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

doi:10.12068/j.issn.1005-3026.2022.02.002

Abstract

Abstract: 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.

Key words: optimization algorithm; quantum harmonic oscillator; multi-scale; data driven; historical information

CLC Number:

TP183

JIN Jin， WANG Peng. Historical Data-Driven Multi-scale Quantum Harmonic Oscillator Optimization Algorithm[J]. Journal of Northeastern University(Natural Science), 2022, 43(2): 160-167.

References

[1]Dorigo M，Birattari M，Stutzle T.Ant colony optimization［J］.IEEE Computational Intelligence Magazine，2006，1(4):28-39.
[2]瞿博阳，梁静，Suganthan P N.双局部粒子群算法解决环境经济调度问题［J］.计算机工程与应用，2014，50(11):1-6.(Qu Bo-yang，Liang Jing，Suganthan P N.Two local best based multi-objective particle swarm optimization algorithm to solve environmental/ economic dispatch problem［J］.Computer Engineering and Application，2014，50(11):1-6.)
[3]王柳静，张贵军，周晓根.基于状态估计反馈的策略自适应差分进化算法［J］.自动化学报，2020，46(4):752-766.(Wang Liu-jing，Zhang Gui-jun，Zhou Xiao-gen.Strategy self-adaptive differential evolution algorithm based on state estimation feedback［J］.Acta Automatica Sinica，2020，46(4):752-766.)
[4]Karaboga D，Basturk B A.Powerful and efficient algorithm for numerical function optimization:artificial bee colony(ABC) algorithm［J］.Journal of Global Optimization，2007，39(3):459-471.
[5]谭营，郑少秋.烟花算法研究进展［J］.智能系统学报，2014，9(5):515-528.(Tan Ying，Zheng Shao-qiu.Recent advances in fireworks algorithm［J］.CAAI Transactions on Intelligent Systems，2014，9(5):515-528.)
[6]Shi Y H.Brain storm optimization algorithm［C］//International Conference on Swarm Intelligence.Chongqing，2011:303-309.
[7]Wang G G，Deb S，Coelho L.Elephant herding optimization［C］//2015 3rd International Symposium on Computational and Business Intelligence(ISCBI).Bali，Indonesia，2015:1-5.
[8]刘三阳，靳安钊.求解约束优化问题的协同进化教与学优化算法［J］.自动化学报，2018，44(9):1690-1697.(Liu San-yang，Jin An-zhao.A co-evolutionary teaching learning-based optimization algorithm for constrained optimization problems［J］.Acta Automatica Sinica，2018，44(9):1690-1697.)
[9]张聪明，刘立群，马立群.一种新的群智能算法:狮群算法［J］.计算机科学，2018，45(6):114-116.(Zhang Cong-ming，Liu Li-qun，Ma Li-qun.New swarm intelligent algorithms:lions algorithm［J］.Computer Science，2018，45(6):114-116.)
[10]Hansen N，Müller S D，Koumoutsakos P.Reducing the time complexity of the derandomized evolution strategy with covariance matrix adaptation(CMA-ES)［J］.Evolutionary Computation，2003，11(1):1-18.
[11]Wang P，Ye X G，Li B，et al.Multi-scale quantum harmonic oscillator algorithm for global numerical optimization［J］.Applied Soft Computing，2018，69(1):655-670.
[12]Ye X G，Wang P.Impact of migration strategies and individual stabilization on multi-scale quantum harmonic oscillator algorithm for global numerical optimization problems［J］. Applied Soft Computing，2019，85:105800.
[13]Xin G，Wang P.Exploring superposition state in multi-scale quantum harmonic oscillator algorithm［J］.Applied Soft Computing，2021，107:107398.
[14]Wang P，Li B，Jin J，et al.Multi-scale quantum harmonic oscillator algorithm with individual stabilization strategy［C］// International Conference on Swarm Intelligence.Shanghai，2018:624-633.
[15]Griffiths D J，Schroeter D F.Introduction to quantum mechanics［M］.3rd ed.Cambridge:Cambridge University Press，2018.
[16]Hansen N，Ostermeier A.Completely derandomized self-adaptation in evolution strategies［J］.Evolutionary Computation，2001，9(2):159-195.
[17]Hauschild M，Pelikan M.An introduction and survey of estimation of distribution algorithms［J］.Swarm and Evolutionary Computation，2011，1(3):111-128.
[18]Jamil M，Yang X S.A literature survey of benchmark functions for global optimization problems［J］.International Journal of Mathematical Modeling and Numerical Optimization，2013，4(2):150-194.
[19]Sun J，Xu W B，Feng B.A global search strategy of quantum-behaved particle swarm optimization［C］//IEEE Conference on Cybernetics and Intelligent Systems.Singapore，2004:8503878.
[20]Zambrano-Bigiarini M，Clerc M，Rojas R.Standard particle swarm optimization 2011 at CEC-2013:a baseline for future PSO improvements［C］// IEEE Congress on Evolutionary Computation.Cancún，Mexico，2013:6557848.
[21]Storn R，Price K.Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces［J］.Journal of Global Optimization，1997，11(4):341-359.
[22]Li J Z，Tan Y.The bare bones fireworks algorithm:a minimalist global optimizer［J］.Applied Soft Computing，2018，62(1):454-462.
[23]Wolpert D H，Macready W G.No free lunch theorems for optimization［J］. IEEE Transactions on Evolutionary Computation，1997，1(1):67-82.