基于改进粒子群算法的军航多机场终端区空域进离场点规划方法OA

Aiming at the problem of insufficient dynamic adaptability of existing methods to the terminal airspace of multiple military airports,through a comparison of opertational differences between military and civil aviation,this paper proposes redundancy design and dynamic adjustment mechanisms to constitute a multi-objective optimization model with dual goals,i.e.traffic flow balancing and minimization of total flight path length through making a comparison between military and civil aviation operations.On the basis of the traditional Particle SwarmOptimization(PSO)algorithm,this paper designs an improved Multi-Ob-jective Particle Swarm Optimization algorithm(MOPSO)by integrating the local optimization mechanism of Simulated Annealing(SA),the non-dominated sorting strategy of NSGA-Ⅱ,and initialization using Lo-gistic chaotic mapping.Furthermore,the entropy weight-TOPSIS method is employed to conduct a com-prehensive evaluation and selection of the solution set,ultimately determining the optimal arrival and de-parture fixes planning scheme.The results show that compared with the conventional Multi-Objective Par-ticle Swarm Optimization(MOPSO)algorithm,this proposed improved algorithm achieves an average im-provement of 23％approximatelyin the Hypervolume metric,and the Inverted Generational Distance met-ric and the Spacing metric decrease by approximately 45％and 39％respectively.This method can effective-ly balance the contradiction between traffic balance and flight path optimization,providing a scientific basis for the efficient planning of military aviation terminal airspace in multi-airport areas.