Systems Engineering and Electronics ›› 2025, Vol. 47 ›› Issue (2): 555-567.doi: 10.12305/j.issn.1001-506X.2025.02.22

• Systems Engineering • Previous Articles    

Multi-layer coding genetic algorithm-based approach to force action planning for carrier aircraft fleets

Haonan WU1, Wei HAN1, Zishuang PAN1, Fang GUO1, Xichao SU2,*   

  1. 1. Aviation Foundation College, Naval Aeronautical University, Yantai 264001, China
    2. School of Aviation Operation and Support, Naval Aviation University, Yantai 264001, China
  • Received:2023-12-18 Online:2025-02-25 Published:2025-03-18
  • Contact: Xichao SU

Abstract:

The cooperative operation of multiple types of carrier aircraft is the key challenge to develop the combat capability of carrier aircraft formation. Therefore, the scientific design of carrier aircraft group force action planning and the allocation of related resources are of great significance to improve the combat efficiency of carrier aircraft. Firstly, based on the characteristics of the carrier aircraft's land assault combat mission, force composition, ammunition loading and other constraints, and the integrated operational scheduling of force action planning is studied. Secondly, the integrated combat scheduling model is carried out for the key stages of carrier aircraft group departure, track planning, air refueling, cooperative combat, landing aircraft recovery, etc., and the heuristic-multi-layer coding genetic algorithm is introduced to decoupage the coupling relationship between each key stage. Thirdly, the improved convex optimization algorithm is used to plan the trajectory path of the carrier aircraft in the case and to calculate the flight time, which is used as the input data for force action planning and scheduling. Finally, based on the case simulation, the time series planning and the scientific allocation of related resources for the force action planning and scheduling of 14, 18, 20 and 24 carrier aircrafts are carried out respectively, and the feasibility and robustness of the designed model and proposed algorithm are verified.

Key words: heuristic-multi-layer coding, genetic algorithm, convex optimization, integrated combat scheduling

CLC Number: 

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