基于种群多样性测度的PSO算法及其应用研究

摘要

Considering the basic particle swarm optimization' s limitation of easy partial constriction, an improved particle swarm optimization algorithm based on dynamic modulation of population diversity measures inertia weight is designed. Emulation and comparison with basic particle swarm optimization, adaptive particle swarm optimization, and contractive particle swarm optimization show that the improved particle swarm optimization algorithm has advantage in enhancing integrate hunting ability. The improved particle swarm optimization algorithm is applied in airborne craft' s path planning and the effectiveness of the algorithm is validated by imitation.%针对基本粒子群算法(particle swarm optimization,PSO)易局部收敛的缺陷,设计了一种根据种群多样性测度动态调整惯性权重的改进粒子群算法,通过仿真测试函数与基本粒子群算法、自适应粒子群算法(adaptive particle swarm optimization,APSO)、带收缩因子的粒子群算法(contractive particle swarm optimization,CPSO)进行比较,改进的PSO算法在提高算法的综合搜索能力方面具有优越性.将改进的PSO算法运用到作战飞行器航迹规划中,并进行了仿真实验,仿真结果验证了改进算法的有效性.