Optimizing Multi-Spacecraft Cislunar Space Domain Awareness Systems via Hidden-Genes Genetic Algorithm

TL;DR

This paper introduces a multi-objective optimization approach using a hidden genes genetic algorithm to design effective and cost-efficient multi-spacecraft cislunar Space Domain Awareness systems, addressing complex dynamical challenges.

Contribution

It presents a novel application of hidden genes genetic algorithm for optimizing multi-spacecraft cislunar SDA architectures considering multiple objectives.

Findings

Optimized architectures balance cost and effectiveness.

The approach explores the full design space of cislunar SDA.

Demonstrates improved system design over traditional methods.

Abstract

This paper proposes an optimization problem formulation to tackle the challenges of cislunar Space Domain Awareness (SDA) through multi-spacecraft monitoring. Due to the large volume of interest as well as the richness of the dynamical environment, traditional design approaches for Earth-based architectures are known to have challenges in meeting design requirements for the cislunar SDA; thus, there is a growing need to have a multi-spacecraft system in cislunar orbits for SDA. The design of multi-spacecraft-based cislunar SDA architecture results in a complex multi-objective optimization problem, where parameters such as number of spacecraft, observability, and orbit stability must be taken into account simultaneously. Through the use of a multi-objective hidden genes genetic algorithm, this study explores the entirety of the design space associated with the cislunar SDA problem. A demonstration case study shows that our approach can provide architectures optimized for both cost and effectiveness.