This paper proposed an improved particle swarm optimization (PSO) algorithm to solve the three-dimensional problem of path planning for the fixed-wing unmanned aerial vehicle (UAV) in the complex environment. The improved PSO algorithm (called DCAPSO) based dynamic divide-and-conquer (DC) strategy and modified algorithm is designed to reach higher precision for the optimal flight path. In the proposed method, the entire path is divided into multiple segments, and these segments are evolved in parallel by using DC strategy, which can convert the complex high-dimensional problem into several parallel low-dimensional problems. In addition, algorithm is adopted to generated an optimal path from the particle swarm, which can avoid premature convergence and enhance global search ability. When DCAPSO is used to solve the large-scale path planning problem, an adaptive dynamic strategy of the segment selection is further developed to complete an effective variable grouping according to the cost. To verify the optimization performance of DCAPSO algorithm, the real terrain data is utilized to test the performance for the route planning. The experiment results show that the proposed DCAPSO algorithm can effectively obtain better optimization results in solving the path planning problem of UAV, and it takes on better optimization ability and stability. In addition, DCAPSO algorithm is proved to search a feasible route in the complex environment with a large number of the waypoints by the experiment.

中文翻译：

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基于改进粒子群算法的固定翼无人机三维路径规划新方法

针对固定翼无人机(UAV)在复杂环境下的三维路径规划问题，提出了一种改进的粒子群优化(PSO)算法。改进的PSO算法（称为DCA PSO）基于动态分而治之（DC）策略和改进算法，旨在为最优飞行路径达到更高的精度。该方法将整个路径分为多个段，这些段采用DC策略并行演化，可以将复杂的高维问题转化为多个并行的低维问题。此外，采用算法从粒子群中生成最优路径，避免早熟收敛，增强全局搜索能力。当 DCAPSO用于解决大规模路径规划问题，进一步发展了自适应动态分段选择策略，根据代价完成有效的变量分组。为了验证DCA PSO算法的优化性能，利用真实地形数据来测试路线规划的性能。实验结果表明，所提出的DCA PSO算法在解决无人机路径规划问题时能有效获得较好的优化结果，具有较好的优化能力和稳定性。此外，通过实验证明了DCA PSO算法在具有大量航路点的复杂环境中搜索可行路线的能力。