IDEAS home Printed from https://ideas.repec.org/a/wut/journl/v33y2023i1p113-150id8.html
   My bibliography  Save this article

Gold rush optimizer: A new population-based metaheuristic algorithm

Author

Listed:
  • Kamran Zolfi

Abstract

Today’s world is characterised by competitive environments, optimal resource utilization, and cost reduction, which has resulted in an increasing role for metaheuristic algorithms in solving complex modern problems. As a result, this paper introduces the gold rush optimizer (GRO), a population-based metaheuristic algorithm that simulates how gold-seekers prospected for gold during the Gold Rush Era using three key concepts of gold prospecting: migration, collaboration, and panning. The GRO algorithm is compared to twelve well-known metaheuristic algorithms on 29 benchmark test cases to assess the pro- posed approach’s performance. For scientific evaluation, the Friedman and Wilcoxon signed-rank tests are used. In addition to these test cases, the GRO algorithm is evaluated using three real-world engineering problems. The results indicated that the proposed algorithm was more capable than other algorithms in proposing qualitative and competitive solutions.

Suggested Citation

  • Kamran Zolfi, 2023. "Gold rush optimizer: A new population-based metaheuristic algorithm," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 33(1), pages 113-150.
  • Handle: RePEc:wut:journl:v:33:y:2023:i:1:p:113-150:id:8
    DOI: 10.37190/ord230108
    as

    Download full text from publisher

    File URL: https://ord.pwr.edu.pl/assets/papers_archive/ord2023vol33no1_8.pdf
    Download Restriction: no

    File URL: https://libkey.io/10.37190/ord230108?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. Clay, Karen & Jones, Randall, 2008. "Migrating to Riches? Evidence from the California Gold Rush," The Journal of Economic History, Cambridge University Press, vol. 68(4), pages 997-1027, December.
    2. David H. Wolpert & William G. Macready, 1995. "No Free Lunch Theorems for Search," Working Papers 95-02-010, Santa Fe Institute.
    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. Kronenberg, Christoph, 2020. "New(spaper) Evidence of a Reduction in Suicide Mentions during the 19th‐century US Gold Rush," CINCH Working Paper Series (since 2020) 73382, Duisburg-Essen University Library, DuEPublico.
    2. Jui-Sheng Chou & Dinh-Nhat Truong & Chih-Fong Tsai, 2021. "Solving Regression Problems with Intelligent Machine Learner for Engineering Informatics," Mathematics, MDPI, vol. 9(6), pages 1-25, March.
    3. Christoph Kronenberg, 2021. "New(spaper) evidence of a reduction in suicide mentions during the 19th century US gold rush," Health Economics, John Wiley & Sons, Ltd., vol. 30(10), pages 2582-2594, September.
    4. Sevvandi Kandanaarachchi & Mario A Munoz & Rob J Hyndman & Kate Smith-Miles, 2018. "On normalization and algorithm selection for unsupervised outlier detection," Monash Econometrics and Business Statistics Working Papers 16/18, Monash University, Department of Econometrics and Business Statistics.
    5. Aktaş, Dilay & Lokman, Banu & İnkaya, Tülin & Dejaegere, Gilles, 2024. "Cluster ensemble selection and consensus clustering: A multi-objective optimization approach," European Journal of Operational Research, Elsevier, vol. 314(3), pages 1065-1077.
    6. William G. Macready & David H. Wolpert, 1995. "What Makes an Optimization Problem Hard?," Working Papers 95-05-046, Santa Fe Institute.
    7. Y.C. Ho & D.L. Pepyne, 2002. "Simple Explanation of the No-Free-Lunch Theorem and Its Implications," Journal of Optimization Theory and Applications, Springer, vol. 115(3), pages 549-570, December.
    8. Murtadha Al-Kaabi & Virgil Dumbrava & Mircea Eremia, 2022. "A Slime Mould Algorithm Programming for Solving Single and Multi-Objective Optimal Power Flow Problems with Pareto Front Approach: A Case Study of the Iraqi Super Grid High Voltage," Energies, MDPI, vol. 15(20), pages 1-33, October.
    9. Galioto, Francesco & Battilani, Adriano, 2021. "Agro-economic simulation for day by day irrigation scheduling optimisation," Agricultural Water Management, Elsevier, vol. 248(C).
    10. Couttenier, Mathieu & Sangnier, Marc, 2015. "Living in the Garden of Eden: Mineral resources and preferences for redistribution," Journal of Comparative Economics, Elsevier, vol. 43(2), pages 243-256.
    11. Gary D. Libecap, 2018. "Property Rights to Frontier Land and Minerals: US Exceptionalism," NBER Working Papers 24544, National Bureau of Economic Research, Inc.
    12. Brodeur, Abel & Haddad, Joanne, 2021. "Institutions, attitudes and LGBT: Evidence from the gold rush," Journal of Economic Behavior & Organization, Elsevier, vol. 187(C), pages 92-110.
    13. Abdel-Rahman Hedar & Emad Mabrouk & Masao Fukushima, 2011. "Tabu Programming: A New Problem Solver Through Adaptive Memory Programming Over Tree Data Structures," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 10(02), pages 373-406.
    14. Mathieu Couttenier & Pauline Grosjean & Marc Sangnier, 2017. "The Wild West IS Wild: The Homicide Resource Curse," Journal of the European Economic Association, European Economic Association, vol. 15(3), pages 558-585.
    15. Murtadha Al-Kaabi & Virgil Dumbrava & Mircea Eremia, 2024. "Multi Criteria Frameworks Using New Meta-Heuristic Optimization Techniques for Solving Multi-Objective Optimal Power Flow Problems," Energies, MDPI, vol. 17(9), pages 1-39, May.
    16. Agarwal, Anurag & Colak, Selcuk & Eryarsoy, Enes, 2006. "Improvement heuristic for the flow-shop scheduling problem: An adaptive-learning approach," European Journal of Operational Research, Elsevier, vol. 169(3), pages 801-815, March.
    17. Julien Prat & Benjamin Walter, 2021. "An Equilibrium Model of the Market for Bitcoin Mining," Journal of Political Economy, University of Chicago Press, vol. 129(8), pages 2415-2452.
    18. Murtadha Al-Kaabi & Virgil Dumbrava & Mircea Eremia, 2022. "Single and Multi-Objective Optimal Power Flow Based on Hunger Games Search with Pareto Concept Optimization," Energies, MDPI, vol. 15(22), pages 1-31, November.
    19. Muangkote, Nipotepat & Sunat, Khamron & Chiewchanwattana, Sirapat & Kaiwinit, Sirilak, 2019. "An advanced onlooker-ranking-based adaptive differential evolution to extract the parameters of solar cell models," Renewable Energy, Elsevier, vol. 134(C), pages 1129-1147.
    20. William G. Macready & David H. Wolpert, 1996. "On 2-Armed Gaussian Bandits and Optimization," Working Papers 96-03-009, Santa Fe Institute.

    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:wut:journl:v:33:y:2023:i:1:p:113-150:id:8. 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: Adam Kasperski (email available below). General contact details of provider: https://edirc.repec.org/data/iopwrpl.html .

    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.