IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v11y2023i11p2501-d1158856.html
   My bibliography  Save this article

Comparative Study of Type-1 and Interval Type-2 Fuzzy Logic Systems in Parameter Adaptation for the Fuzzy Discrete Mycorrhiza Optimization Algorithm

Author

Listed:
  • Hector Carreon-Ortiz

    (Tijuana Institute of Technology, TecNM, Tijuana 22379, Mexico)

  • Fevrier Valdez

    (Tijuana Institute of Technology, TecNM, Tijuana 22379, Mexico)

  • Oscar Castillo

    (Tijuana Institute of Technology, TecNM, Tijuana 22379, Mexico)

Abstract

The Fuzzy Discrete Mycorrhiza Optimization (FDMOA) Algorithm is a new hybrid optimization method using the Discrete Mycorrhiza Optimization Algorithm (DMOA) in combination with type-1 or interval type-2 fuzzy logic system. In this new research, when using T1FLS, membership functions are defined by type-1 fuzzy sets, which allows for a more flexible and natural representation of uncertain and imprecise data. This approach has been successfully applied to several optimization problems, such as in feature selection, image segmentation, and data clustering. On the other hand, when DMOA is using IT2FLS, membership functions are represented by interval type-2 fuzzy sets, which allows for a more robust and accurate representation of uncertainty. This approach has been shown to handle higher levels of uncertainty and noise in the input data and has been successfully applied to various optimization problems, including control systems, pattern recognition, and decision-making. Both DMOA using T1FLS and DMOA using IT2FLS have shown better performance than the original DMOA algorithm in many applications. The combination of DMOA with fuzzy logic systems provides a powerful and flexible optimization framework that can be adapted to various problem domains. In addition, these techniques have the potential to more efficiently and effectively solve real-world problems.

Suggested Citation

  • Hector Carreon-Ortiz & Fevrier Valdez & Oscar Castillo, 2023. "Comparative Study of Type-1 and Interval Type-2 Fuzzy Logic Systems in Parameter Adaptation for the Fuzzy Discrete Mycorrhiza Optimization Algorithm," Mathematics, MDPI, vol. 11(11), pages 1-38, May.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:11:p:2501-:d:1158856
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/11/11/2501/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/11/11/2501/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Urmila M. Diwekar, 2020. "Introduction to Applied Optimization," Springer Optimization and Its Applications, Springer, edition 3, number 978-3-030-55404-0, December.
    2. Mohammad Bagher Ghaemi & Nahid Gharakhanlu & Themistocles M. Rassias & Reza Saadati, 2021. "Advances in Matrix Inequalities," Springer Optimization and Its Applications, Springer, number 978-3-030-76047-2, December.
    3. Mao, Kun & Pan, Quan-ke & Pang, Xinfu & Chai, Tianyou, 2014. "A novel Lagrangian relaxation approach for a hybrid flowshop scheduling problem in the steelmaking-continuous casting process," European Journal of Operational Research, Elsevier, vol. 236(1), pages 51-60.
    4. Stavros P. Adam & Stamatios-Aggelos N. Alexandropoulos & Panos M. Pardalos & Michael N. Vrahatis, 2019. "No Free Lunch Theorem: A Review," Springer Optimization and Its Applications, in: Ioannis C. Demetriou & Panos M. Pardalos (ed.), Approximation and Optimization, pages 57-82, Springer.
    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. Masoud Zahedi Vahid & Ziad M. Ali & Ebrahim Seifi Najmi & Abdollah Ahmadi & Foad H. Gandoman & Shady H. E. Abdel Aleem, 2021. "Optimal Allocation and Planning of Distributed Power Generation Resources in a Smart Distribution Network Using the Manta Ray Foraging Optimization Algorithm," Energies, MDPI, vol. 14(16), pages 1-25, August.
    2. Jilong Zhang & Yuan Diao, 2024. "Hierarchical Learning-Enhanced Chaotic Crayfish Optimization Algorithm: Improving Extreme Learning Machine Diagnostics in Breast Cancer," Mathematics, MDPI, vol. 12(17), pages 1-26, August.
    3. Hou, Meng & He, Qiushi & Ma, Yuechao, 2024. "Quantized adaptive practical fixed-time synchronization of stochastic complex networks with actuator faults," Chaos, Solitons & Fractals, Elsevier, vol. 181(C).
    4. Seyed Mojtaba Hoseyni & Francesco Di Maio & Enrico Zio, 2023. "Subset simulation for optimal sensors positioning based on value of information," Journal of Risk and Reliability, , vol. 237(5), pages 897-909, October.
    5. G. Rius-Sorolla & J. Maheut & Jairo R. Coronado-Hernandez & J. P. Garcia-Sabater, 2020. "Lagrangian relaxation of the generic materials and operations planning model," 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. 28(1), pages 105-123, March.
    6. Pieter Moerloose & Broos Maenhout, 2023. "A two-stage local search heuristic for solving the steelmaking continuous casting scheduling problem with dual shared-resource and blocking constraints," Operational Research, Springer, vol. 23(1), pages 1-43, March.
    7. Marco-Antonio Moreno-Ibarra & Yenny Villuendas-Rey & Miltiadis D. Lytras & Cornelio Yáñez-Márquez & Julio-César Salgado-Ramírez, 2021. "Classification of Diseases Using Machine Learning Algorithms: A Comparative Study," Mathematics, MDPI, vol. 9(15), pages 1-21, July.
    8. Jahedul Islam & Md Shokor A. Rahaman & Pandian M. Vasant & Berihun Mamo Negash & Ahshanul Hoqe & Hitmi Khalifa Alhitmi & Junzo Watada, 2021. "A Modified Niching Crow Search Approach to Well Placement Optimization," Energies, MDPI, vol. 14(4), pages 1-33, February.
    9. Torres, Nelson & Greivel, Gus & Betz, Joshua & Moreno, Eduardo & Newman, Alexandra & Thomas, Brian, 2024. "Optimizing steel coil production schedules under continuous casting and hot rolling," European Journal of Operational Research, Elsevier, vol. 314(2), pages 496-508.
    10. Pan, Quan-Ke, 2016. "An effective co-evolutionary artificial bee colony algorithm for steelmaking-continuous casting scheduling," European Journal of Operational Research, Elsevier, vol. 250(3), pages 702-714.
    11. Ünal, Yusuf Ziya & Uysal, Özgür, 2014. "A new mixed integer programming model for curriculum balancing: Application to a Turkish university," European Journal of Operational Research, Elsevier, vol. 238(1), pages 339-347.
    12. Marco Pastori & Angel Udias & Luigi Cattaneo & Magda Moner-Girona & Awa Niang & Cesar Carmona-Moreno, 2021. "Bioenergy Potential of Crop Residues in the Senegal River Basin: A Cropland–Energy–Water-Environment Nexus Approach," Sustainability, MDPI, vol. 13(19), pages 1-23, October.
    13. Oussama Ouramdane & Elhoussin Elbouchikhi & Yassine Amirat & Ehsan Sedgh Gooya, 2021. "Optimal Sizing and Energy Management of Microgrids with Vehicle-to-Grid Technology: A Critical Review and Future Trends," Energies, MDPI, vol. 14(14), pages 1-45, July.
    14. Ursavas, Evrim, 2017. "A benders decomposition approach for solving the offshore wind farm installation planning at the North Sea," European Journal of Operational Research, Elsevier, vol. 258(2), pages 703-714.
    15. Liu, Min & Jiang, Shenglong & Wu, Cheng, 2015. "A soft-decision based two-layered scheduling approach for uncertain steelmaking-continuous casting processAuthor-Name: Hao, Jinghua," European Journal of Operational Research, Elsevier, vol. 244(3), pages 966-979.
    16. José-Luis Velázquez-Rodríguez & Yenny Villuendas-Rey & Oscar Camacho-Nieto & Cornelio Yáñez-Márquez, 2020. "A Novel and Simple Mathematical Transform Improves the Perfomance of Lernmatrix in Pattern Classification," Mathematics, MDPI, vol. 8(5), pages 1-46, May.
    17. Elena Niculina Dragoi & Vlad Dafinescu, 2021. "Review of Metaheuristics Inspired from the Animal Kingdom," Mathematics, MDPI, vol. 9(18), pages 1-52, September.
    18. Mokhtar Said & Ali M. El-Rifaie & Mohamed A. Tolba & Essam H. Houssein & Sanchari Deb, 2021. "An Efficient Chameleon Swarm Algorithm for Economic Load Dispatch Problem," Mathematics, MDPI, vol. 9(21), pages 1-14, November.
    19. Hui, Jiuwu, 2024. "Coordinated discrete-time super-twisting sliding mode controller coupled with time-delay estimator for PWR-based nuclear steam supply system," Energy, Elsevier, vol. 301(C).
    20. Eliton Smith dos Santos & Marcus Vinícius Alves Nunes & Manoel Henrique Reis Nascimento & Jandecy Cabral Leite, 2022. "Rational Application of Electric Power Production Optimization through Metaheuristics Algorithm," Energies, MDPI, vol. 15(9), pages 1-31, April.

    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:11:y:2023:i:11:p:2501-:d:1158856. 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.