A Genetic Algorithm Framework for Optimizing Three-Impulse Orbital Transfers with Poliastro Simulation

TL;DR

This paper introduces a genetic algorithm framework coupled with Poliastro to optimize three-impulse orbital transfers, successfully validating classical solutions and discovering more efficient high-energy trajectories with significant fuel savings.

Contribution

It presents a flexible computational framework that combines genetic algorithms with open-source orbital mechanics tools for autonomous trajectory optimization.

Findings

GA converges to Hohmann transfer for LEO-GEO case

GA finds a superior Bi-elliptic transfer for high-energy orbit

High-energy transfer saves 213.47 m/s ΔV but takes over 140 years

Abstract

Orbital maneuver planning is a critical aspect of mission design, aimed at minimizing propellant consumption, which is directly correlated with the total velocity change ($\Delta V$). While analytical solutions like the Hohmann and Bi-elliptic transfers offer optimal strategies for specific cases, they lack the flexibility for more general optimization problems. This paper presents a computational framework that couples a Genetic Algorithm (GA) with the Poliastro orbital mechanics library to autonomously discover fuel-optimal, three-impulse transfer trajectories between coplanar circular orbits. We validate this framework across two distinct scenarios: a low-energy transfer from Low Earth Orbit (LEO) to a Geostationary Orbit (GEO), and a high-energy transfer to a distant orbit with a radius 20 times that of LEO. Our results demonstrate the framework's remarkable adaptability. For the LEO-to-GEO transfer, the GA precisely converges to the classical Hohmann transfer, achieving an identical $\Delta V$ of 3853.96 m/s and validating the method's accuracy. Conversely, for the high-energy transfer, the GA identifies a superior Bi-elliptic trajectory that yields a significant $\Delta V$ saving of 213.47 m/s compared to the Hohmann transfer. This fuel efficiency, however, necessitates a trade-off, extending the mission duration from approximately 1 day to over 140 years. This work demonstrates an accessible and powerful toolchain for the rapid prototyping of optimal trajectories, showcasing how combining evolutionary algorithms with open-source libraries provides a robust method for solving complex astrodynamics problems and quantifying their critical design trade-offs.