Dynamic airspace sectorization via improved genetic algorithm

Abstract

This paper deals with dynamic airspace sectorization (DAS) problem by an improved genetic algorithm (iGA). A graph model is first constructed that represents the airspace static structure. Then the DAS problem is formulated as a graph-partitioning problem to balance the sector workload under the premise of ensuring safety. In the iGA, multiple populations and hybrid coding are applied to determine the optimal sector number and airspace sectorization. The sector constraints are well satisfied by the improved genetic operators and protect zones. This method is validated by being applied to the airspace of North China in terms of three indexes, which are sector balancing index, coordination workload index and sector average flight time index. The improvement is obvious, as the sector balancing index is reduced by 16.5 %, the coordination workload index is reduced by 11.2 %, and the sector average flight time index is increased by 11.4 % during the peak-hour traffic. Keywords Dynamic airspace sectorization (DAS) Á Improved genetic algorithm (iGA) Á Graph model Á Multiple populations Á Hybrid coding Á Sector constraints

Cognitive dynamic airspace management

“…[38,118,106,123,67,101] ) are generally referred to the airspace sectorization problem. Over the years, the problem has been modelled as a graph partitioning problem consisting of air tra c flows [38,17,109,80,77,24,98], a combinatorial problem of elementary building blocks [21,123,26,39,76,18,58,74,67] and a continuous black-box problem with the use of computational geometry [106,119,107]. The use of Voronoi Diagram as a computational geometry tool in the airspace sectorization problem posed as a continuous black-box problem is shown to be e↵ective in previous studies [106,107].…”

“…The use of Voronoi Diagram as a computational geometry tool in the airspace sectorization problem posed as a continuous black-box problem is shown to be e↵ective in previous studies [106,107]. The variation of objectives and constraints used in the formulation of the airspace sectorization problem have been built into mostly single 1 objective [38,37,98,119,24,101] and multi-objective [106] done to re-route the aircraft [93,73,117,63,87]. However, only Yousefi et al [122] addressed the need for resectorization to provide a well-balanced workload for the ATC after re-routing the airways.…”

“…• Top-down Approach: Division of airspace to form smaller sectors -Graph-based [38,17,109,80,77,24,98]: Modelled as a NP-complete combinatorial problem of graph partitioning, usually require post-processing -(Computational) Geometry-based [37,105,119,106,101,107]: Sectors are formed using geometry methods and are usually paired with evolutionary algorithms…”

“…After the problem have been resolved, the final sectors are to be constructed in geometric post-processing [38]. Following this fundamental framework, works focusing on di↵erent methods of computation [109,24,17] and computational geometry [77,80,98] have been explored.…”

“…Many previous works have considered both workloads as an objective-constraint pair [123,109,17,80,21,39,77,107], optimising only one of the workload while bounding the other. Some works considered optimising both workloads using a weighted sum in a single-objective optimisation model [38,37,98,119,24,101]. However, the workloads are conflicting in nature and should be modelled as a multi-objective optimisation problem like in [106].…”