粒子群优化
计算机科学
差异进化
数学优化
局部最优
趋同(经济学)
路径(计算)
运动规划
操作员(生物学)
算法
人工智能
数学
生物化学
化学
抑制因子
转录因子
经济
基因
经济增长
机器人
程序设计语言
作者
Chen Huang,Xiangbing Zhou,Xiaojuan Ran,Jiamiao Wang,Huayue Chen,Wu Deng
标识
DOI:10.1016/j.engappai.2023.105942
摘要
Particle swarm optimization (PSO) algorithm has a potential to solve route planning problem for unmanned aerial vehicle (UAV). However, the traditional PSO algorithm is easy to fall into local optimum under the complicated environments with multiple threats. In order to improve the performance in different complicated environments, a novel and effective PSO algorithm with adaptive adjustment of the parameters, cylinder vector and different evolution operator, named ACVDEPSO, is proposed and demonstrated to be effective for route planning problem for UAV. In the proposed ACVDEPSO, the velocity of the particle is converted to its cylinder vector for the convenience of the path search. It is worth highlighting that the parameters of ACVDEPSO algorithm are automatically chosen by the time and the fitness values of the particles. Furthermore, a challenger based on differential evolution operator is introduced to reduce the probability of falling into local optimum and accelerate the algorithm convergence speed. The simulation experiments have been conducted in real digital elevation model (DEM) maps to test the performance of the ACVDEPSO. The experiment results validate that the optimization performance of the ACVDEPSO outperforms the other comparison methods, which can efficiently generate a higher quality path for UAV under the complicated 3D environments.
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