Mathematical Models and Reinforcement Learning based Evolutionary Algorithm Framework for Satellite Scheduling Problem

For complex combinatorial optimization problems, models and algorithms are at the heart of the solution. The complexity of many types of satellite mission planning problems is NP-hard and places high demands on the solution. In this paper, two types of satellite scheduling problem models are introduced and a reinforcement learning based evolutionary algorithm framework based is proposed. Problem Description The EDSSP problem is to designate a time-ordered task execution sequence for electromagnetic detection satellites [1]. The goal is to maximize the detection sequence profit while satisfying various satellite constraints.