基于改进遗传算法的多弹混合瞄准点优化

Multi-missile aiming point optimization based on improved genetic algorithm

瞄准点优化是指通过对导弹瞄准点的选择使得对目标的打击效果达到或接近最佳。以具有不同毁伤效果的多弹型导弹打击变电所目标为例,采用改进遗传算法对多弹型导弹打击同一目标的瞄准点优化问题进行研究。结合变电所的目标易损性,选择合适的毁伤指标及对应类型的导弹武器,建立最优瞄准点求解模型。为解决传统遗传算法易陷入局部最优值的缺陷,建立存放优势个体的经验池,避免优势个体的丢失,并且,保留基因型相差较大的个体,增强种群多样性,避免算法过早收敛。经过计算,该算法可有效解决多弹混合瞄准点优化问题,相较于传统算法,整体寻优结果更佳,且具有良好的收敛性,因此,具有比较高的应用价值。

Aiming point optimization refers to the selection of the aiming point of a missile so that the strike effect on the target is at or near the best. As an example, a modified genetic algorithm is used to study the aiming point optimization problem of a multi-bullet missile striking the same target with different damage effects. Combined with the target vulnerability of the substation, suitable damage indicators and corresponding types of missile weapons are selected to establish the optimal aiming point solution model. In order to solve the defects of traditional genetic algorithm, an experience pool is established to store the dominant individuals to avoid the loss of dominant individuals and to retain the individuals with different genotypes to enhance the population diversity and avoid premature convergence of the algorithm. After calculations, the algorithm can effectively solve the problem of multi-bullet hybrid aiming point optimization, and the overall optimization result is better than the traditional algorithm, and has good convergence, which is of high application value.