System Architecture Optimization: An Example Application to Space Mission Planning

Abstract

Space mission planning involves coupled architecture decisions that non-trivially influence system-level performance metrics such as weight, power usage, and scientific value. We present an exemplary space mission planning problem solved using System Architecture Optimization (SAO): a technique where numerical optimization algorithms are used to explore the architecture design space and find a Pareto front of architectures. The architecture design space is modeled based on functions using the Architecture Design Space Graph (ADSG) implemented in the ADORE editing and optimization tool. Evaluation code is implemented in Python and linked to the design space model using class factories. The design space is explored using NSGA-II, a multi-objective evolutionary algorithm, resulting in a Pareto front trading-off system mass and total experiment duration.