基于蛇鹭优化算法的多站协同构型优化方法OA

The optimization of the types and quantities of multi-station deployment is a hot issue in the field of passive positioning.The quality of station deployment directly affects the accuracy of target positioning and track-ing.Limited by the convergence ability of the optimization algorithm,the traditional algorithm has a slow conver-gence speed and can only find suboptimal solutions as alternative configurations,which affects the positioning per-formance of passive localization.In this study,GDOP(Geometric Dilution of Precision)is used to achieve efficient site deployment by improving the snake-heron optimization algorithm.To address the issue of slow convergence speed of the optimization algorithm,an adaptive distance limit between two observation stations is proposed to pre-vent the snake-heron optimization algorithm from being trapped in local optimal solutions for a long time.In view of the problem of poor stability of the optimization algorithm,it is proposed to adopt binary coding to simulate the itera-tive way of species renewal in nature to enhance the diversity of population renewal.In view of the insufficient con-vergence ability of the optimization algorithm,a hybrid operator model is proposed for species update and iteration.The snake-heron optimization algorithm is improved to adapt to the multi-station configuration optimization prob-lem.The experimental results show that in terms of optimization ability and optimization speed,the performance of the proposed algorithm is superior to that of the traditional algorithm.