摘要
结合遗传算法和模拟退火算法,构造出具有全局搜索优化特性的遗传模拟退火算法。根据空间目标表面的多组多角度双向反射分布函数(bidirectional reflectance distribution function,BRDF)实验数据和统计模型,获得样片BRDF五参数模型参数值及2D、3D的BRDF分布。比较基本遗传算法和遗传模拟退火算法在迭代次数、计算时间、参数值及精度等之间的差异并分析其原因。遗传模拟退火算法更适用于BRDF的统计建模。
Combining the basic genetic algorithm（GA） with the simulated annealing algorithm,a genetic simulated annealing algorithm（GSAA） is obtained with the character of global searching and optimum.Both GA and GSAA are used to fit bistatic mulitiangle data of experiment to the bidirectional reflectance distribution function（BRDF） statistical model.The parameters of the model and the 2D and 3D BRDF are obtained.The differences and causations between GA and GSAA in the iterative numbers,time,precision,data fitting and parameters are compared and analyzed.GA and GSAA held for BRDF statistic modeling.
出处
《系统工程与电子技术》
EI
CSCD
北大核心
2010年第7期1529-1531,共3页
Systems Engineering and Electronics
基金
国家自然科学基金(60771038)资助课题
关键词
双向反射分布函数
建模
遗传算法
遗传模拟退火算法
bidirectional reflectance distribution function
modeling
genetic algorithms
genetic simulated annealing algorithms
