGA based on an adaptive evolutionary strategy and a multiconstraints conflict resolution-based adaptive mutation strategy is proposed. Numerical simulation results demonstrate that the method solves the AEOSSP for large-scale tasks with notable advancements in computational speed and scheduling solution revenue. This article also analyzes the impact of dynamic adaptive evolutionary parameters on the efficiency of scheduling solutions. For satellite engineering projects with large-scale tasks, the method studied in this article can offer a viable solution to the AEOSSP.