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Gold rush optimizer: A new population-based metaheuristic algorithm

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  • 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
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    References listed on IDEAS

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    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.
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