IDEAS home Printed from https://ideas.repec.org/a/spr/jglopt/v55y2013i1p165-188.html
   My bibliography  Save this article

A multi-start opposition-based particle swarm optimization algorithm with adaptive velocity for bound constrained global optimization

Author

Listed:
  • Massimiliano Kaucic

Abstract

In this paper we present a multi-start particle swarm optimization algorithm for the global optimization of a function subject to bound constraints. The procedure consists of three main steps. In the initialization phase, an opposition learning strategy is performed to improve the search efficiency. Then a variant of the adaptive velocity based on the differential operator enhances the optimization ability of the particles. Finally, a re-initialization strategy based on two diversity measures for the swarm is act in order to avoid premature convergence and stagnation. The strategy uses the super-opposition paradigm to re-initialize particles in the swarm. The algorithm has been evaluated on a set of 100 global optimization test problems. Comparisons with other global optimization methods show the robustness and effectiveness of the proposed algorithm. Copyright Springer Science+Business Media, LLC. 2013

Suggested Citation

  • Massimiliano Kaucic, 2013. "A multi-start opposition-based particle swarm optimization algorithm with adaptive velocity for bound constrained global optimization," Journal of Global Optimization, Springer, vol. 55(1), pages 165-188, January.
    • Handle: RePEc:spr:jglopt:v:55:y:2013:i:1:p:165-188
    DOI: 10.1007/s10898-012-9913-4
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s10898-012-9913-4
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s10898-012-9913-4?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. Mishra, Sudhanshu, 2006. "Some new test functions for global optimization and performance of repulsive particle swarm method," MPRA Paper 2718, University Library of Munich, Germany.
    2. Jiao, Bin & Lian, Zhigang & Gu, Xingsheng, 2008. "A dynamic inertia weight particle swarm optimization algorithm," Chaos, Solitons & Fractals, Elsevier, vol. 37(3), pages 698-705.
    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. Ling Wang & Lu An & Jiaxing Pi & Minrui Fei & Panos M. Pardalos, 2017. "A diverse human learning optimization algorithm," Journal of Global Optimization, Springer, vol. 67(1), pages 283-323, January.
    2. Morteza Ahandani & Mohammad-Taghi Vakil-Baghmisheh & Mohammad Talebi, 2014. "Hybridizing local search algorithms for global optimization," Computational Optimization and Applications, Springer, vol. 59(3), pages 725-748, December.
    3. Tapas Si & Ramkinkar Dutta, 2019. "Partial Opposition-Based Particle Swarm Optimizer in Artificial Neural Network Training for Medical Data Classification," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 18(05), pages 1717-1750, September.
    4. Isen, Evren & Duman, Serhat, 2024. "Improved stochastic fractal search algorithm involving design operators for solving parameter extraction problems in real-world engineering optimization problems," Applied Energy, Elsevier, vol. 365(C).

    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. Mishra, SK, 2006. "Performance of Differential Evolution and Particle Swarm Methods on Some Relatively Harder Multi-modal Benchmark Functions," MPRA Paper 449, University Library of Munich, Germany.
    2. Weitao Sun & Yuan Dong, 2011. "Study of multiscale global optimization based on parameter space partition," Journal of Global Optimization, Springer, vol. 49(1), pages 149-172, January.
    3. Hou, Peng & Hu, Weihao & Soltani, Mohsen & Chen, Cong & Chen, Zhe, 2017. "Combined optimization for offshore wind turbine micro siting," Applied Energy, Elsevier, vol. 189(C), pages 271-282.
    4. Hou, Peng & Hu, Weihao & Chen, Cong & Soltani, Mohsen & Chen, Zhe, 2016. "Optimization of offshore wind farm layout in restricted zones," Energy, Elsevier, vol. 113(C), pages 487-496.
    5. Mehmet Hakan Satman & Emre Akadal, 2020. "Machine Coded Compact Genetic Algorithms for Real Parameter Optimization Problems," Alphanumeric Journal, Bahadir Fatih Yildirim, vol. 8(1), pages 43-58, June.
    6. Mengran Zhou & Tianyu Hu & Kai Bian & Wenhao Lai & Feng Hu & Oumaima Hamrani & Ziwei Zhu, 2021. "Short-Term Electric Load Forecasting Based on Variational Mode Decomposition and Grey Wolf Optimization," Energies, MDPI, vol. 14(16), pages 1-17, August.
    7. Wang Yong & Li Jing-yang & Li Chun-lei, 2013. "Double Flight-Modes Particle Swarm Optimization," Journal of Optimization, Hindawi, vol. 2013, pages 1-8, December.
    8. Mishra, SK, 2012. "Global optimization of some difficult benchmark functions by cuckoo-hostco-evolution meta-heuristics," MPRA Paper 40615, University Library of Munich, Germany.
    9. Linas Stripinis & Remigijus Paulavičius, 2022. "Experimental Study of Excessive Local Refinement Reduction Techniques for Global Optimization DIRECT-Type Algorithms," Mathematics, MDPI, vol. 10(20), pages 1-18, October.
    10. Alatas, Bilal & Akin, Erhan & Ozer, A. Bedri, 2009. "Chaos embedded particle swarm optimization algorithms," Chaos, Solitons & Fractals, Elsevier, vol. 40(4), pages 1715-1734.
    11. Timothy Haas, 2020. "Developing political-ecological theory: The need for many-task computing," PLOS ONE, Public Library of Science, vol. 15(11), pages 1-26, November.
    12. Ziadi, Raouf & Bencherif-Madani, Abdelatif & Ellaia, Rachid, 2016. "Continuous global optimization through the generation of parametric curves," Applied Mathematics and Computation, Elsevier, vol. 282(C), pages 65-83.
    13. Hou, Peng & Enevoldsen, Peter & Hu, Weihao & Chen, Cong & Chen, Zhe, 2017. "Offshore wind farm repowering optimization," Applied Energy, Elsevier, vol. 208(C), pages 834-844.
    14. Ali M. Eltamaly & Zeyad A. Almutairi & Mohamed A. Abdelhamid, 2023. "Modern Optimization Algorithm for Improved Performance of Maximum Power Point Tracker of Partially Shaded PV Systems," Energies, MDPI, vol. 16(13), pages 1-22, July.
    15. Deepika Garg & Sarita Devi, 2021. "RAP via hybrid genetic simulating annealing algorithm," 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. 12(3), pages 419-425, June.
    16. Mohammad Javad Amoshahy & Mousa Shamsi & Mohammad Hossein Sedaaghi, 2016. "A Novel Flexible Inertia Weight Particle Swarm Optimization Algorithm," PLOS ONE, Public Library of Science, vol. 11(8), pages 1-42, August.
    17. Beopsoo Kim & Nikita Rusetskii & Haesung Jo & Insu Kim, 2021. "The Optimal Allocation of Distributed Generators Considering Fault Current and Levelized Cost of Energy Using the Particle Swarm Optimization Method," Energies, MDPI, vol. 14(2), pages 1-18, January.
    18. Sudhanshu K Mishra, 2013. "Global Optimization of Some Difficult Benchmark Functions by Host-Parasite Coevolutionary Algorithm," Economics Bulletin, AccessEcon, vol. 33(1), pages 1-18.
    19. Mi Li & Huan Chen & Xiaodong Wang & Ning Zhong & Shengfu Lu, 2019. "An Improved Particle Swarm Optimization Algorithm with Adaptive Inertia Weights," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 18(03), pages 833-866, May.
    20. Liu, Hanwu & Lei, Yulong & Fu, Yao & Li, Xingzhong, 2022. "A novel hybrid-point-line energy management strategy based on multi-objective optimization for range-extended electric vehicle," Energy, Elsevier, vol. 247(C).

    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:spr:jglopt:v:55:y:2013:i:1:p:165-188. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.