YAN Tao and LIU Fengxian, “Quantum-Behaved Particle Swarm Optimization Algorithm Based on the Two-Body Problem,” Chinese Journal of Electronics, vol. 28, no. 3, pp. 569-576, 2019, doi: 10.1049/cje.2019.03.023
Citation: YAN Tao and LIU Fengxian, “Quantum-Behaved Particle Swarm Optimization Algorithm Based on the Two-Body Problem,” Chinese Journal of Electronics, vol. 28, no. 3, pp. 569-576, 2019, doi: 10.1049/cje.2019.03.023

Quantum-Behaved Particle Swarm Optimization Algorithm Based on the Two-Body Problem

doi: 10.1049/cje.2019.03.023
Funds:  This work was supported by National Natural Science Fund of China (No.61672332, No.61432011 and No.U1435212), Program for New Century Excellent Talents in University (No.NCET-12-1031), Program for the Outstanding Innovative Teams of Higher Learning Institutions of Shanxi, and Program for the Young San Jin Scholars of Shanxi.
  • Received Date: 2017-04-26
  • Publish Date: 2019-05-10
  • The present study proposes an improved Quantum-behaved particle swarm optimization algorithm based on the two-body problem model (QTPSO) for solving the problem that other quantum-behaved particle swarm optimization algorithms easily converge on local optimal solutions when solving complex nonlinear problems. In the proposed QTPSO algorithm, particles are categorised as core particles and edge particles. Once the position of the core particle is determined, the edge particle appears in the vicinity of the attractor exhibiting a high probability, and the attractor is obtained through the random weighted sum of the core particle and the optimal mean position. Through simulation of the motion of these two particles by applying the interaction of the particles in the two-body problem, this mechanism not only improves the diversity of the population, but also enhances the local search capacity. To validate the proposed algorithm, three groups of experimental results were obtained to compare the proposed algorithm with other swarm intelligence algorithms. The experimental results indicate the superiority of the QTPSO algorithm.
  • loading
  • J. Kennedy and R.C. Eberhart,"Particle swarm optimization", Proc. of IEEE International Conference on Neural Networks, Piscataway, New Jersey, USA, pp.1942-1948, 1995.
    D. Karaboga and B. Akay,"A comparative study of artificial bee colony algorithm", Neurocomputing, Vol. 214, No.1, pp.108-132, 2009.
    X. Meng, L. Yu, X.Z. Gao, et al.,"A new Bio-inspired algorithm:Chicken swarm optimization", Proc. of International Conference in Swarm Intelligence, Hefei, China, pp.86-94, 2014.
    D.E. Kong, H. Peng, J.J. M,"Adaptive stochastic resonance method based on artificial-fish swarm optimization", Acta Electronica Sinica, Vol.45, No.8, pp.1864-1872, 2017. (in Chinese)
    X.K. Yang, M.L. Zhong, X.J. Jing, et al.,"NIR chemical-Image analysis based on adaptive local optimization PSO", Chinese Journal of Electronics, Vol.23, No.1, pp.115-118, 2014.
    J. Wang, P. Hong, T.U. Min, et al.,"A fault diagnosis method of power systems based on an improved adaptive fuzzy spiking neural P systems and PSO algorithms", Chinese Journal of Electronics, Vol.25, No.2, pp.320-327, 2016.
    S. Taghiyeh and J. Xu,"A new particle swarm optimization algorithm for noisy optimization problem", Swarm Intelligence, Vol.10, No.3, pp.161-192, 2016.
    F. van den Bergh," An analysis of particle swarm optimizers", Ph.D.Thesis, University of Pretoria, USA, 2002.
    M.R. Tanweer, S. Suresh and N. Sundararajan,"Selfregulating particle swarm optimization algorithm", Information Sciences, Vol.294, No.10, pp.182-202, 2015.
    T. Yan, F.X. Liu and B. Chen,"New particle swarm optimisation Algorithm with Hénon chaotic map structure", Chinese Journal of Electronics, Vol.26, No.4, pp.747-753, 2017.
    Y. Shi, H.C. Liu, L. Gao, et al.,"Cellular particle swarm optimization", Information Sciences, Vol.181, No.20, pp.4460-4493, 2011.
    J. Sun, B. Feng and W. Xu,"Particle swarm optimization with particles having quantum behavior", Proc. of the 2004 Congress on Evolutionary Computation, Portland, USA, pp.1571-1580, 2004.
    L.D.S. Coelho,"A quantum particle swarm optimizer with chaotic mutation operator", Chaos Solitons and Fractals, Vol.37, No.5, pp.1409-1418, 2008.
    D. Tang, Y. Zhao and Y. Xue,"A quantum-behaved particle swarm optimization with memetic algorithm and memory for continuous non-linear large scale problems", Information Sciences, Vol.289, No.24, pp.162-189, 2014.
    L. Lin, F. Guo, X. Xie, et al.,"Novel adaptive hybrid rule network based on TS fuzzy rules using an improved quantumbehaved particle swarm optimization", Neurocomputing, Vol.149, pp.1003-1013, 2015.
    O.E. Turgut,"Hybrid chaotic quantum behaved particle swarm optimization algorithm for thermal design of plate fin heat exchangers", Applied Mathematical Modelling, Vol.40, No.1, pp.50-69, 2015.
    B. Haddar, M. Khemakhem, S. Hanafi, et al.,"A hybrid quantum particle swarm optimization for the multidimensional knapsack problem", Engineering Applications of Artificial Intelligence, Vol.55, pp.1-13, 2016.
    C. Jin and S.W. Jin,"Automatic image annotation using feature selection based on improving quantum particle swarm optimization", Signal Processing, Vol.109, No.2, pp.172-181, 2015.
    J.Y. Zen,"Quantum mechanics", Science Press, Beijing, China, pp.27-108, 2007.
    P.N. Suganthan and N. Hansen et al.,"Problem definitions and evaluation criteria for the CEC 2005 special session on real-parameter optimization", Technical Report:Nanyang Technological University AND KanGAL Report, pp.1-50, 2005.
    P.J. Angeline,"Evolutionary optimization versus particle swarm optimization:Philosophy and performance differences", Proc. of International Conference on Evolutionary Programming Vii, Heidelberg, Berlin, pp.601-610, 1998.
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Article Metrics

    Article views (406) PDF downloads(178) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return