摘要
针对布谷鸟搜索算法在多维函数优化搜索中存在收敛速度慢和寻优精度不高的缺点,提出一种逐维加权的布谷鸟搜索算法。该算法引入逐维动态加权的鸟窝位置更新方式,每次迭代后保留上一代的最优位置并进行下一代位置更新,同时改写偏好随机游动的步长更新方式。通过6个标准的测试函数的测试结果表明,改进后的算法,在提高算法的收敛速度和寻优精度上有效。
Aiming at the weakness of low convergence rate and poor optimization accuracy exposed by cuckoo search algorithm in multi-dimensional function optimization,this paper presents a cuckoo search algorithm with dimension by dimension weighting. The improved cuckoo search algorithm adopts nest position updating proposed by dynamic weight dimension by dimension. The latter nest position is updated on the basis of the former best nest position that has been saved after each iteration,and the step updating of preferred random flight is revised at the same time. According to the simulation results collected of six standard trial function,the improved algorithm shows better convergence rate and optimization accuracy.
出处
《西华师范大学学报（自然科学版）》
2017年第1期111-116,共6页
Journal of China West Normal University(Natural Sciences)
基金
四川省教育厅自然科学基金项目(14ZA0127)
西华师范大学博士启动基金项目(12B022)
西华师范大学校级创新团队(CXTD2015-4)
关键词
布谷鸟搜索算法
函数优化
逐维加权
cuckoo search algorithm
function optimization
dimension by dimension weighting
