淘金热优化器:一种新的基于人口的元启发式算法

IF 0.7 Q4 OPERATIONS RESEARCH & MANAGEMENT SCIENCE Operations Research and Decisions Pub Date : 2023-01-01 DOI:10.37190/ord230108
Kamran Zolfi
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引用次数: 5

摘要

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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Gold rush optimizer: A new population-based metaheuristic algorithm
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.
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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
期刊最新文献
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