A Distance Similarity-based Genetic Optimization Algorithm for Satellite Ground Network Planning Considering Feeding Mode

TL;DR

This paper introduces a novel genetic optimization algorithm tailored for satellite ground network planning, aiming to enhance data transmission efficiency by considering feeding modes and task similarities.

Contribution

It proposes a distance similarity-based genetic algorithm (DSGA) that improves solution quality for satellite network planning by incorporating task similarity measures.

Findings

The DSGA algorithm effectively maximizes network task profits.

Incorporating task similarity improves solution quality.

The method addresses constraints related to satellite feeding modes.

Abstract

With the rapid development of the satellite industry, the information transmission network based on communication satellites has gradually become a major and important part of the future satellite ground integration network. However, the low transmission efficiency of the satellite data relay back mission has become a problem that is currently constraining the construction of the system and needs to be solved urgently. Effectively planning the task of satellite ground networking by reasonably scheduling resources is crucial for the efficient transmission of task data. In this paper, we hope to provide a task execution scheme that maximizes the profit of the networking task for satellite ground network planning considering feeding mode (SGNPFM). To solve the SGNPFM problem, a mixed-integer planning model with the objective of maximizing the gain of the link-building task is constructed, which considers various constraints of the satellite in the feed-switching mode. Based on the problem characteristics, we propose a distance similarity-based genetic optimization algorithm (DSGA), which considers the state characteristics between the tasks and introduces a weighted Euclidean distance method to determine the similarity between the tasks. To obtain more high-quality solutions, different similarity evaluation methods are designed to assist the algorithm in intelligently screening individuals.