Artificial gorilla troops optimizer: A new nature-inspired metaheuristic algorithm for global optimization problems
Artificial gorilla troops optimizer: A new nature-inspired metaheuristic algorithm for global optimization problems
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DOI：
10.1002/int.22535
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发表时间：
2021-07-12
影响因子：
7
通讯作者：
Mirjalili, Seyedali
中科院分区：
文献类型：
--
作者：
Abdollahzadeh, Benyamin;Gharehchopogh, Farhad Soleimanian;Mirjalili, Seyedali
元分析在解决优化问题中起着至关重要的作用，其中大多数都受到自然界中自然生物的集体智慧的启发。本文提出了一种新的启发式算法，称为人工大猩猩部队优化（GTO）。在这个算法中，大猩猩的集体生活是数学公式化的，并设计了新的机制来执行探索和开发。为了评估GTO，我们将其应用于52个标准基准函数和7个工程问题。Friedman检验和Wilcoxon秩和统计检验在统计上比较了所提出的方法与几个现有的元分析。结果表明，GTO的性能优于比较算法在大多数基准函数，特别是在高维问题。结果表明，GTO可以提供上级的结果相比，其他metalchemistics。
Metaheuristics play a critical role in solving optimization problems, and most of them have been inspired by the collective intelligence of natural organisms in nature. This paper proposes a new metaheuristic algorithm inspired by gorilla troops' social intelligence in nature, called Artificial Gorilla Troops Optimizer (GTO). In this algorithm, gorillas' collective life is mathematically formulated, and new mechanisms are designed to perform exploration and exploitation. To evaluate the GTO, we apply it to 52 standard benchmark functions and seven engineering problems. Friedman's test and Wilcoxon rank-sum statistical tests statistically compared the proposed method with several existing metaheuristics. The results demonstrate that the GTO performs better than comparative algorithms on most benchmark functions, particularly on high-dimensional problems. The results demonstrate that the GTO can provide superior results compared with other metaheuristics.