Abstract:

In optimizations with higher dimensional objectives, the non-dominated sorting genetic algorithm-Ⅲ (NSGA-Ⅲ) compared to other many-objective evolutionary algorithms, has the strong ability of diversity maintenance, but the convergence ability is still weak. On the basis that the genetic K-means (GKM) clustering algorithm is introduced to improve the convergence ability of the NSGA-Ⅲ, the NSGA-Ⅲ-GKM is proposed to solve the optimization problem of the fire distribution for multiple space-to-ground strike weapon (SGSW). Firstly, taking the minimum duration of the transfer trajectory, the velocity of the SGSW at the landing point, and the penetration angle of the SGSW at the landing point as optimization indexes, the optimization models of transfer trajectories are established, which lays a foundation of the computation for indexes of the fire distribution. Secondly, the optimization model of fire distribution is established based on the NSGA-Ⅲ-GKM. Finally, the simulation results demonstrate that the NSGA-Ⅲ-GKM has better diversity and convergence and optimization results than other representative many-objective evolutionary algorithms, which can effectively solve the fire distribution problem of multiple SGSW.

Key words: space-to-ground strike weapon (SGSW), multi-objective evolutionary algorithms, non-dominated sorting genetic algorithm-Ⅲ (NSGA-Ⅲ), fire distribution, genetic K-means (GKM)