一种协方差矩阵的多尺度量子谐振子算法

A covariance-matrix multi-scale quantum harmonic oscillator algorithm

针对多尺度量子谐振子算法在处理高维全局优化问题时难以收敛的问题,提出一种协方差矩阵的多尺度量子谐振子优化算法,并给出新算法的核心数学模型.所提算法改进了多元正态分布评估算法中的协方差矩阵生成方式,保留了之前采样点的记忆,加入动态迭代步长加快了新协方差矩阵的更新速度.实验结果表明,所提算法的性能远超原算法,与4种经典优化算法相比,在收敛精度、收敛速度和鲁棒性上也具有优势.

For global optimization problems with high dimension, the multi-scale quantum harmonic oscillator algorithm is hard to converge. For this problem, a covariance-matrix multi-scale quantum harmonic oscillator algorithm is proposed,and the mathematical model of core part is given, which improves the method of generating covariance matrix from the estimation of multivariate normal algorithm and reserves the memory of old sampling points. Moreover, dynamic iteration steps are intraduced to accelerate updating of the new covariance matrix. The experimental results show that the performance of the proposed algorithm is far better than that of the original algorithm, and it＇s obviously superior to four classic optimization algorithms on convergence precision, convergence rate and robustness.