Gold rush optimizer: A new population-based metaheuristic algorithm

IF 0.7 Q4 OPERATIONS RESEARCH & MANAGEMENT SCIENCE
Kamran Zolfi
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引用次数: 5

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 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.
淘金热优化器:一种新的基于人口的元启发式算法
当今世界的特点是竞争环境,优化资源利用和降低成本,这导致了元启发式算法在解决复杂的现代问题中的作用越来越大。因此,本文介绍了淘金热优化器(GRO),这是一种基于人口的元启发式算法,它模拟了淘金热时代淘金者如何利用三个关键的金矿找矿概念:迁移、协作和淘金。在29个基准测试用例上,将GRO算法与12种知名的元启发式算法进行了比较,以评估该方法的性能。为了进行科学评价,使用了Friedman和Wilcoxon符号秩检验。除了这些测试用例之外,GRO算法还使用三个现实世界的工程问题进行了评估。结果表明,该算法在定性求解和竞争性求解方面优于其他算法。
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来源期刊
Operations Research and Decisions
Operations Research and Decisions OPERATIONS RESEARCH & MANAGEMENT SCIENCE-
CiteScore
1.00
自引率
25.00%
发文量
16
审稿时长
15 weeks
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