Genetic Programming With Multifidelity Surrogates for Large-Scale Dynamic Air Traffic Flow Management

Abstract

Dynamic air traffic flow management (DATFM) aims at flexibly balancing air traffic demand with limited airspace by scheduling aircraft, particularly during unforeseen events, to maintain efficiency in aviation operations. Genetic programming (GP) has shown success in evolving effective heuristics across various domains. However, directly adopting GP to DATFM may be less effective due to the computationally intense simulations required for large-scale aircraft decision-making over broad airspace. To address the issue, we develop a novel multifidelity surrogate-assisted GP. The core idea is that if a computationally efficient low-fidelity surrogate provides enough information to guide the population toward promising areas effectively, then employing more accurate but resource-intensive evaluations would only increase computational effort without enhancing the direction of evolution. A key innovation in our method is a surrogate management strategy that automatically determines when and which surrogate model to use, based on collective information from the evolving population. This approach allows for more effective management of computational resources during the evolutionary process, enabling exploration of a broader range of the heuristic space and increasing the likelihood of identifying promising solutions. The proposed method has been tested on various benchmark instances derived from actual air traffic data. Extensive experimental results demonstrate that the proposed algorithm significantly outperforms current state-of-the-art methods in both effectiveness and efficiency.