基于混沌DESAPSO算法的无人机三维航迹规划

3-D Route Planning of UAV Based on Chaotic DESAPSO Algorithm

粒子群(PSO)算法易陷入局部最优,将其运用于无人机三维航迹规划会导致航迹品质不高,针对这一问题,提出了将差分进化(DE)算法、模拟退火(SA)算法嵌入PSO算法中,构成混沌差分进化模拟退火粒子群(DESAPSO)算法,从三个方面提高了航迹品质:一是利用DE算法的变异、交叉及竞争选择增加种群多样性;二是利用SA算法概率突跳能力跳出局部最优解;三是对PSO算法参数进行混沌优化,进一步增强种群多样性。仿真结果表明:混沌DESAPSO算法改进航迹品质效果明显,获得了满意的无人机三维航迹。

Particle Swarm Optimization （PSO） algorithm is easy to fall into local optimum, and being applied to 3-D route planning of UAV will result in poor quality. To solve this problem, the chaotic DESAPSO algorithm was put forward by embedding the differential evolution （DE） algorithm and the simulated annealing （SA） algorithm in the chaotic PSO algorithm. The quality of the route was improved from three aspects, the first is to increase diversity of the population by using variation, crossover and competitive selection of DE algorithm; and the second is to jump local optimum by using the probabilistic jumping ability of SA algorithm; and the third is to increase diversity of the population further by chaotic optimization in the parameters of PSO algorithm. The results of simulation show that the quality of route was obvious improved by the chaotic DESAPSO algorithm, and the 3-D route was satisfied.