摘要
针对多峰优化问题,本文结合多尺度量子谐振子算法的全局优化特性提出了基于划分的多尺度量子谐振子算法.对定义域进行合理均匀划分,根据划分区域长度构建初始基态高斯曲线,随着标准差衰减高斯曲线逐渐收敛,从而在各个区域内快速搜索到极值点.对于实际函数的维度和极值数不同,本文提出固定分辨率策略和多级分辨率策略来解决实际问题,通过寻优精确性、全极值点寻优和全局多峰优化三个角度进行实验,对比蚁群算法、差分进化算法等主流群智能算法,可以表明该算法参数设置简单,具有很好的寻优准确性、快速收敛性和记忆性.
To solve the problem of multimodal optimization, a partition-based multi-scale quantum harmonic oscillator algorithm（MQHOA） is proposed depending on MQHOA s global optimization characteristic. It divides reasonably a domain into uniform areas, and then Gauss curves with ground state can be constructed according to the lengths of these uniform areas. With the attenuation of standard deviation, the Gauss curves will converge gradually, thus, extreme points can be found quickly. In addition, two strategies comprising fixed wavelength resolution and muti-level resolution are used for practical problems. Experiments are carried out from three aspects including optimization s accuracy, all extremal points optimization and global multimodal optimization. Compared with the ant colony algorithm, differential evolution algorithms and other mainstream swarm intelligence algorithms, the algorithm has, in addition to its simpleness on setting parameters, superior optimization accuracy, fast convergence and memory property.
出处
《自动化学报》
EI
CSCD
北大核心
2016年第2期235-245,共11页
Acta Automatica Sinica
基金
国家自然科学基金(60702075)
国家社会科学基金(12XSH019)资助~~
关键词
量子谐振子
多峰优化
全极值
群智能算法
Quantum harmonic oscillator
multimodal optimization
all the extremum
swarm intelligence algorithms
