基于IBA-MOEA的无人机航路规划多目标优化方法

Multi-Objective Optimization Method for UAV Route Planning Based on IBA-MOEA

为进一步增强传统无人机航路规划模型的适用性,提高求解此类问题的计算效率,首先建立以复杂山区和敌我双方对抗条件为背景的无人机路径规划模型,在此基础上提出了一种基于改进蝙蝠算法的多目标进化算法(IBA-MOEA),并使用此算法对模型进行了有效求解。所提算法将卷积粒子滤波与蝙蝠算法进行融合,并根据蝙蝠种群的特点,加入蝙蝠种群的交叉、变异策略,在克服算法搜索易进入局部最优的缺点时使用了搜索区间的自适应扩大方法,使算法性能具有较大优势。仿真结果表明,所提算法成功对模型进行了求解并获得了无人机规划航路,且在算法收敛性和种群分布性上相比同类算法具有相对较大优势。

To further enhance the applicability of the traditional UAV path planning model and improve the computational efficiency in solving such problems,the UAV path planning model is established in the complex mountain background and the condition of confrontation between the enemy and ourselves.On this basis,a multi-objective evolutionary algorithm based on improved bat algorithm(IBA-MOEA)is proposed and applied to solving the model effectively.This algorithm combines the convolution particle filter with the bat algorithm.According to the characteristics of the bat population,the strategies of crossover and mutation of the bat population are added.To overcome the disadvantage that the algorithm is easy to fall into local optimum,the algorithm adopts the adaptive expansion method of the search interval,thus greatly improving the performance of the algorithm.Simulation results show that this algorithm successfully solves the model and gets the path planning of UAV,exhibiting advantages over similar algorithms in convergence and population distribution.