With the multi-scale binary sampling methods on the basis of Gaussian scaling function ,the model of multi-scale quantum harmonic oscillator algorithm for high-dimensional function global optimization problems is proposed .High-dimensional function optimization process is divided into two steps ,scale convergence and quantum harmonic oscillator convergence .This algo-rithm model is based on the same convergence process probability interpretations between function optimization problem and quan-tum harmonic oscillator from high-energy state to ground state .This algorithm ,which has explicit physical model ,can realize high-dimensional function optimization without coding or complex initial conditions .Experiments and analysis are done for 15 typical two-dimensional optimized test functions and 6 typical high-dimensional optimized test functions .The results show that multi-scale quantum harmonic oscillator algorithm gets precise global optimum for high-dimensional function quickly ,and with “reduced fre-quency”approach ,the search speed of function with“high frequency”component improves significantly .%函数优化问题与量子谐振子从高能态向基态收敛过程具有相似的概率解释，结合基于高斯尺度函数的多尺度二进信息采样方法，提出了高维函数优化问题的多尺度量子谐振子算法模型，该算法模型将高维函数优化过程分为尺度收敛和量子谐振子收敛两个步骤，物理模型明确，无需编码和复杂的初始条件设定，即可实现高维函数优化。通过对15种典型二维优化测试函数和6种典型的高维优化测试函数进行实验和分析表明，多尺度量子谐振子算法可以快速精确地获得高维函数的全局最优解，同时采用“降频”方法可以提高对具有“高频”成分函数的搜索速度。
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