## 1 Introduction  
Earth observation satellites (EOSs) are pivotal in environmental monitoring, disaster response, national security, etc. [1, 2]. EOSs typically orbit in the low Earth orbit, however, through the satellite attitude maneuver system, the attitude of a satellite can be adjusted in order to locate the ground target that has to be captured by the satellite camera. These images are then stored in the onboard storage memory and downloaded to a ground station when the satellite passes by the ground station. When the satellite is operating, the electrical power subsystem of the satellite provides electrical energy to each component and also gains recharging electrical power from solar arrays that convert solar energy into electrical power. Some typical EOSs are IKONOS [3], PLEIADES [4], FY, and HY [5]. To carry out observation missions, the satellite's missions are scheduled and on-board actions are arranged reasonably according to the satellite's state, target location, and the ground station's location, given that the constraints are satisfied so that the satellite can observe its target and transmit the observation data back to the ground station [6, 7]. Due to the advent of agile Earth observation satellites (AEOSs), more observation data can be obtained within a period of time as the satellite's maneuverability and device capabilities have been greatly improved [8, 9]. An Earth observation satellite can execute more missions because its capabilities are improved with the cooperation of multiple subsystems. Meanwhile, it is accompanied by more complicated coupling relationships among subsystems as well as the scarcity of resources such as data memory and electrical energy. As a result, a new challenge is raised in terms of how to schedule the satellite mission more reasonably so that the satellite can carry out more missions while meeting the constraint requirements. The Earth observation satellite mission scheduling problem can be solved by both heuristic scheduling methods and meta-heuristic scheduling algorithms [10]. Heuristic scheduling methods are used to design scheduling strategies for solving scheduling problems based on problem analysis and previous experience. [PERSON] [11] proposed a temporal conflict network-based heuristic algorithm, sorted the targets using a temporal conflict network, and executed target selection and scheduling according to the order of the targets. [PERSON] [12] selected targets according to the strategy of maximizing target priority and weight acquisition, and executed the decision arrangement of the observation, attitude pointing, data transmission, and equipment switching by sequentially making different levels of decision to solve the scheduling problem. Furthermore, [PERSON] [13] modeled the scheduling problem as a complex network, defined two factors to describe the node/target importance, and obtained the effective scheduling results based on the two factors. [PERSON] [14] proposed a scheduling method for dynamically adjusting priority ranking based on factors such as the target weight, image quality, and satellite workload balancing. [PERSON] [15] calculated the urgency degree and merging degree according to the task urgency, value, and observation opportunity, sorted targets by the urgency degree and merging degree, and designed four strategies of target insertion into the existing target sequence to solve the scheduling problem. [PERSON] [16] proposed an efficient heuristic with a set of precedence rules for satellite onboard activity, which could achieve real-time planning of the onboard mission. According to the aforementioned research, heuristic methods solve scheduling problems with a definite planning strategy, giving them the advantage of high computation efficiency. However, since the scheduling strategy is commonly designed for specific conditions, it lacks adaptability to different working conditions, especially when the coupling between multiple satellite subsystems increases, which lead to different coupling states.