Generalized Model and Deep Reinforcement Learning-Based Evolutionary Method for Multitype Satellite Observation Scheduling
Küçük Resim Yok
Tarih
2024
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Ieee-Inst Electrical Electronics Engineers Inc
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Multitype satellite observation, including optical observation satellites, synthetic aperture radar (SAR) satellites, and electromagnetic satellites, has become an important direction in integrated satellite applications due to its ability to cope with various complex situations. In the multitype satellite observation scheduling problem (MTSOSP), the constraints involved in different types of satellites make the problem challenging. This article proposes a mixed-integer programming model and a generalized profit representation method in the model to effectively cope with the situation of multiple types of satellite observations. To obtain a suitable observation plan, a deep reinforcement learning-based genetic algorithm (DRL-GA) is proposed by combining the learning method and genetic algorithm. The DRL-GA adopts a solution generation method to obtain the initial population and assist with local search. In this method, a set of statistical indicators that consider resource utilization and task arrangement performance are regarded as states. By using deep neural networks to estimate the $Q$ value of each action, this method can determine the preferred order of task scheduling. An individual update strategy and an elite strategy are used to enhance the search performance of DRL-GA. Simulation results verify that DRL-GA can effectively solve the MTSOSP and outperforms the state-of-the-art algorithms in several aspects. This work reveals the advantages of the proposed generalized model and scheduling method, which exhibit good scalability for various types of observation satellite scheduling problems.
Açıklama
Anahtar Kelimeler
Combinatorial Optimization Problem, Deep Reinforcement Learning (Drl), Evolutionary Algorithm (Ea), Generalized Model, Multitype, Satellite Observation, Scheduling
Kaynak
Ieee Transactions on Systems Man Cybernetics-Systems
WoS Q Değeri
N/A
Scopus Q Değeri
Q1
