IDEAS home Printed from https://ideas.repec.org/a/hin/complx/5301284.html
   My bibliography  Save this article

A Multiobjective Brain Storm Optimization Algorithm Based on Decomposition

Author

Listed:
  • Cai Dai
  • Xiujuan Lei

Abstract

Brain storm optimization (BSO) algorithm is a simple and effective evolutionary algorithm. Some multiobjective brain storm optimization algorithms have low search efficiency. This paper combines the decomposition technology and multiobjective brain storm optimization algorithm (MBSO/D) to improve the search efficiency. Given weight vectors transform a multiobjective optimization problem into a series of subproblems. The decomposition technology determines the neighboring clusters of each cluster. Solutions of adjacent clusters generate new solutions to update population. An adaptive selection strategy is used to balance exploration and exploitation. Besides, MBSO/D compares with three efficient state-of-the-art algorithms, e.g., NSGAII and MOEA/D, on twenty-two test problems. The experimental results show that MBSO/D is more efficient than compared algorithms and can improve the search efficiency for most test problems.

Suggested Citation

  • Cai Dai & Xiujuan Lei, 2019. "A Multiobjective Brain Storm Optimization Algorithm Based on Decomposition," Complexity, Hindawi, vol. 2019, pages 1-11, January.
    • Handle: RePEc:hin:complx:5301284
    DOI: 10.1155/2019/5301284
    as

    Download full text from publisher

    File URL: http://downloads.hindawi.com/journals/8503/2019/5301284.pdf
    Download Restriction: no
    File URL: http://downloads.hindawi.com/journals/8503/2019/5301284.xml
    Download Restriction: no
    File URL: https://libkey.io/10.1155/2019/5301284?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. Yuhui Shi, 2011. "An Optimization Algorithm Based on Brainstorming Process," International Journal of Swarm Intelligence Research (IJSIR), IGI Global, vol. 2(4), pages 35-62, October.
    2. Yuhui Shi & Jingqian Xue & Yali Wu, 2013. "Multi-Objective Optimization Based on Brain Storm Optimization Algorithm," International Journal of Swarm Intelligence Research (IJSIR), IGI Global, vol. 4(3), pages 1-21, July.
    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. Alberto Pajares & Xavier Blasco & Juan Manuel Herrero & Miguel A. Martínez, 2021. "A Comparison of Archiving Strategies for Characterization of Nearly Optimal Solutions under Multi-Objective Optimization," Mathematics, MDPI, vol. 9(9), pages 1-28, April.

    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. Yuansheng Huang & Lei Yang & Shijian Liu & Guangli Wang, 2019. "Multi-Step Wind Speed Forecasting Based On Ensemble Empirical Mode Decomposition, Long Short Term Memory Network and Error Correction Strategy," Energies, MDPI, vol. 12(10), pages 1-22, May.
    2. Lalit Kumar & Sushil Kumar Gupta & Sanjay Kumar, 2022. "A novel brainstorm based optimization method for optimum planning of reactive power with FACTS devices," 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. 13(6), pages 3062-3073, December.
    3. Tran, Dai-Duong & Vafaeipour, Majid & El Baghdadi, Mohamed & Barrero, Ricardo & Van Mierlo, Joeri & Hegazy, Omar, 2020. "Thorough state-of-the-art analysis of electric and hybrid vehicle powertrains: Topologies and integrated energy management strategies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 119(C).
    4. Gongdan Xu & Zhiwei Zhang & Zhiwu Li & Xiwang Guo & Liang Qi & Xianzhao Liu, 2023. "Multi-Objective Discrete Brainstorming Optimizer to Solve the Stochastic Multiple-Product Robotic Disassembly Line Balancing Problem Subject to Disassembly Failures," Mathematics, MDPI, vol. 11(6), pages 1-22, March.
    5. Babayan, Narek & Tahani, Mojtaba, 2019. "Team Arrangement Heuristic Algorithm (TAHA): Theory and application," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 166(C), pages 155-176.
    6. Wang, Xianjia & Lv, Shaojie & Quan, Ji, 2017. "The evolution of cooperation in the Prisoner’s Dilemma and the Snowdrift game based on Particle Swarm Optimization," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 482(C), pages 286-295.

    More about this item

    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:hin:complx:5301284. 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: Mohamed Abdelhakeem (email available below). General contact details of provider: https://www.hindawi.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.