IDEAS home Printed from https://ideas.repec.org/a/eee/apmaco/v269y2015icp904-929.html
   My bibliography  Save this article

Particle swarm optimization with stochastic selection of perturbation-based chaotic updating system

Author

Listed:
  • Tatsumi, Keiji
  • Ibuki, Takeru
  • Tanino, Tetsuzo

Abstract

In this paper, we consider the particle swarm optimization (PSO). In particular, we focus on an improved PSO called the CPSO-VQO, which uses a perturbation-based chaotic system and a threshold-based method of selecting from the standard and chaotic updating systems for each particle on the basis of the difference vector between its pbest and the gbest. Although it was reported that the CPSO-VQO performs well, it is not easy to select an amplitude of the perturbation and a threshold appropriately for an effective search. This is because the bifurcation structure of the chaotic system depends on the difference vector, and the difference vector varies widely between different stages of the search and between different problems.

Suggested Citation

  • Tatsumi, Keiji & Ibuki, Takeru & Tanino, Tetsuzo, 2015. "Particle swarm optimization with stochastic selection of perturbation-based chaotic updating system," Applied Mathematics and Computation, Elsevier, vol. 269(C), pages 904-929.
    • Handle: RePEc:eee:apmaco:v:269:y:2015:i:c:p:904-929
    DOI: 10.1016/j.amc.2015.07.098
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0096300315010206
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.amc.2015.07.098?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Alatas, Bilal & Akin, Erhan & Ozer, A. Bedri, 2009. "Chaos embedded particle swarm optimization algorithms," Chaos, Solitons & Fractals, Elsevier, vol. 40(4), pages 1715-1734.
    2. Tatsumi, Keiji & Obita, Yoshinori & Tanino, Tetsuzo, 2009. "Chaos generator exploiting a gradient model with sinusoidal perturbations for global optimization," Chaos, Solitons & Fractals, Elsevier, vol. 42(3), pages 1705-1723.
    3. Liu, Bo & Wang, Ling & Jin, Yi-Hui & Tang, Fang & Huang, De-Xian, 2005. "Improved particle swarm optimization combined with chaos," Chaos, Solitons & Fractals, Elsevier, vol. 25(5), pages 1261-1271.
    4. Chuanwen, Jiang & Bompard, Etorre, 2005. "A hybrid method of chaotic particle swarm optimization and linear interior for reactive power optimisation," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 68(1), pages 57-65.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Tatsumi, Keiji & Obita, Yoshinori & Tanino, Tetsuzo, 2009. "Chaos generator exploiting a gradient model with sinusoidal perturbations for global optimization," Chaos, Solitons & Fractals, Elsevier, vol. 42(3), pages 1705-1723.
    2. Acharjee, P. & Mallick, S. & Thakur, S.S. & Ghoshal, S.P., 2011. "Detection of maximum loadability limits and weak buses using Chaotic PSO considering security constraints," Chaos, Solitons & Fractals, Elsevier, vol. 44(8), pages 600-612.
    3. Yu, Haiquan & Zhou, Jianxin & Si, Fengqi & Nord, Lars O., 2022. "Combined heat and power dynamic economic dispatch considering field operational characteristics of natural gas combined cycle plants," Energy, Elsevier, vol. 244(PA).
    4. El-Shorbagy, M.A. & Mousa, A.A. & Nasr, S.M., 2016. "A chaos-based evolutionary algorithm for general nonlinear programming problems," Chaos, Solitons & Fractals, Elsevier, vol. 85(C), pages 8-21.
    5. Li, Chaoshun & Zhou, Jianzhong & Xiao, Jian & Xiao, Han, 2012. "Parameters identification of chaotic system by chaotic gravitational search algorithm," Chaos, Solitons & Fractals, Elsevier, vol. 45(4), pages 539-547.
    6. Bingol, Harun & Alatas, Bilal, 2020. "Chaos based optics inspired optimization algorithms as global solution search approach," Chaos, Solitons & Fractals, Elsevier, vol. 141(C).
    7. Changchun Cai & Bing Jiang & Lihua Deng, 2015. "General Dynamic Equivalent Modeling of Microgrid Based on Physical Background," Energies, MDPI, vol. 8(11), pages 1-20, November.
    8. Ahmadi, Mohamadreza & Mojallali, Hamed, 2012. "Chaotic invasive weed optimization algorithm with application to parameter estimation of chaotic systems," Chaos, Solitons & Fractals, Elsevier, vol. 45(9), pages 1108-1120.
    9. Ivona Brajević & Jelena Ignjatović, 2019. "An upgraded firefly algorithm with feasibility-based rules for constrained engineering optimization problems," Journal of Intelligent Manufacturing, Springer, vol. 30(6), pages 2545-2574, August.
    10. Wang, Jianzhou & Qin, Shanshan & Jin, Shiqiang & Wu, Jie, 2015. "Estimation methods review and analysis of offshore extreme wind speeds and wind energy resources," Renewable and Sustainable Energy Reviews, Elsevier, vol. 42(C), pages 26-42.
    11. Xuanhu He & Wei Wang & Jiuchun Jiang & Lijie Xu, 2015. "An Improved Artificial Bee Colony Algorithm and Its Application to Multi-Objective Optimal Power Flow," Energies, MDPI, vol. 8(4), pages 1-26, March.
    12. Saghaei, Mahsa & Ghaderi, Hadi & Soleimani, Hamed, 2020. "Design and optimization of biomass electricity supply chain with uncertainty in material quality, availability and market demand," Energy, Elsevier, vol. 197(C).
    13. Hossein Lotfi, 2022. "A Multiobjective Evolutionary Approach for Solving the Multi-Area Dynamic Economic Emission Dispatch Problem Considering Reliability Concerns," Sustainability, MDPI, vol. 15(1), pages 1-23, December.
    14. Ma, Yuze & Han, Xu & Zhang, Ting & Li, Ang & Song, Zhicheng & Li, Tao & Wang, Yongli, 2024. "Research on station-network planning of electricity-thermal -cooling regional integrated energy system considering multiple-load clusters and network costs," Energy, Elsevier, vol. 297(C).
    15. Sedighizadeh, Davoud & Masehian, Ellips & Sedighizadeh, Mostafa & Akbaripour, Hossein, 2021. "GEPSO: A new generalized particle swarm optimization algorithm," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 179(C), pages 194-212.
    16. Naanaa, Anis, 2015. "Fast chaotic optimization algorithm based on spatiotemporal maps for global optimization," Applied Mathematics and Computation, Elsevier, vol. 269(C), pages 402-411.
    17. Zhang, Wen Yu & Hong, Wei-Chiang & Dong, Yucheng & Tsai, Gary & Sung, Jing-Tian & Fan, Guo-feng, 2012. "Application of SVR with chaotic GASA algorithm in cyclic electric load forecasting," Energy, Elsevier, vol. 45(1), pages 850-858.
    18. He, Qie & Wang, Ling & Liu, Bo, 2007. "Parameter estimation for chaotic systems by particle swarm optimization," Chaos, Solitons & Fractals, Elsevier, vol. 34(2), pages 654-661.
    19. Adel Taieb & Moêz Soltani & Abdelkader Chaari, 2017. "Parameter Optimization of MIMO Fuzzy Optimal Model Predictive Control By APSO," Complexity, Hindawi, vol. 2017, pages 1-11, October.
    20. Gehad Ismail Sayed & Ashraf Darwish & Aboul Ella Hassanien, 2018. "A New Chaotic Whale Optimization Algorithm for Features Selection," Journal of Classification, Springer;The Classification Society, vol. 35(2), pages 300-344, July.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:apmaco:v:269:y:2015:i:c:p:904-929. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/applied-mathematics-and-computation .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.