Multi-Objective Optimization of Computation Offloading in Space Systems under review
Journal of Aerospace Information Systems (AIAA) · submitted Jul 2026 · second author
Where should a computational task run — onboard flight computer, orbital data
center, ground station, or terrestrial cloud — when latency, cost, energy, radiation,
security, and reliability all conflict? The paper reframes an earlier single-utility
model as multi-objective trade-space search: a workload is a split across compute
tiers — a point on the tier simplex — and the answer is the Pareto front of feasible
splits, not one utility number.
The computational core is my work. The two-stage framework: discrete flight-computer selection under hard TRL and radiation gates, then continuous routing of the workload over the simplex. Both mission evaluators — including the nonlinear one, built from seven separately-ablatable subsystem couplings drawn from spaceflight engineering (M/M/1 queueing on latency, contact-window saturation, link-setup energy, security-protocol overhead, thermal-margin degradation, congestion-driven link availability, silent-data-corruption compounding), all carried into the paper intact. And the six-optimizer benchmark, compared on hypervolume, spacing, and feasible rate, with constraint-domination handling hard mission limits without penalty tuning.
Python · NSGA-II · SPEA2 · MOEA/D · MOPSO · ε-constraint · projected gradient