IDEAS home Printed from https://ideas.repec.org/a/spr/ijsaem/v13y2022i4d10.1007_s13198-021-01516-x.html
   My bibliography  Save this article

Real power loss reduction by Q-learning and hyper-heuristic method

Author

Listed:
  • Lenin Kanagasabai

    (Prasad V. Potluri Siddhartha Institute of Technology)

Abstract

This paper proposes an algorithm endorsement design using Q-learning and hyper-heuristic method (QH) to support choice architects select the supreme appropriate bio-inspired algorithm for the power loss reduction problem. For this an artificial bee colony (ABC) algorithm, Mobulidae optimization algorithm (MOA), enhanced Salp swarm algorithm (ESS) and Orcinus orca optimization (OOO) algorithm are employed as small level optimizers consequently that the Q-learning and hyper-heuristic robotically pick the optimizer in every cycle of the optimization procedure. Q-learning is a prototypical unrestricted fortification learning procedure to discover the optimal solution. In Q-learning, representatives interrelate with the environs, and their segment is rationalized. At every segment, a representative does engagements and obtains an incentive or fine. Q-learning contains of five constituents, including representatives, environs, engagements, segment, and incentive. In this paper, Q-learning intends to pick the bio-inspired algorithm in every series of the run. Hyper-heuristic is demarcated as an elevated heuristic that exploits a set of small level heuristics to determine the preeminent solution. A hyper-heuristic is frequently used to select a local examine tool such as inset, passage, and exchange. In this paper ABC, MOA, ESS, and OOO are engaged as small level heuristics. As a response tool, extra fruitful algorithms are endorsed based on the grade of development. Proposed QH is corroborated in IEEE 30 bus system and loss lessening is amplified.

Suggested Citation

  • Lenin Kanagasabai, 2022. "Real power loss reduction by Q-learning and hyper-heuristic method," 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(4), pages 1607-1622, August.
    • Handle: RePEc:spr:ijsaem:v:13:y:2022:i:4:d:10.1007_s13198-021-01516-x
    DOI: 10.1007/s13198-021-01516-x
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s13198-021-01516-x
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s13198-021-01516-x?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. Subbaraj, P. & Rengaraj, R. & Salivahanan, S., 2009. "Enhancement of combined heat and power economic dispatch using self adaptive real-coded genetic algorithm," Applied Energy, Elsevier, vol. 86(6), pages 915-921, June.
    2. Edmund K Burke & Michel Gendreau & Matthew Hyde & Graham Kendall & Gabriela Ochoa & Ender Özcan & Rong Qu, 2013. "Hyper-heuristics: a survey of the state of the art," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 64(12), pages 1695-1724, December.
    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. Ahmed Kheiri & Alina G. Dragomir & David Mueller & Joaquim Gromicho & Caroline Jagtenberg & Jelke J. Hoorn, 2019. "Tackling a VRP challenge to redistribute scarce equipment within time windows using metaheuristic algorithms," EURO Journal on Transportation and Logistics, Springer;EURO - The Association of European Operational Research Societies, vol. 8(5), pages 561-595, December.
    2. Andrzej Kozik, 2017. "Handling precedence constraints in scheduling problems by the sequence pair representation," Journal of Combinatorial Optimization, Springer, vol. 33(2), pages 445-472, February.
    3. Bach Hoang Dinh & Thang Trung Nguyen & Nguyen Vu Quynh & Le Van Dai, 2018. "A Novel Method for Economic Dispatch of Combined Heat and Power Generation," Energies, MDPI, vol. 11(11), pages 1-27, November.
    4. Vinay Kumar Jadoun & G. Rahul Prashanth & Siddharth Suhas Joshi & Anshul Agarwal & Hasmat Malik & Majed A. Alotaibi & Abdulaziz Almutairi, 2021. "Optimal Scheduling of Non-Convex Cogeneration Units Using Exponentially Varying Whale Optimization Algorithm," Energies, MDPI, vol. 14(4), pages 1-30, February.
    5. W. B. Yates & E. C. Keedwell, 2019. "An analysis of heuristic subsequences for offline hyper-heuristic learning," Journal of Heuristics, Springer, vol. 25(3), pages 399-430, June.
    6. Derya Deliktaş, 2022. "Self-adaptive memetic algorithms for multi-objective single machine learning-effect scheduling problems with release times," Flexible Services and Manufacturing Journal, Springer, vol. 34(3), pages 748-784, September.
    7. Huang, Jinbo & Li, Zhigang & Wu, Q.H., 2017. "Coordinated dispatch of electric power and district heating networks: A decentralized solution using optimality condition decomposition," Applied Energy, Elsevier, vol. 206(C), pages 1508-1522.
    8. Surafel Luleseged Tilahun & Mohamed A. Tawhid, 2019. "Swarm hyperheuristic framework," Journal of Heuristics, Springer, vol. 25(4), pages 809-836, October.
    9. Beigvand, Soheil Derafshi & Abdi, Hamdi & La Scala, Massimo, 2017. "A general model for energy hub economic dispatch," Applied Energy, Elsevier, vol. 190(C), pages 1090-1111.
    10. Venkatesh Pandiri & Alok Singh, 2020. "Two multi-start heuristics for the k-traveling salesman problem," OPSEARCH, Springer;Operational Research Society of India, vol. 57(4), pages 1164-1204, December.
    11. Guozheng Li & Rui Wang & Tao Zhang & Mengjun Ming, 2018. "Multi-Objective Optimal Design of Renewable Energy Integrated CCHP System Using PICEA-g," Energies, MDPI, vol. 11(4), pages 1-26, March.
    12. Glotić, Arnel & Zamuda, Aleš, 2015. "Short-term combined economic and emission hydrothermal optimization by surrogate differential evolution," Applied Energy, Elsevier, vol. 141(C), pages 42-56.
    13. Li, Wenwen & Özcan, Ender & John, Robert, 2017. "Multi-objective evolutionary algorithms and hyper-heuristics for wind farm layout optimisation," Renewable Energy, Elsevier, vol. 105(C), pages 473-482.
    14. Jari Kyngäs & Kimmo Nurmi & Nico Kyngäs & George Lilley & Thea Salter & Dries Goossens, 2017. "Scheduling the Australian Football League," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(8), pages 973-982, August.
    15. Sara Ceschia & Rosita Guido & Andrea Schaerf, 2020. "Solving the static INRC-II nurse rostering problem by simulated annealing based on large neighborhoods," Annals of Operations Research, Springer, vol. 288(1), pages 95-113, May.
    16. Paramjeet Kaur & Krishna Teerth Chaturvedi & Mohan Lal Kolhe, 2023. "Combined Heat and Power Economic Dispatching within Energy Network using Hybrid Metaheuristic Technique," Energies, MDPI, vol. 16(3), pages 1-17, January.
    17. Aleksandra Swiercz & Wojciech Frohmberg & Michal Kierzynka & Pawel Wojciechowski & Piotr Zurkowski & Jan Badura & Artur Laskowski & Marta Kasprzak & Jacek Blazewicz, 2018. "GRASShopPER—An algorithm for de novo assembly based on GPU alignments," PLOS ONE, Public Library of Science, vol. 13(8), pages 1-23, August.
    18. Stefan Vonolfen & Michael Affenzeller, 2016. "Distribution of waiting time for dynamic pickup and delivery problems," Annals of Operations Research, Springer, vol. 236(2), pages 359-382, January.
    19. Zhou, Tianmin & Chen, Jiamin & Xu, Xuancong & Ou, Zuhong & Yin, Hao & Luo, Jianqiang & Meng, Anbo, 2023. "A novel multi-agent based crisscross algorithm with hybrid neighboring topology for combined heat and power economic dispatch," Applied Energy, Elsevier, vol. 342(C).
    20. Garside, Annisa Kesy & Ahmad, Robiah & Muhtazaruddin, Mohd Nabil Bin, 2024. "A recent review of solution approaches for green vehicle routing problem and its variants," Operations Research Perspectives, Elsevier, vol. 12(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:ijsaem:v:13:y:2022:i:4:d:10.1007_s13198-021-01516-x. 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.