IDEAS home Printed from https://ideas.repec.org/a/adm/journl/v8y2019i5p135-138.html
   My bibliography  Save this article

Nuclear Energy Spectrum Decomposition Based on Hybrid Particle Swarm Optimization

Author

Listed:
  • Xing-Ke Ma
  • Yang-Zhen Ji

Abstract

A nonlinear fitting model is proposed for the problem of nuclear energy spectrum decomposition. And the hybrid particle swarm optimization algorithm based on natural selection idea and random inertia weight is used to solve. First, a nonlinear fitting model was introduced. Secondly, the defects of the traditional particle swarm optimization algorithm based on linear inertia weight are analyzed, and the ideas of stochastic inertia weight and natural selection are integrated into the algorithm for these shortcomings. Then, according to the specific problems involved in this paper and the existing data, the continuous function model is transformed into a discrete series model. According to the nature that the absolute value is not less than zero, the fitness value is appropriately modified to achieve the purpose of improving the calculation accuracy and the operation speed of the algorithm.

Suggested Citation

  • Xing-Ke Ma & Yang-Zhen Ji, 2019. "Nuclear Energy Spectrum Decomposition Based on Hybrid Particle Swarm Optimization," International Journal of Sciences, Office ijSciences, vol. 8(05), pages 135-138, May.
    • Handle: RePEc:adm:journl:v:8:y:2019:i:5:p:135-138
    DOI: 10.18483/ijSci.2075
    as

    Download full text from publisher

    File URL: https://www.ijsciences.com/pub/article/2075
    Download Restriction: no
    File URL: https://www.ijsciences.com/pub/pdf/V82019052075.pdf
    Download Restriction: no
    File URL: https://libkey.io/10.18483/ijSci.2075?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
    ---><---

    References listed on IDEAS

    as
    1. 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.
    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. Dong, Yingchao & Zhang, Shaohua & Zhang, Hongli & Zhou, Xiaojun & Jiang, Jiading, 2025. "Chaotic evolution optimization: A novel metaheuristic algorithm inspired by chaotic dynamics," Chaos, Solitons & Fractals, Elsevier, vol. 192(C).
    2. 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.
    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. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. Yang, Dixiong & Li, Gang & Cheng, Gengdong, 2007. "On the efficiency of chaos optimization algorithms for global optimization," Chaos, Solitons & Fractals, Elsevier, vol. 34(4), pages 1366-1375.
    12. dos Santos Coelho, Leandro & Coelho, Antonio Augusto Rodrigues, 2009. "Model-free adaptive control optimization using a chaotic particle swarm approach," Chaos, Solitons & Fractals, Elsevier, vol. 41(4), pages 2001-2009.
    13. Mohamad Javad Alizadeh & Davoud Ahmadyar & Ali Afghantoloee, 2017. "Improvement on the Existing Equations for Predicting Longitudinal Dispersion Coefficient," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(6), pages 1777-1794, April.
    14. Hong, Wei-Chiang, 2010. "Application of chaotic ant swarm optimization in electric load forecasting," Energy Policy, Elsevier, vol. 38(10), pages 5830-5839, October.
    15. Othman Abdullah Almatroud & Ali A. Shukur & Viet-Thanh Pham & Giuseppe Grassi, 2024. "Oscillator with Line of Equilibiria and Nonlinear Function Terms: Stability Analysis, Chaos, and Application for Secure Communications," Mathematics, MDPI, vol. 12(12), pages 1-18, June.
    16. He, Yao-Yao & Zhou, Jian-Zhong & Xiang, Xiu-Qiao & Chen, Heng & Qin, Hui, 2009. "Comparison of different chaotic maps in particle swarm optimization algorithm for long-term cascaded hydroelectric system scheduling," Chaos, Solitons & Fractals, Elsevier, vol. 42(5), pages 3169-3176.
    17. 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.
    18. Liu, Bo & Wang, Ling & Jin, Yi-Hui & Tang, Fang & Huang, De-Xian, 2006. "Directing orbits of chaotic systems by particle swarm optimization," Chaos, Solitons & Fractals, Elsevier, vol. 29(2), pages 454-461.
    19. Jinn-Tong Chiu & Ching-Hai Lin, 2016. "A Modified Particle Swarm Optimization Based on Eagle Foraging Behavior," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 15(03), pages 703-727, May.
    20. Pan, Hui & Wang, Ling & Liu, Bo, 2008. "Chaotic annealing with hypothesis test for function optimization in noisy environments," Chaos, Solitons & Fractals, Elsevier, vol. 35(5), pages 888-894.

    More about this item

    Keywords

    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:adm:journl:v:8:y:2019:i:5:p:135-138. 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: Staff ijSciences The email address of this maintainer does not seem to be valid anymore. Please ask Staff ijSciences to update the entry or send us the correct address (email available below). General contact details of provider: .

    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.