A Novel Ant Colony and Sparrow Search Optimization-Based Routing Algorithm for LEO Satellite Networks

To improve the convergence capability of ant colony routing in LEO satellite networks, a novel hybrid ant colony-sparrow search optimization routing algorithm is proposed. The sparrow algorithm is used to identify local optimal solutions in the ACO routing process and guide the algorithm to re-converge. Simulation results show that, compared to the traditional ACO algorithm, the proposed algorithm significantly improves communication delay and reduces communication costs.

I. Introduction

Low Earth Orbit (LEO) satellite networks have gained significant attention in recent years due to their potential to provide global, low-latency communication services [1]. Unlike traditional geostationary satellites, LEO satellites operate at much lower altitudes, typically between 500 and 2000 kilometers above Earth's surface. This enables high-speed data transmission and reduced signal propagation delays, making LEO satellite networks ideal for applications such as real-time video streaming, global internet coverage, and Internet of Things (IoT) connectivity [2]. However, the dynamic nature of LEO satellite networks, characterized by frequent topology changes due to satellite mobility, poses significant challenges to network routing. Efficient routing algorithms are crucial to ensure reliable communication, minimize latency, and reduce communication costs in such rapidly changing environments.