IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v10y2022i21p4162-d965594.html
   My bibliography  Save this article

Moth Search: Variants, Hybrids, and Applications

Author

Listed:
  • Juan Li

    (School of Information Engineering, Wuhan Business University, Wuhan 430056, China
    School of Artificial Intelligence, Wuchang University of Technology, Wuhan 430223, China
    Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, China)

  • Yuan-Hua Yang

    (School of Computer and Information Engineering, Hubei Normal University, Huangshi 435002, China)

  • Qing An

    (School of Artificial Intelligence, Wuchang University of Technology, Wuhan 430223, China)

  • Hong Lei

    (School of Artificial Intelligence, Wuchang University of Technology, Wuhan 430223, China)

  • Qian Deng

    (School of Artificial Intelligence, Wuchang University of Technology, Wuhan 430223, China)

  • Gai-Ge Wang

    (Department of Computer Science and Technology, Ocean University of China, Qingdao 266100, China
    Intelligent Information Systems Institute, Wenzhou University, Wenzhou 325035, China
    Key Laboratory of Intelligent Image Processing and Analysis, Wenzhou 325035, China)

Abstract

Moth search (MS) is a nature-inspired metaheuristic optimization algorithm based on the most representative characteristics of moths, Lévy flights and phototaxis. Phototaxis signifies a movement which organism towards or away from a source of light, which is the representative features for moths. The best moth individual is seen as the light source in Moth search. The moths that have a smaller distance from the best one will fly around the best individual by Lévy flights. For reasons of phototaxis, the moths, far from the fittest one, will fly towards the best one with a big step. These two features, Lévy flights and phototaxis, correspond to the processes of exploitation and exploration for metaheuristic optimization. The superiority of the moth search has been demonstrated in many benchmark problems and various application areas. A comprehensive survey of the moth search was conducted in this paper, which included the three sections: statistical research studies about moth search, different variants of moth search, and engineering optimization/applications. The future insights and development direction in the area of moth search are also discussed.

Suggested Citation

  • Juan Li & Yuan-Hua Yang & Qing An & Hong Lei & Qian Deng & Gai-Ge Wang, 2022. "Moth Search: Variants, Hybrids, and Applications," Mathematics, MDPI, vol. 10(21), pages 1-19, November.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:21:p:4162-:d:965594
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/10/21/4162/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/10/21/4162/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Juan Li & Dan-dan Xiao & Hong Lei & Ting Zhang & Tian Tian, 2020. "Using Cuckoo Search Algorithm with Q -Learning and Genetic Operation to Solve the Problem of Logistics Distribution Center Location," Mathematics, MDPI, vol. 8(2), pages 1-32, January.
    2. Fathy, Ahmed & Elaziz, Mohamed Abd & Sayed, Enas Taha & Olabi, A.G. & Rezk, Hegazy, 2019. "Optimal parameter identification of triple-junction photovoltaic panel based on enhanced moth search algorithm," Energy, Elsevier, vol. 188(C).
    3. Pauline Ong & Chon Haow Chong & Mohammad Zulafif Rahim & Woon Kiow Lee & Chee Kiong Sia & Muhammad Ariff Haikal Ahmad, 2020. "Intelligent approach for process modelling and optimization on electrical discharge machining of polycrystalline diamond," Journal of Intelligent Manufacturing, Springer, vol. 31(1), pages 227-247, January.
    4. Yi Chu & Boxiao Liu & Shaowei Cai & Chuan Luo & Haihang You, 2020. "An efficient local search algorithm for solving maximum edge weight clique problem in large graphs," Journal of Combinatorial Optimization, Springer, vol. 39(4), pages 933-954, May.
    5. Juan Li & Hong Lei & Amir H. Alavi & Gai-Ge Wang, 2020. "Elephant Herding Optimization: Variants, Hybrids, and Applications," Mathematics, MDPI, vol. 8(9), pages 1-25, August.
    6. Minhee Kim & Junjae Chae, 2019. "Monarch Butterfly Optimization for Facility Layout Design Based on a Single Loop Material Handling Path," Mathematics, MDPI, vol. 7(2), pages 1-21, February.
    7. Xuan Chen & Feng Cheng & Cong Liu & Long Cheng & Yin Mao, 2021. "An improved Wolf pack algorithm for optimization problems: Design and evaluation," PLOS ONE, Public Library of Science, vol. 16(8), pages 1-23, August.
    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. Yanhong Feng & Hongmei Wang & Zhaoquan Cai & Mingliang Li & Xi Li, 2023. "Hybrid Learning Moth Search Algorithm for Solving Multidimensional Knapsack Problems," Mathematics, MDPI, vol. 11(8), 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. Juan Li & Qing An & Hong Lei & Qian Deng & Gai-Ge Wang, 2022. "Survey of Lévy Flight-Based Metaheuristics for Optimization," Mathematics, MDPI, vol. 10(15), pages 1-27, August.
    2. Emad M. Ahmed & Mokhtar Aly & Manar Mostafa & Hegazy Rezk & Hammad Alnuman & Waleed Alhosaini, 2022. "An Accurate Model for Bifacial Photovoltaic Panels," Sustainability, MDPI, vol. 15(1), pages 1-27, December.
    3. Papul Changmai & Sunil Deka & Shashank Kumar & Thanikanti Sudhakar Babu & Belqasem Aljafari & Benedetto Nastasi, 2022. "A Critical Review on the Estimation Techniques of the Solar PV Cell’s Unknown Parameters," Energies, MDPI, vol. 15(19), pages 1-20, September.
    4. Mohamed Abdel-Basset & Reda Mohamed & Karam M. Sallam & Ripon K. Chakrabortty, 2022. "Light Spectrum Optimizer: A Novel Physics-Inspired Metaheuristic Optimization Algorithm," Mathematics, MDPI, vol. 10(19), pages 1-63, September.
    5. Olabi, A.G. & Wilberforce, Tabbi & Abdelkareem, Mohammad Ali, 2021. "Fuel cell application in the automotive industry and future perspective," Energy, Elsevier, vol. 214(C).
    6. Meshari Alsharari & Ammar Armghan & Khaled Aliqab, 2023. "Numerical Analysis and Parametric Optimization of T-Shaped Symmetrical Metasurface with Broad Bandwidth for Solar Absorber Application Based on Graphene Material," Mathematics, MDPI, vol. 11(4), pages 1-15, February.
    7. Cui, Yuanlong & Zhu, Jie & Zhang, Fan & Shao, Yiming & Xue, Yibing, 2022. "Current status and future development of hybrid PV/T system with PCM module: 4E (energy, exergy, economic and environmental) assessments," Renewable and Sustainable Energy Reviews, Elsevier, vol. 158(C).
    8. Mehmet Burak Şenol & Ekrem Alper Murat, 2023. "A sequential solution heuristic for continuous facility layout problems," Annals of Operations Research, Springer, vol. 320(1), pages 355-377, January.
    9. Hegazy Rezk & Basem Alamri & Mokhtar Aly & Ahmed Fathy & Abdul G. Olabi & Mohammad Ali Abdelkareem & Hamdy A. Ziedan, 2021. "Multicriteria Decision-Making to Determine the Optimal Energy Management Strategy of Hybrid PV–Diesel Battery-Based Desalination System," Sustainability, MDPI, vol. 13(8), pages 1-19, April.
    10. Li, Shuijia & Gong, Wenyin & Gu, Qiong, 2021. "A comprehensive survey on meta-heuristic algorithms for parameter extraction of photovoltaic models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 141(C).
    11. Zuo, Wei & Li, Qingqing & He, Zhu & Li, Yawei, 2020. "Numerical investigations on thermal performance enhancement of hydrogen-fueled micro planar combustors with injectors for micro-thermophotovoltaic applications," Energy, Elsevier, vol. 194(C).
    12. Madhusmita Das & Biju R. Mohan & Ram Mohana Reddy Guddeti & Nandini Prasad, 2024. "Hybrid Bio-Optimized Algorithms for Hyperparameter Tuning in Machine Learning Models: A Software Defect Prediction Case Study," Mathematics, MDPI, vol. 12(16), pages 1-31, August.
    13. Hegazy Rezk & Ahmed Fathy, 2020. "Stochastic Fractal Search Optimization Algorithm Based Global MPPT for Triple-Junction Photovoltaic Solar System," Energies, MDPI, vol. 13(18), pages 1-28, September.
    14. Chen, Xiao & Cao, Benyi & Pouramini, Somayeh, 2023. "Energy cost and consumption reduction of an office building by Chaotic Satin Bowerbird Optimization Algorithm with model predictive control and artificial neural network: A case study," Energy, Elsevier, vol. 270(C).
    15. Elena Niculina Dragoi & Vlad Dafinescu, 2021. "Review of Metaheuristics Inspired from the Animal Kingdom," Mathematics, MDPI, vol. 9(18), pages 1-52, September.
    16. Jun Wu & Minghao Yin, 2021. "A Restart Local Search for Solving Diversified Top- k Weight Clique Search Problem," Mathematics, MDPI, vol. 9(21), pages 1-17, October.
    17. Hsieh, Tsung-Jung, 2023. "A Q-learning guided search for developing a hybrid of mixed redundancy strategies to improve system reliability," Reliability Engineering and System Safety, Elsevier, vol. 236(C).
    18. Qais, Mohammed H. & Hasanien, Hany M. & Alghuwainem, Saad, 2020. "Parameters extraction of three-diode photovoltaic model using computation and Harris Hawks optimization," Energy, Elsevier, vol. 195(C).
    19. Enas Taha Sayed & Hussain Alawadhi & Khaled Elsaid & A. G. Olabi & Maryam Adel Almakrani & Shaikha Tamim Bin Tamim & Ghada H. M. Alafranji & Mohammad Ali Abdelkareem, 2020. "A Carbon-Cloth Anode Electroplated with Iron Nanostructure for Microbial Fuel Cell Operated with Real Wastewater," Sustainability, MDPI, vol. 12(16), pages 1-11, August.
    20. Nassef, Ahmed M. & Houssein, Essam H. & Helmy, Bahaa El-din & Rezk, Hegazy, 2022. "Modified honey badger algorithm based global MPPT for triple-junction solar photovoltaic system under partial shading condition and global optimization," Energy, Elsevier, vol. 254(PA).

    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:21:p:4162-:d:965594. 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.