基于改进K-means聚类和量子粒子群算法的多航迹规划

摘要

针对在复杂环境下需要通过多航迹规划以实现武器协同的问题，利用排挤机制产生 K-means聚类的初始聚类中心，并将改进K-means聚类与量子粒子群算法( QPSO)相结合应用于无人机的三维多航迹规划。改进算法解决了K-means聚类易陷入局部最优、聚类准确率低的问题。根据产生的初始聚类中心，将粒子划分成多个子种群，利用QPSO算法对每个子种群进行优化，使得每个子种群可以产生一条可行航迹。仿真分析证明了改进算法可以有效保证子种群之间的多样性，生成较为分散的多条可行航迹。%For the problem of multiple routes planning to realize the weapon cooperation in complex envi-ronment,K-means clustering is improved by an exclusion mechanism which generates the initial cluster centers. A method combining quantum-behaved particle swarm optimization( QPSO) with K-means cluste-ring is proposed and applied to 3-D multiple routes planning of unmanned aerial vehicle( UAV) . The im-proved algorithm solves the problem of falling in local best and improves the clustering accuracy. It classi-fies the particles to several subgroups. Then every subgroup is optimized by QPSO so as to generate a feasi-ble route. Finally,multiple and dispersive routes are constituted. Simulation proves that the improved algo-rithm can assure the variety of subgroups and generates feasible and diverse routes.