Gold rush optimizer : a new population-based metaheuristic algorithm

• Autorzy: Zolfi Kamran

Identyfikatory

DOI

10.37190/ord230108

• Języki publikacji: EN

Abstrakty

EN

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 proposed 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.

Słowa kluczowe

EN

gold rush optimizer; metaheuristic; global optimization; population-based algorithm

• Wydawca: Oficyna Wydawnicza Politechniki Wrocławskiej
• Czasopismo: Operations Research and Decisions
• Rocznik: 2023
• Tom: Vol. 33, No. 1
• Strony: 113--150
• Opis fizyczny: Bibliogr. 80 poz., rys.

Twórcy

autor

Zolfi Kamran

• Department of Industrial Engineering, Lenjan Branch, Islamic Azad University, Isfahan, Iran

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).