自动化学报Issue(2)：235-245,11.DOI:10.16383/j.aas.2016.c150429
基于划分的多尺度量子谐振子算法多峰优化
Partition-based MQHOA for Multimodal Optimization
摘要
Abstract
To solve the problem of multimodal optimization, a partition-based multi-scale quantum harmonic oscillator algorithm (MQHOA) is proposed depending on MQHOA0s 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 optimization0s 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.关键词
量子谐振子/多峰优化/全极值/群智能算法Key words
Quantum harmonic oscillator/multimodal optimization/all the extremum/swarm intelligence algorithms引用本文复制引用
陆志君,安俊秀,王鹏..基于划分的多尺度量子谐振子算法多峰优化[J].自动化学报,2016,(2):235-245,11.基金项目
国家自然科学基金(60702075),国家社会科学基金(12XSH019)资助@@@@Supported by National Natural Science Foundation of China (60702075), National Social Science Foundation of China (12XSH019) （60702075）
