ISSN 0253-2778

CN 34-1054/N

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Adaptive fractional order particle swarm optimization using swarm activity feedback and mutation operator

  • 1.

    Jiangsu Key Laboratory of Data Science and Smart Software, Jinling Institute of Technology, Nanjing 211169, China

  • 2.

    School of Computer Science, Jiangsu University of Science and Technology, Zhenjiang 212003, China

Cite this:
https://doi.org/10.3969/j.issn.0253-2778.2020.07.021
  • Received Date: 23 April 2020
  • Accepted Date: 28 July 2020
  • Rev Recd Date: 28 July 2020
  • Publish Date: 31 July 2020
    Abstract

  • Abstract

    The basic particle swarm optimizer with fractional-order (FOPSO) is easy to fall into premature convergence, because its overall performance depends on the fractional order α. To solve the problem, a new adaptive fractional-order PSO algorithm, SFOPSO is proposed, by cooperating mutation operators into swarm activity feedback with S-model. During the iteration of this new algorithm, the fractional-order α of particles is adjusted adaptively according to the swarm activity with S-model and the activity value of single particles. At the same time, to enhance the ability of the swarm to escape out of local optimum during the process of exploitation or exploration, the hybrid model was designed by using mutation operators. The convergence of the proposed algorithm SFOPSO is analyzed theoretically and the experimental results show that the proposed algorithm is practicable and effective in improving convergence accuracy and convergence speed.

    Abstract

    The basic particle swarm optimizer with fractional-order (FOPSO) is easy to fall into premature convergence, because its overall performance depends on the fractional order α. To solve the problem, a new adaptive fractional-order PSO algorithm, SFOPSO is proposed, by cooperating mutation operators into swarm activity feedback with S-model. During the iteration of this new algorithm, the fractional-order α of particles is adjusted adaptively according to the swarm activity with S-model and the activity value of single particles. At the same time, to enhance the ability of the swarm to escape out of local optimum during the process of exploitation or exploration, the hybrid model was designed by using mutation operators. The convergence of the proposed algorithm SFOPSO is analyzed theoretically and the experimental results show that the proposed algorithm is practicable and effective in improving convergence accuracy and convergence speed.
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    • References(20)
  • [1]
    JAIN N K, NANGIA U, JAIN J. A review of particle swarm optimization [J]. Journal of The Institution of Engineers (India): Series B, 2018, 99(4):407-411.
    [2]
    HARRISON K R, OMBUKI-BERMAN B M, ENGELLBECHT A P. A parameter-free particle swarm optimization algorithm using performance classifiers [J]. Information Sciences, 2019, 503(7):381-400.
    [3]
    SU S B, GUO H F, TIAN H M, et al. A novel pattern clustering algorithm based on particle swarm optimization joint adaptive wavelet neural network model [J]. Mobile Networks and Applications, 2017, 22(4): 692-701.
    [4]
    ISIET M, GADALA M. Self-adapting control parameters in particle swarm optimization[J]. Applied Soft Computing Journal, 2019, 83 (10):105653.
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    GHAMISI P, COUCEIRO M S, BENEDIKTSSON J A, et al. An efficient method for segmentation of images based on fractional calculus and natural selection[J]. Expert Systems with Applications, 2012, 39(16):12407-12417.
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    陈秋鑫.分数阶粒子群优化及其在聚类分析中的应用[D]. 镇江:江苏科技大学, 2018.
    CHEN Q X. Fractional-order particle swarm optimization and its applications in clustering analysis [D]. Zhenjiang:Jiangsu University of Science and Technology, 2018.
    [7]
    翟兆睿,苏守宝.一种动态压缩因子的分数阶粒子群优化[J]. 重庆理工大学学报（自然科学）, 2019, 33(7):94-101.
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    PIRES E J S, MACHADO J A T, OLIVEIRA P B M, et al. Particle swarm optimization with fractional-order velocity[J]. Nonlinear Dynamics, 2010, 61(1-2): 295-301.
    [12]
    COUCEIRO M S, GHAMISI P. Fractional Order Darwinian Particle Swarm Optimization[M]. Springer, 2016.
    [13]
    COUCEIRO M S, GHAMISI P. Fractional order darwinian particle swarm optimization[J]. Applied Sciences & Technology, 2015, 2011(11): 127-136.
    [14]
    COUCEIRO M S. Introducing the fractional-order darwinian PSO[J]. Signal Image & Video Processing, 2012, 6(3): 343-350.
    [15]
    ZHANG X, DUAN H. Comments on Particle swarm optimization with fractional-order velocity [J]. Nonlinear Dynamics, 2014, 77(1-2):427-429.
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    郭通, 兰巨龙, 李玉峰, 等. 自适应的分数阶达尔文粒子群优化算法[J]. 通信学报, 2014, 35(4):130-140.
    GUO T, LAN J L, LI Y F, et al. Adaptive fractional-order darwinian particle swarm optimization algorithm [J]. Journal of Communications, 2014, 35(4): 130-140.
    [17]
    YU S, SU S B, HUANG L. A simple diversity guided firefly algorithm[J]. Kybernetes, 2015, 44(1): 43-56.
    [18]
    SU S B, CHEN Q X, ZHAI Z R. Fractional-order particle swarm optimization with swarm activity feedback[J]. Basic & Clinical Pharmacology & Toxicology, 2019, 124(S2): 98-99.
    [19]
    苏守宝,曹喜滨,孔敏. 群活性与粒子群优化的稳定性分析[J]. 控制理论与应用, 2010, 27(10):1411-1417.
    SU S B, CAO X B, KONG M. Stability analysis of particle swarm optimization using warm activity [J]. Control Theory and Applications, 2010, 27(10): 1411-1417.
    [20]
    Huang L, DU W W, Ding L X. Particle swarm optimization algorithm based on adaptive Sigmoid inertia weight[J]. Application Research of Computers, 2012, 29(1): 32-34.
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    [1]
    JAIN N K, NANGIA U, JAIN J. A review of particle swarm optimization [J]. Journal of The Institution of Engineers (India): Series B, 2018, 99(4):407-411.
    [2]
    HARRISON K R, OMBUKI-BERMAN B M, ENGELLBECHT A P. A parameter-free particle swarm optimization algorithm using performance classifiers [J]. Information Sciences, 2019, 503(7):381-400.
    [3]
    SU S B, GUO H F, TIAN H M, et al. A novel pattern clustering algorithm based on particle swarm optimization joint adaptive wavelet neural network model [J]. Mobile Networks and Applications, 2017, 22(4): 692-701.
    [4]
    ISIET M, GADALA M. Self-adapting control parameters in particle swarm optimization[J]. Applied Soft Computing Journal, 2019, 83 (10):105653.
    [5]
    GHAMISI P, COUCEIRO M S, BENEDIKTSSON J A, et al. An efficient method for segmentation of images based on fractional calculus and natural selection[J]. Expert Systems with Applications, 2012, 39(16):12407-12417.
    [6]
    陈秋鑫.分数阶粒子群优化及其在聚类分析中的应用[D]. 镇江:江苏科技大学, 2018.
    CHEN Q X. Fractional-order particle swarm optimization and its applications in clustering analysis [D]. Zhenjiang:Jiangsu University of Science and Technology, 2018.
    [7]
    翟兆睿,苏守宝.一种动态压缩因子的分数阶粒子群优化[J]. 重庆理工大学学报（自然科学）, 2019, 33(7):94-101.
    ZHAI Z R, SU S B. A fractional-order particle swarm optimization with dynamic constriction factor [J]. Journal of Chongqing University of Technology (Natural Science), 2019, 33(7): 94-101.
    [8]
    BENSON D A, MEERSCHAERT M M, REVIELLE J. Fractional calculus in hydrologic modeling: A numerical perspective[J]. Advances in Water Resources, 2013, 51(1): 479-497.
    [9]
    ZHANG Y, SUN H G, STOWELL H H, et al. A review of applications of fractional calculus in Earth system dynamics[J]. Chaos Solitons & Fractals, 2017, 102: 29-46..
    [10]
    PIRES E J S, MACHADO J A T, OLIVEIRA P B M, et al. Fractional order dynamics in a particle swarm optimization algorithm[C]// International Conference on Intelligent Systems Design and Applications. IEEE, 2007: 703-710.
    [11]
    PIRES E J S, MACHADO J A T, OLIVEIRA P B M, et al. Particle swarm optimization with fractional-order velocity[J]. Nonlinear Dynamics, 2010, 61(1-2): 295-301.
    [12]
    COUCEIRO M S, GHAMISI P. Fractional Order Darwinian Particle Swarm Optimization[M]. Springer, 2016.
    [13]
    COUCEIRO M S, GHAMISI P. Fractional order darwinian particle swarm optimization[J]. Applied Sciences & Technology, 2015, 2011(11): 127-136.
    [14]
    COUCEIRO M S. Introducing the fractional-order darwinian PSO[J]. Signal Image & Video Processing, 2012, 6(3): 343-350.
    [15]
    ZHANG X, DUAN H. Comments on Particle swarm optimization with fractional-order velocity [J]. Nonlinear Dynamics, 2014, 77(1-2):427-429.
    [16]
    郭通, 兰巨龙, 李玉峰, 等. 自适应的分数阶达尔文粒子群优化算法[J]. 通信学报, 2014, 35(4):130-140.
    GUO T, LAN J L, LI Y F, et al. Adaptive fractional-order darwinian particle swarm optimization algorithm [J]. Journal of Communications, 2014, 35(4): 130-140.
    [17]
    YU S, SU S B, HUANG L. A simple diversity guided firefly algorithm[J]. Kybernetes, 2015, 44(1): 43-56.
    [18]
    SU S B, CHEN Q X, ZHAI Z R. Fractional-order particle swarm optimization with swarm activity feedback[J]. Basic & Clinical Pharmacology & Toxicology, 2019, 124(S2): 98-99.
    [19]
    苏守宝,曹喜滨,孔敏. 群活性与粒子群优化的稳定性分析[J]. 控制理论与应用, 2010, 27(10):1411-1417.
    SU S B, CAO X B, KONG M. Stability analysis of particle swarm optimization using warm activity [J]. Control Theory and Applications, 2010, 27(10): 1411-1417.
    [20]
    Huang L, DU W W, Ding L X. Particle swarm optimization algorithm based on adaptive Sigmoid inertia weight[J]. Application Research of Computers, 2012, 29(1): 32-34.
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