Multi-strategy Grey Wolf Optimizer for Engineering Problems and Sewage Treatment Prediction

IF 6.8 Q1 AUTOMATION & CONTROL SYSTEMS Advanced intelligent systems (Weinheim an der Bergstrasse, Germany) Pub Date : 2024-06-02 DOI:10.1002/aisy.202300406
Chenhua Tang, Changcheng Huang, Yi Chen, Ali Asghar Heidari, Shuihua Wang, Huiling Chen, Yudong Zhang
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Abstract

Grey wolf optimizer (GWO) is a highly valued heuristic algorithm in many fields. However, for some complex problems, especially high-dimensional and multimodal problems, the basic algorithm has limited computational power and cannot get a satisfactory answer. In order to find a better solution, an improved algorithm based on GWO is proposed herein. Gaussian barebone, random selection and chaotic game mechanisms are introduced into the GWO algorithm to enhance the global search ability. The GWO enhanced by three mechanisms is called CBRGWO. To verify the performance of CBRGWO, using IEEE CEC 2017 as a test function, CBRGWO is compared to five GWO variants, five basic algorithms, six advanced algorithms, and four champion algorithms. CBRGWO is evaluated using the Friedman test and Wilcoxon signed-rank test. Then, the stability of CBRGWO is analyzed. To verify that CBRGWO is still effective in practical application, CBRGWO is applied to five engineering problems and a water quality prediction problem. The experimental findings indicate that CBRGWO maintains excellent optimization ability in practical engineering problems.

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工程问题和污水处理预测的多策略灰狼优化器
灰狼优化算法(GWO)是许多领域中备受推崇的启发式算法。然而,对于一些复杂问题,尤其是高维和多模态问题,基本算法的计算能力有限,无法得到满意的答案。为了找到更好的解决方案,本文提出了一种基于 GWO 的改进算法。在 GWO 算法中引入了高斯裸骨、随机选择和混沌博弈机制,以增强全局搜索能力。由三种机制增强的 GWO 被称为 CBRGWO。为了验证 CBRGWO 的性能,以 IEEE CEC 2017 为测试函数,CBRGWO 与五种 GWO 变种、五种基本算法、六种高级算法和四种冠军算法进行了比较。CBRGWO 采用 Friedman 检验和 Wilcoxon 符号秩检验进行评估。然后,分析了 CBRGWO 的稳定性。为了验证 CBRGWO 在实际应用中的有效性,将 CBRGWO 应用于五个工程问题和一个水质预测问题。实验结果表明,CBRGWO 在实际工程问题中保持了出色的优化能力。
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0.00%
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审稿时长
4 weeks
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