Localization of Acoustic Emission Source in Rock Using SMIGWO Algorithm
Jiong Wei1
Fuqiang Gao2
Jinfu Lou2
Lei Yang3
Xiaoqing Wang2
1.Coal Mining Research Institute,China Coal Technology and Engineering Group Co.,Ltd.,Beijing 100013,China;State Key Laboratory of Intelligent Coal Mining and Strata Control,Beijing 100013,China;State Key Laboratory of Hydraulics and Mountain River Engineering,Sichuan University,Chengdu 610065,China2.Coal Mining Research Institute,China Coal Technology and Engineering Group Co.,Ltd.,Beijing 100013,China;State Key Laboratory of Intelligent Coal Mining and Strata Control,Beijing 100013,China3.Coal Mining Research Institute,China Coal Technology and Engineering Group Co.,Ltd.,Beijing 100013,China;State Key Laboratory of Intelligent Coal Mining and Strata Control,Beijing 100013,China;School of Energy and Mining Engineering,China University of Mining and Technology(Beijing),Beijing 100083,China
摘要：The Grey Wolf Optimization(GWO)algorithm is acknowledged as an effective method for rock acoustic emission localiza-tion.However,the conventional GWO algorithm encounters challenges related to solution accuracy and convergence speed.To address these concerns,this paper develops a Simplex Improved Grey Wolf Optimizer(SMIGWO)algorithm.The randomly generating initial populations are replaced with the iterative chaotic sequences.The search process is optimized using the convergence factor optimization algorithm based on the inverse incomplete Γ function.The simplex method is utilized to address issues related to poorly positioned grey wolves.Experimental results demonstrate that,compared to the conventional GWO algorithm-based AE localization algorithm,the proposed algorithm achieves a higher solution accuracy and showcases a shorter search time.Additionally,the algorithm demonstrates fewer convergence steps,indicating superior convergence efficiency.These findings highlight that the proposed SMIGWO algorithm offers enhanced solution accuracy,stability,and optimization performance.The benefits of the SMIGWO algorithm extend universally across various materials,such as aluminum,granite,and sandstone,showcasing consistent effectiveness irrespective of material type.Consequently,this algorithm emerges as a highly effective tool for identifying acoustic emission signals and improving the precision of rock acoustic emission localization.
机标关键词：localizationacousticalgorithmemissionsourcerocksmigwousing
论文发表日期：2025-04-30
在线出版日期：2025-10-15（本平台首次上网日期，不代表文献的发表时间）
页数：10( 42-51 )
英文信息