摘要
将云的生成、动态运动、降雨和再生成等自然现象与智能优化算法的思想融合,设计了一种新的智能优化算法——云搜索优化算法(Clouds Search Optimization Algorithm)。云团内部水滴可以产生差商信息来指导搜索,差商可以逼近梯度,且负差商与负梯度同样为函数值下降方向。基于此,进一步提出带差商信息的云搜索优化算法(Clouds Search Optimization Algorithm with Difference Quotient Information)。依据差商与梯度的近似关系,证明了DCSO具有类似经典的基于梯度的优化算法的收敛性,最优水滴可以收敛到极值点。benchmark函数的数值实验表明,CSO与DCSO都具有很强的寻优能力,且差商信息可以指导水滴迅速向极值点移动,大大提高了DCSO的收敛速度。
Clouds have many natural phenomena such as generation,dynamic movement,rainfall and regeneration.A novel intelligent optimization algorithm called clouds search optimization algorithm,or CSO was proposed by blending these natural phenomena of clouds with the ideas of intelligent optimization algorithms.Droplets inside a cloud can produce difference quotient information to guide the search.Difference quotient information can approximate gradient,and its reverse direction can guide function value＇s decline.On the basis of difference quotient＇s those properties,clouds search optimization algorithm with difference quotient information（DCSO） was also proposed.It proved to be convergent by using the relationship between difference quotient and gradient,and the convergence property is similar to classical gradient-based algorithm.Finally,the numerical experiments on benchmark functions show the excellent perfor-mance of the two algorithms and the fast convergence speed of DCSO.
出处
《计算机科学》
CSCD
北大核心
2012年第1期252-255,267,共5页
Computer Science
关键词
云搜索优化算法
智能优化
函数优化
差商信息
梯度
Clouds search optimization algorithm
Intelligent optimization
Function optimization
Difference quotient information
Gradient
