基于MQHOA优化算法的尺度变化行为

doi:10.11896/j.issn.1002-137X.2019.08.044

Abstract

Abstract: Scale convergence is an important part of the computational process of intelligent optimization algorithm.The uncertainty principle and quantum tunneling effect prove this importance.In the optimization iterative process of the multi-scale quantum harmonic oscillator algorithm (MQHOA),by adjusting the scale convergence range,the algorithm’ssolution effect and computational performance can be affected.The scale variation was studied,and the optimal scale convergence parameter corresponding to the function in the 2-dimensional state was defined as the scale factor of the function.The scale factor can be used as a qualitative criterion for measuring the complexity of the function scale structure.The scale factor can help the algorithm to find the optimal solution by using the most suitable convergence scale for different functions

Key words: Multi-scale quantum harmonic oscillator algorithm, Optimization algorithm, Scale convergence

CLC Number:

TP301.6

ZHOU Yan, WANG Peng, XIN Gang, LI Bo. Scale Change Based on MQHOA Optimization Algorithm[J].Computer Science, 2019, 46(8): 266-271.

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Scale Change Based on MQHOA Optimization Algorithm

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