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

TL;DR

This paper introduces new models and a reinforcement learning-based evolutionary algorithm framework to address the NP-hard satellite scheduling problem, aiming to improve solution efficiency for complex mission planning.

Contribution

It presents two novel satellite scheduling models and a reinforcement learning-based evolutionary algorithm framework, advancing solution methods for NP-hard satellite mission planning problems.

Findings

Proposed models effectively represent satellite scheduling complexities.

Reinforcement learning enhances evolutionary algorithm performance.

Framework shows promise in solving NP-hard satellite scheduling problems.

Abstract

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.