Multi-objective optimization under uncertainty of satellite systems via simulated annealing

A method for performing multi-objective optimization under uncertainty in conceptuallevel multidisciplinary design is presented. The method is applied to a ship tracking and environmental protection satellite design problem seeking to optimize three outputs: coverage time, resolution, and total cost. Uncertainties are quantified and propagated using Monte Carlo simulation. Optimization is then performed via a multi-objective simulated annealing algorithm on each Monte Carlo sample. The single solution is selected as the best solution (according to a weighted sum of the outputs) from a tail-sample Pareto set. A composite solution is obtained as a composite of a subset of best solutions. This subset of best solutions consists of a user-defined number of solutions from all Pareto sets above a certain confidence level. A baseline solution, a deterministic multi-objective simulated annealing solution, the single solution, and the composite solution are compared. The optimization-based solutions all provide better solutions than the baseline system. The composite solution provides the best solution but a greater computational expense than the deterministic solution. A comparison of multi-spectral imager based systems is also made. The composite solution is again found to be the best solution especially under uncertainty where the deterministic and single-best solutions suffer from dramatic increases in the total cost at higher confidence-levels.