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Acta Aeronautica et Astronautica Sinica ›› 2023, Vol. 44 ›› Issue (17): 328116-328116.doi: 10.7527/S1000-6893.2023.28116

• Electronics and Electrical Engineering and Control • Previous Articles     Next Articles

Optimal fire distribution method of small diameter guided bomb in air-to-surface strike based on multi-factor modified NSGA-Ⅱ

Wenhao BI(), Jiuli ZHOU, Xiaobo DUAN, An ZHANG, Shuangfei XU   

  1. School of Aeronautics,Northwestern Polytechnical University,Xi’an  710072,China
  • Received:2022-10-14 Revised:2022-11-16 Accepted:2023-02-21 Online:2023-09-15 Published:2023-03-03
  • Contact: Wenhao BI E-mail:biwenhao@nwpu.edu.cn
  • Supported by:
    National Natural Science Foundation of China(61903305);Fundamental Research Funds for the Central Universities(HXGJXM202214)

Abstract:

To maximize their strike efficiency against area targets, this paper proposes an optimal firepower allocation method for small diameter guided bombs in air-to-surface strike based on Multi-Factors Modified Non-Dominated Sorting Genetic Algorithm-II (MFM-NSGA-II). Considering the error dispersion of the guided bomb, an evaluation model of the strike efficiency of a single guided bomb is established by using the grid method. On this basis, the optimal fire distribution model of multiple guided bombs is established. The optimization objective function for the maximum strike efficiency and the minimum amount of ammunition is constructed, and the optimal fire distribution model of the guided bomb is established. By introducing the probability selection operator, hybrid crossover operator and improved elite retention strategy, the multi-factors in Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) are improved to enhance the global search ability of the algorithm and improve the performance of the algorithm. The simulation results show that the MFM-NSGA-II can obtain an effective optimal firepower allocation scheme for small diameter guided bombs. The amount of ammunition corresponding to the optimal firepower allocation result changes with the change of scene parameters, and the solution quality of the proposed method is better than that of the original NSGA-II and multi-objective particle swarm optimization.

Key words: small diameter guided bomb, area target, fire distribution, multiobjective optimization, Non-Dominated Sorting Genetic Algorithm-II (NSGA-II)

CLC Number: