IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v10y2022i22p4169-d966101.html
   My bibliography  Save this article

An Extended Membrane System Based on Cell-like P Systems and Improved Particle Swarm Optimization for Image Segmentation

Author

Listed:
  • Lin Wang

    (School of Management Engineering, Shandong JianZhu University, Jinan 250101, China)

  • Xiyu Liu

    (School of Business, Shandong Normal University, Jinan 250014, China)

  • Jianhua Qu

    (School of Business, Shandong Normal University, Jinan 250014, China)

  • Yuzhen Zhao

    (School of Business, Shandong Normal University, Jinan 250014, China)

  • Zhenni Jiang

    (School of Management Science and Engineering, Shandong University of Finance and Economics, Jinan 250014, China)

  • Ning Wang

    (School of Management Engineering, Shandong JianZhu University, Jinan 250101, China)

Abstract

An extended membrane system with a dynamic nested membrane structure, which is integrated with the evolution-communication mechanism of a cell-like P system with evolutional symport/antiport rules and active membranes (ECP), and the evolutionary mechanisms of particle swarm optimization (PSO) and improved PSO inspired by starling flock behavior (SPSO), named DSPSO-ECP, is designed and developed to try to break application restrictions of P systems in this paper. The purpose of DSPSO-ECP is to enhance the performance of extended membrane system in solving optimization problems. In the proposed DSPSO-ECP, the updated model of velocity and position of standard PSO, as basic evolution rules, are adopted to evolve objects in elementary membranes. The modified updated model of the velocity of improved SPSO is used as local evolution rules to evolve objects in sub-membranes. A group of sub-membranes for elementary membranes are specially designed to avoid prematurity through membrane creation and dissolution rules with promoter/inhibitor. The exchange and sharing of information between different membranes are achieved by communication rules for objects based on evolutional symport rules of ECP. At last, computational results, which are made on numerical benchmark functions and classic test images, are discussed and analyzed to validate the efficiency of the proposed DSPSO-ECP.

Suggested Citation

  • Lin Wang & Xiyu Liu & Jianhua Qu & Yuzhen Zhao & Zhenni Jiang & Ning Wang, 2022. "An Extended Membrane System Based on Cell-like P Systems and Improved Particle Swarm Optimization for Image Segmentation," Mathematics, MDPI, vol. 10(22), pages 1-32, November.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:22:p:4169-:d:966101
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/10/22/4169/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/10/22/4169/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Qiang Yang & Xu Guo & Xu-Dong Gao & Dong-Dong Xu & Zhen-Yu Lu, 2022. "Differential Elite Learning Particle Swarm Optimization for Global Numerical Optimization," Mathematics, MDPI, vol. 10(8), pages 1-32, April.
    2. Lin Wang & Xiyu Liu & Minghe Sun & Jianhua Qu, 2020. "An Extended Clustering Membrane System Based on Particle Swarm Optimization and Cell-Like P System with Active Membranes," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-18, January.
    3. Yudong Zhang & Shuihua Wang & Genlin Ji, 2015. "A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications," Mathematical Problems in Engineering, Hindawi, vol. 2015, pages 1-38, October.
    4. Qiang Yang & Yu-Wei Bian & Xu-Dong Gao & Dong-Dong Xu & Zhen-Yu Lu & Sang-Woon Jeon & Jun Zhang, 2022. "Stochastic Triad Topology Based Particle Swarm Optimization for Global Numerical Optimization," Mathematics, MDPI, vol. 10(7), pages 1-39, March.
    5. Qiang Yang & Yufei Jing & Xudong Gao & Dongdong Xu & Zhenyu Lu & Sang-Woon Jeon & Jun Zhang, 2022. "Predominant Cognitive Learning Particle Swarm Optimization for Global Numerical Optimization," Mathematics, MDPI, vol. 10(10), pages 1-35, May.
    6. Xiao Sang & Xiyu Liu & Zhe Zhang & Lin Wang, 2021. "Improved Biogeography-Based Optimization Algorithm by Hierarchical Tissue-Like P System with Triggering Ablation Rules," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-24, March.
    7. Lin Wang & Xiyu Liu & Minghe Sun & Jianhua Qu & Yanmeng Wei, 2018. "A New Chaotic Starling Particle Swarm Optimization Algorithm for Clustering Problems," Mathematical Problems in Engineering, Hindawi, vol. 2018, pages 1-14, August.
    8. Ping Guo & Wenjie Jiang & Yuchi Liu, 2019. "A P system for hierarchical clustering," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 30(08), pages 1-14, August.
    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. Tian-Tian Wang & Qiang Yang & Xu-Dong Gao, 2023. "Dual Elite Groups-Guided Differential Evolution for Global Numerical Optimization," Mathematics, MDPI, vol. 11(17), pages 1-51, August.
    2. Mariusz Korzeń & Maciej Kruszyna, 2023. "Modified Ant Colony Optimization as a Means for Evaluating the Variants of the City Railway Underground Section," IJERPH, MDPI, vol. 20(6), pages 1-15, March.
    3. Frédérique Bec & Heino Bohn Nielsen & Sarra Saïdi, 2020. "Mixed Causal–Noncausal Autoregressions: Bimodality Issues in Estimation and Unit Root Testing," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 82(6), pages 1413-1428, December.
    4. Mohammad Soleimani Amiri & Rizauddin Ramli & Ahmad Barari, 2023. "Optimally Initialized Model Reference Adaptive Controller of Wearable Lower Limb Rehabilitation Exoskeleton," Mathematics, MDPI, vol. 11(7), pages 1-14, March.
    5. Chenyang Gao & Teng Li & Yuelin Gao & Ziyu Zhang, 2024. "A Comprehensive Multi-Strategy Enhanced Biogeography-Based Optimization Algorithm for High-Dimensional Optimization and Engineering Design Problems," Mathematics, MDPI, vol. 12(3), pages 1-35, January.
    6. Byung-Ki Jeon & Eui-Jong Kim, 2021. "LSTM-Based Model Predictive Control for Optimal Temperature Set-Point Planning," Sustainability, MDPI, vol. 13(2), pages 1-14, January.
    7. Cui, Huixia & Chen, Xiangyong & Guo, Ming & Jiao, Yang & Cao, Jinde & Qiu, Jianlong, 2023. "A distribution center location optimization model based on minimizing operating costs under uncertain demand with logistics node capacity scalability," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 610(C).
    8. Grzegorz Sroka & Mariusz Oszust, 2021. "Approximation of the Constant in a Markov-Type Inequality on a Simplex Using Meta-Heuristics," Mathematics, MDPI, vol. 9(3), pages 1-10, January.
    9. Genbao Liu & Tengfei Zhao & Hong Yan & Han Wu & Fuming Wang, 2022. "Evaluation of Urban Green Building Design Schemes to Achieve Sustainability Based on the Projection Pursuit Model Optimized by the Atomic Orbital Search," Sustainability, MDPI, vol. 14(17), pages 1-23, September.
    10. Perera, A.T.D. & Soga, Kenichi & Xu, Yujie & Nico, Peter S. & Hong, Tianzhen, 2023. "Enhancing flexibility for climate change using seasonal energy storage (aquifer thermal energy storage) in distributed energy systems," Applied Energy, Elsevier, vol. 340(C).
    11. Zaiyu Gu & Guojiang Xiong & Xiaofan Fu, 2023. "Parameter Extraction of Solar Photovoltaic Cell and Module Models with Metaheuristic Algorithms: A Review," Sustainability, MDPI, vol. 15(4), pages 1-45, February.
    12. Tri-Hai Nguyen & Luong Vuong Nguyen & Jason J. Jung & Israel Edem Agbehadji & Samuel Ofori Frimpong & Richard C. Millham, 2020. "Bio-Inspired Approaches for Smart Energy Management: State of the Art and Challenges," Sustainability, MDPI, vol. 12(20), pages 1-24, October.
    13. Qiang Yang & Yufei Jing & Xudong Gao & Dongdong Xu & Zhenyu Lu & Sang-Woon Jeon & Jun Zhang, 2022. "Predominant Cognitive Learning Particle Swarm Optimization for Global Numerical Optimization," Mathematics, MDPI, vol. 10(10), pages 1-35, May.
    14. Mojgan Fayyazi & Paramjotsingh Sardar & Sumit Infent Thomas & Roonak Daghigh & Ali Jamali & Thomas Esch & Hans Kemper & Reza Langari & Hamid Khayyam, 2023. "Artificial Intelligence/Machine Learning in Energy Management Systems, Control, and Optimization of Hydrogen Fuel Cell Vehicles," Sustainability, MDPI, vol. 15(6), pages 1-38, March.
    15. Caiyang Wei & Theo Hofman & Esin Ilhan Caarls, 2021. "Co-Design of CVT-Based Electric Vehicles," Energies, MDPI, vol. 14(7), pages 1-33, March.
    16. Vamsi Krishna Reddy, Aala Kalananda & Venkata Lakshmi Narayana, Komanapalli, 2022. "Meta-heuristics optimization in electric vehicles -an extensive review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).
    17. Yanzheng Zhu & Yangbo Chen & Yanjun Zhao & Feng Zhou & Shichao Xu, 2023. "Application and Research of Liuxihe Model in the Simulation of Inflow Flood at Zaoshi Reservoir," Sustainability, MDPI, vol. 15(13), pages 1-14, June.
    18. Adebola Orogun & Oluwaseun Fadeyi & Ondrej Krejcar, 2019. "Sustainable Communication Systems: A Graph-Labeling Approach for Cellular Frequency Allocation in Densely-Populated Areas," Future Internet, MDPI, vol. 11(9), pages 1-14, August.
    19. Qiuping Ni & Yuanxiang Tang, 2023. "A Bibliometric Visualized Analysis and Classification of Vehicle Routing Problem Research," Sustainability, MDPI, vol. 15(9), pages 1-37, April.
    20. Sergey S. Berg, 2023. "Utility of Particle Swarm Optimization in Statistical Population Reconstruction," Mathematics, MDPI, vol. 11(4), pages 1-28, February.

    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:22:p:4169-:d:966101. 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.