历史数据驱动的多尺度量子谐振子优化算法

Historical Data-Driven Multi-scale Quantum Harmonic Oscillator Optimization Algorithm

多尺度量子谐振子优化算法(MQHOA)是近年提出的一种基于量子物理的自然计算方法.本文针对该算法未能充分利用迭代中历史信息的问题,提出一种历史数据驱动的多尺度量子谐振子优化算法(HI-MQHOA).在两步迭代过程中,HI-MQHOA引入历史数据作为驱动,形成下一代个体分布的参数及动态调整算法尺度.形成的下一代个体分布参数可以有效指导算法的开发和探索,动态尺度调整可以避免早熟停滞.通过多个经典测试函数验证,该算法在解的质量、准确率和伸缩性方面优于MQHOA和改进的MQHOA,以及其他自然计算算法.

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.