基于改进NSGA-Ⅲ的多SGSW火力分配优化

Optimization of fire distribution for multiple SGSW based on improved NSGA-Ⅲ

在高维多目标优化中,基于参考点非支配排序遗传算法(non-dominated sorting genetic algorithm-Ⅲ,NSGA-Ⅲ)相比于其他多目标进化算法,具备较强的多样性保持能力,但收敛能力存在一定不足。因此引入遗传K均值(genetic K-means,GKM)聚类算法以提高NSGA-Ⅲ的收敛能力,提出基于NSGA-Ⅲ-GKM算法的多天基对地打击武器(space-to-ground strike weapon,SGSW)火力分配优化方法。首先,建立以转移时间最短、落地点速度最大和落地点侵彻角最大为优化目标的SGSW转移轨道优化模型,为后续优化目标的计算打下基础;其次,建立基于NSGA-Ⅲ-GKM算法的火力分配优化模型;最后,仿真结果表明,NSGA-Ⅲ-GKM算法相比于其他代表性多目标进化算法具备较好的多样性保持能力和收敛能力,总体性能较好,该方法能够更有效地解决多SGSW火力分配优化问题。

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.