摘要
针对融合系统中系统误差未固定的情况,将模拟退火算法SA(Simulated Annealing)引入到改进的粒子群优化算法PSO(Particle Swarm Optimization)中来解决系统误差配准问题。该方法结合了改进PSO的全面、快速寻优能力和SA的概率突跳特性,解决了PSO容易陷入局部最优的缺点,也保证了群体的多样性,避免了种群的退化。仿真结果表明,改进的SA-PSO方法较PSO、GA方法在系统误差配准精度上得到了提高。
System error is change in some fusion system. In order to resolve the problem of system error registration, this paper combine improved Particle Swarm Optimization （PSO） with Simulated Annealing （SA）.This method integrates the capacity of fast optimization seeking in improved PSO with the characteristic ofprobabilistic leap in SA, which not only solves the defect that PSO easily plunges into local optimum, but also ensures population variety and avoids population degradation. The simulations show that compared with PSO and GA, improved SA-PSO has better precision in system error registration.
出处
《光电工程》
CAS
CSCD
北大核心
2010年第9期27-31,38,共6页
Opto-Electronic Engineering
基金
国家自然科学基金重点项目(60634030)
国家自然科学基金项目(60702066)
