An Improved Genetic Algorithm for Flight Path Re-Routes with Reduced Passenger Impact

Abstract

Adverse weather has serious implications for flight timeliness, as well as passenger and aircraft safety. This often implies that alternative flight paths have to be used by aircraft to avoid adverse weather. To reduce the impact of such path re-routes, exact techniques such as artificial potential field model and Dijkstra's algorithms have been proposed. However, such approaches are often unsuitable for real time scenarios involving large number of waypoints and constraints. This has led to the use of metaheuristic techniques that give sub-optimal solutions in good time. In this work, an improved genetic algorithm-based technique has been proposed. The algorithm used an improved mutation operator, reduced passenger inconvenience and considered the schedules of aircraft.

Stratified opposition-based initialization for variable-length chromosome shortest path problem evolutionary algorithms

“…In (Chen et al, 2017), the GA SP was used for urban areas for rescue planning in disasters such as earthquakes. In (Ayo et al, 2017), the GA SP was used in air transportation as a quick method to suggest flight paths when rerouting was needed. In (Vignesh & Premalatha, 2019), the GA SP was used in military information system optimization.…”

Multi-Flight Rerouting Optimisation Based on Typical Flight Paths Under Convective Weather in the Terminal Area

“…The graph-theoretic algorithms mainly include algorithms such as the K shortest path algorithm [14] and A* algorithm [15]. The meta-heuristic optimisation algorithms mainly involve the ant colony optimisation algorithm [16], genetic algorithm [17,18], simulated annealing algorithm [19,20] and the SB-RRT* algorithm (Scenario-Based Rapidly-exploring Random Tree*) [21]. Machine learning algorithms include the RNN algorithm [22], HMM algorithm [23], CGAN algorithm [24], etc.…”