Trading-off simulation fidelity and optimization accuracy in air-traffic experiments using differential evolution

In many engineering applications, black-box optimization relies on the use of a simulation to obtain a numeric evaluation or a score for a proposed solution. The cost of optimization is mostly a reflection of the cost of running this simulation environment. On the one hand, the higher the fidelity of the simulation environment, the longer it is likely to take to evaluate a single solution. Consequently, less solutions are allowed to be evaluated given a time constraint on the running time of the optimization algorithm. On the other hand, the lesser the fidelity of the simulation environment, the more likely more solutions could be evaluated within the same time constraint. However, understanding the relationship between fidelity and the quality of the final solution obtained by the optimization method is largely an unexplored area of research. In this paper, we present an approach for adjusting taskload of Air traffic controllers (ATC) in real time by using three different shadow simulators of increasing fidelity and Differential Evolution (DE) as the evolutionary optimization algorithm. According to air traffic conditions, DE optimizes a goal programming model to steer the taskload up or down towards a predefined taskload target by generating two ATC requests every 10 minutes. The results demonstrate how a high fidelity simulator can help DE to achieve better solution quality in the absence of any time constraint on running the experiments. However, when there is a tight time constraint imposed, lower fidelity simulators allow DE to explore more solutions in the search space by cutting down on the extra time needed when higher fidelity simulators are used.