Research and Simulation of Resource Scheduling Algorithm based on Multi-Objective Optimization in Space-Based Environment

This paper investigates the resource scheduling problem in space-based information networks and proposes a multi-objective optimization-based scheduling algorithm. First, a dynamic task priority model is introduced, which considers both task value and urgency. A scheduling architecture with high-orbit satellites as control nodes is developed, and multi-dimensional resources are modeled using a quadruple. Next, a reinforcement learning-based resource scheduling strategy is designed, where the scheduling problem is modeled as a Markov Decision Process. The algorithm is improved through adaptive reward functions, penalty factors, and an expert experience pool, optimizing task scheduling efficiency.