Gold rush optimizer: A new population-based metaheuristic algorithm
Kamran Zolfi ()
Operations Research and Decisions, 2023, vol. 33, issue 1, 113-150
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.
Keywords: gold rush optimizer; metaheuristic; global optimization; population-based algorithm (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:wut:journl:v:33:y:2023:i:1:p:113-150:id:8
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