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The multi-scale quantum harmonic oscillator optimization algorithm(MQHOA)is a natural calculation algorithm based on quantum physics proposed in recent years. Aiming at the problem that the algorithm fails to make full use of the historical information in the iteration, this paper proposes a historical information-driven multi-scale quantum harmonic oscillator optimization algorithm(HI-MQHOA). In the two-step iterative process, HI-MQHOA introduces historical data as a driver to form the parameters of the next generation individual distribution and dynamically adjust the scale of the algorithm. The next generation individual distribution parameters can effectively guide the development and exploration of the algorithm, and dynamic scaling can avoid premature stagnation. Verified by several classical test functions, the algorithm is superior to MQHOA, improved MQHOA and other natural computing algorithms in solution quality, accuracy and scalability. [ABSTRACT FROM AUTHOR]