IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v10y2022i3p495-d741793.html
   My bibliography  Save this article

Adaptive Guided Spatial Compressive Cuckoo Search for Optimization Problems

Author

Listed:
  • Wangying Xu

    (School of Management Science and Engineering, Nanjing University of Information Science & Technology, Nanjing 211544, China)

  • Xiaobing Yu

    (School of Management Science and Engineering, Nanjing University of Information Science & Technology, Nanjing 211544, China
    Ministry of Education & Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD), Nanjing University of Information Science & Technology, Nanjing 211544, China)

Abstract

Cuckoo Search (CS) is one of the heuristic algorithms that has gradually drawn public attention because of its simple parameters and easily understood principle. However, it still has some disadvantages, such as its insufficient accuracy and slow convergence speed. In this paper, an Adaptive Guided Spatial Compressive CS (AGSCCS) has been proposed to handle the weaknesses of CS. Firstly, we adopt a chaotic mapping method to generate the initial population in order to make it more uniform. Secondly, a scheme for updating the personalized adaptive guided local location areas has been proposed to enhance the local search exploitation and convergence speed. It uses the parent’s optimal and worst group solutions to guide the next iteration. Finally, a novel spatial compression (SC) method is applied to the algorithm to accelerate the speed of iteration. It compresses the convergence space at an appropriate time, which is aimed at improving the shrinkage speed of the algorithm. AGSCCS has been examined on a suite from CEC2014 and compared with the traditional CS, as well as its four latest variants. Then the parameter identification and optimization of the photovoltaic (PV) model are applied to examine the capacity of AGSCCS. This is conducted to verify the effectiveness of AGSCCS for industrial problem application.

Suggested Citation

  • Wangying Xu & Xiaobing Yu, 2022. "Adaptive Guided Spatial Compressive Cuckoo Search for Optimization Problems," Mathematics, MDPI, vol. 10(3), pages 1-28, February.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:3:p:495-:d:741793
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/10/3/495/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/10/3/495/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Bérubé, Jean-François & Gendreau, Michel & Potvin, Jean-Yves, 2009. "An exact [epsilon]-constraint method for bi-objective combinatorial optimization problems: Application to the Traveling Salesman Problem with Profits," European Journal of Operational Research, Elsevier, vol. 194(1), pages 39-50, April.
    2. Kim, Jong Suk & Edgar, Thomas F., 2014. "Optimal scheduling of combined heat and power plants using mixed-integer nonlinear programming," Energy, Elsevier, vol. 77(C), pages 675-690.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Xiang Li & Shuo Zhang & Wei Zhang, 2023. "Applied Computing and Artificial Intelligence," Mathematics, MDPI, vol. 11(10), pages 1-4, May.

    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. Tang, Lianhua & Li, Yantong & Bai, Danyu & Liu, Tao & Coelho, Leandro C., 2022. "Bi-objective optimization for a multi-period COVID-19 vaccination planning problem," Omega, Elsevier, vol. 110(C).
    2. Bai, Yang & Zhong, Haiwang & Xia, Qing & Kang, Chongqing & Xie, Le, 2015. "A decomposition method for network-constrained unit commitment with AC power flow constraints," Energy, Elsevier, vol. 88(C), pages 595-603.
    3. Najafi, Arsalan & Falaghi, Hamid & Contreras, Javier & Ramezani, Maryam, 2016. "Medium-term energy hub management subject to electricity price and wind uncertainty," Applied Energy, Elsevier, vol. 168(C), pages 418-433.
    4. Xuecheng Tian & Yanxia Guan & Shuaian Wang, 2023. "Data Transformation in the Predict-Then-Optimize Framework: Enhancing Decision Making under Uncertainty," Mathematics, MDPI, vol. 11(17), pages 1-12, September.
    5. Xuemin Liu & Guozhong Huang & Shengnan Ou & Xingyu Xiao & Xuehong Gao & Zhangzhou Meng & Youqiang Pan & Ibrahim M. Hezam, 2023. "Biobjective Optimization Model Considering Risk and Profit for the Multienterprise Layout Design in Village-Level Industrial Parks in China," Sustainability, MDPI, vol. 15(4), pages 1-27, February.
    6. Ahn, Jaemyung & de Weck, Olivier & Geng, Yue & Klabjan, Diego, 2012. "Column generation based heuristics for a generalized location routing problem with profits arising in space exploration," European Journal of Operational Research, Elsevier, vol. 223(1), pages 47-59.
    7. Christian Artigues & Nicolas Jozefowiez & Boadu M. Sarpong, 2018. "Column generation algorithms for bi-objective combinatorial optimization problems with a min–max objective," EURO Journal on Computational Optimization, Springer;EURO - The Association of European Operational Research Societies, vol. 6(2), pages 117-142, June.
    8. Burdett, Robert & Kozan, Erhan, 2016. "A multi-criteria approach for hospital capacity analysis," European Journal of Operational Research, Elsevier, vol. 255(2), pages 505-521.
    9. Peter Reiter & Walter Gutjahr, 2012. "Exact hybrid algorithms for solving a bi-objective vehicle routing problem," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 20(1), pages 19-43, March.
    10. Ioanna Makarouni & John Psarras & Eleftherios Siskos, 2015. "Interactive bicriterion decision support for a large scale industrial scheduling system," Annals of Operations Research, Springer, vol. 227(1), pages 45-61, April.
    11. Hoang, Kiet Tuan & Thilker, Christian Ankerstjerne & Knudsen, Brage Rugstad & Imsland, Lars Struen, 2024. "A hierarchical framework for minimising emissions in hybrid gas-renewable energy systems under forecast uncertainty," Applied Energy, Elsevier, vol. 373(C).
    12. Mohammadi, Mohammad & Noorollahi, Younes & Mohammadi-ivatloo, Behnam & Yousefi, Hossein, 2017. "Energy hub: From a model to a concept – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 80(C), pages 1512-1527.
    13. Sune Lauth Gadegaard & Andreas Klose & Lars Relund Nielsen, 2018. "A bi-objective approach to discrete cost-bottleneck location problems," Annals of Operations Research, Springer, vol. 267(1), pages 179-201, August.
    14. Nazari-Heris, M. & Mohammadi-Ivatloo, B. & Gharehpetian, G.B., 2018. "A comprehensive review of heuristic optimization algorithms for optimal combined heat and power dispatch from economic and environmental perspectives," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 2128-2143.
    15. Moghaddam, Iman Gerami & Saniei, Mohsen & Mashhour, Elaheh, 2016. "A comprehensive model for self-scheduling an energy hub to supply cooling, heating and electrical demands of a building," Energy, Elsevier, vol. 94(C), pages 157-170.
    16. Kirlik, Gokhan & Sayın, Serpil, 2014. "A new algorithm for generating all nondominated solutions of multiobjective discrete optimization problems," European Journal of Operational Research, Elsevier, vol. 232(3), pages 479-488.
    17. Masar Al-Rabeeah & Santosh Kumar & Ali Al-Hasani & Elias Munapo & Andrew Eberhard, 2019. "Bi-objective integer programming analysis based on the characteristic equation," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 10(5), pages 937-944, October.
    18. Parantapa Sawant & Oscar Villegas Mier & Michael Schmidt & Jens Pfafferott, 2021. "Demonstration of Optimal Scheduling for a Building Heat Pump System Using Economic-MPC," Energies, MDPI, vol. 14(23), pages 1-15, November.
    19. Di Martinelly, Christine & Meskens, Nadine, 2017. "A bi-objective integrated approach to building surgical teams and nurse schedule rosters to maximise surgical team affinities and minimise nurses' idle time," International Journal of Production Economics, Elsevier, vol. 191(C), pages 323-334.
    20. Muhammad Faisal Shehzad & Mainak Dan & Valerio Mariani & Seshadhri Srinivasan & Davide Liuzza & Carmine Mongiello & Roberto Saraceno & Luigi Glielmo, 2021. "A Heuristic Algorithm for Combined Heat and Power System Operation Management," Energies, MDPI, vol. 14(6), pages 1-22, March.

    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:gam:jmathe:v:10:y:2022:i:3:p:495-:d:741793. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.