Stochastic Optimization Techniques for the Design of Just-In-Time Collision Avoidance Constellations

We present a stochastic, multi-objective framework for designing dust-based Just-in-Time Collision Avoidance constellations. The formulation couples a catalog-informed synthetic conjunction generator with two competing objectives—total maneuver impulse and dust mass—under an 80% probabilistic remediation constraint. Our analysis of Walker, Dual Walker, Flower, and Arbitrary constellations using genetic algorithms, differential evolution, and particle swarm optimization reveals a convex Pareto trade-off, with practical designs achievable at approximately 0.2 km s−1 Δ�� and 50 kg dust mass under the assumed uncertainty models. The results demonstrate that Walker architectures offer the most compact Δ�� solutions for the target population. From an algorithmic perspective, robust optimization algorithms like R-NSDE proved essential for ensuring reliability under uncertainty, validating the No Free Lunch theorem’s implication that specialized solvers outweigh generic heuristics for this stochastic mission class.