Multi-Objective, Multidisciplinary Design Optimization Methodology for Distributed Satellite Systems

A multi-objective, multidisciplinary design optimization methodology for mathematically modeling the distributed satellite system conceptual design problem as an optimization problem has been developed. The tradespace for distributed satellite systems can be enormous, too large to enumerate, analyze, and compare all possible architectures. The seven-step methodology enables an efficient search of the tradespace for the best families of architectures during the conceptual design phase. Four classes of optimization techniques are investigated, Taguchi, heuristic, gradient, and univariate methods. The heuristic simulated annealing algorithm found the best distributed satellite system architectures with the greatest consistency due to its ability to escape local optima within a nonconvex tradespace. The conceptual design problem scope is then broadened by expanding from single-objective to multiobjective optimization problems, and two variant multi-objective simulated annealing algorithms are developed. Finally, several methods are explored for approximating the true global Pareto boundary with only a limited knowledge of the full design tradespace. In this manner, the methodology serves as a powerful, versatile systems engineering and architecting tool for the conceptual design of distributed satellite systems. Nomenclature Av = system availability C = capability E = system energy L = mission duration N = number of dimensions in the objective function P = candidate Pareto optimal set P ∗ = true Pareto optimal set p = state probability T = system temperature ˜ T = arithmetic mean t = time U = utility y = mission year Γ = design vector Γb = current baseline design vector Γi = initial design vector γ = design vector variable � = objective function value difference θr = angular resolution, marcsec � = life-cycle cost, $ χ = random number between 0 and 1 � = system performance � = null depth