摘要
提出了一种自适应参数策略的混沌粒子群优化算法。该方法将自适应加速度系数调整策略引入到PSO中,以有效地控制全局和局部搜索,并利用混沌运动的遍历性在解空间产生较大规模的初始群体,从中择优选出分布均匀的初始种群以提高粒子的质量,同时根据种群适应度方差对陷入早熟收敛的粒子进行混沌扰动,提高算法收敛的精度。将该方法用于飞行控制器的参数优化设计中。仿真结果表明:使用该方法能够有效地解决飞行控制系统的参数优化设计,极大地提高了飞行控制器参数的设计效率。
A chaotic PSO algorithm of adaptive parameter strategy was proposed.In this method,an adaptive strategy of acceleration coefficients was incorporated into PSO algorithm to keep balance between global search and local search,meanwhile large scale initial population from which well-distributed initial population was chosen to improve quality of particles was generated in the solution space with ergodicity of chaos and chaotic perturbation based on fitness variance of population was utilized to avoid the particles being trapped into local convergence to improve precision in the search.The flight controller parameters were optimized using this method.The simulation results show that the optimal design of flight control system parameters can solve efficiently and the design efficiency is greatly increased with this method.
出处
《系统仿真学报》
CAS
CSCD
北大核心
2010年第5期1222-1225,共4页
Journal of System Simulation
关键词
混沌粒子群算法
自适应
飞行控制
参数寻优
chaotic particle swarm optimization
adaptive
flight control
optimization
