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Predicting roof displacement of roadways in underground coal mines using adaptive neuro-fuzzy inference system optimized by various physics-based optimization algorithms

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Journal of Rock Mechanics and Geotechnical Engineering 2021年第6期13卷 1452-1465页

作者： Chengyu Xie Hoang Nguyen Xuan-Nam Bui Van-Thieu Nguyen Jian Zhou School of Environment and Resources Xiangtan UniversityXiangtan411105China Mining Faculty Hanoi University of Mining and GeologyHanoi100000Viet Nam Innovations for Sustainable and Responsible Mining(ISRM)Group Hanoi University of Mining and GeologyHanoi100000Viet Nam Artificial Intelligence Independent Research Group Hanoi100000Viet Nam School of Resources and Safety Engineering Central South UniversityChangsha410083China

Due to the rapid industrialization and the development of the economy in each country,the demand for energy is increasing *** coal mines have to pace up the mining operations with large production to meet the energy *** requirement has led underground coal mines to go deeper with more difficult conditions,especially the mining hazards,such as large deformations,rockburst,coal burst,roof collapse,to name a ***,this study aims at investigating and predicting the stability of the roadways in underground coal mines exploited by longwall mining method,using various novel intelligent techniques based on physics-based optimization algorithms(***-verse optimizer(MVO),equilibrium optimizer(EO),simulated annealing(SA),and Henry gas solubility optimization(HGSO)) and adaptive neuro-fuzzy inference system(ANFIS),named as MVO-ANFIS,EO-ANFIS,SA-ANFIS and HGSOANFIS ***,162 roof displacement events were investigated based on the characteristics of surrounding rocks,such as cohesion,Young’s modulus,density,shear strength,angle of internal friction,uniaxial compressive strength,quench durability index,rock mass rating,and tensile *** MVO-ANFIS,EO-ANFIS,SA-ANFIS and HGSO-ANFIS models were then developed and evaluated based on this dataset for predicting roof displacements in roadways of underground *** results indicated that the proposed intelligent techniques could accurately predict the roof displacements in roadways of underground mines with an accuracy in the range of 83%-92%.Remarkably,the SA-ANFIS model yielded the most dominant accuracy(i.e.92%).based on the accurate predictions from the proposed techniques,the reinforced solutions can be timely suggested to ensure the stability of roadways during exploiting coal,especially in the underground coal mines exploited by the longwall mining.

关键词： Roof displacement Longwall mining Underground mine physics-based optimization Risk assessment Mining hazards

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Performance Comparison of physics based Meta-Heuristic optimization Algorithms

Performance Comparison of Physics Based Meta-Heuristic Optim...

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International Conference on Artificial Intelligence and Data Processing (IDAP)

作者： Demirol, Doygun Oztemiz, Furkan Karci, Ali Inonu Univ Dept Comp Engn Malatya Turkey

ISBN: (纸本)9781538668788

The optimization process is a process that aims to find the minimum or maximum point according to the objective function. Many different algorithms have been developed for optimization problems. While analytical methods are committed to finding the exact solution specific to their problem, heuristic methods are committed to finding the best solution to the larger set of problems. Mathematical models of the system and the objective function are needed to solve the problems. General purpose heuristic optimization algorithms are evaluated in eight different groups including physics, biology, social, herd, music, chemistry, sports and mathematics. In this study, Be about Water Cycle Algorithm, Electromagnetic Field optimization, Big Bang Big Crunch, Gravitational Search Algorithm, Optics Inspired optimization, the performance results of 5 different algorithms were compared for Sphere, Rastrigin, Rosenbrock, Griewank and Ackley test functions. In consequence of the number of stated population, size, run and iteration, after the minimum, maximum, standard deviation, and their mean values were established, their superiority to each other was determined.

关键词： Water Cycle Algorithm Electromagnetic Field optimization Big Bang Big Crunch Gravitational Search Algorithm Optics Inspired optimization physics-based optimization

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Assessment of battery energy storage uses in microgrid operation under varied demand uncertainties through a machine learning based stochastic optimization approach

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PHYSICA SCRIPTA 2025年第3期100卷 036013-036013页

作者： Patty, Sukriti Malakar, Tanmoy Natl Inst Technol Dept Elect Engn Silchar 788010 Assam India

The sustainability of Microgrid (MG) relies on improving reliability through effective energy management strategies. However, the presence of volatile Renewable Energy Sources (RES) and uncertain energy demand pose serious challenges. Although integrating Battery Energy Storage Systems (BES) is a promising solution, the utilization of BES under uncertain operating conditions remains underexplored. Given this, the present study aims to investigate BES operational behavior while minimizing the overall MG operation cost under varied operating conditions. Also, it evaluates the impact of prediction accuracy on overall MG operation costs and DER operation. Here, a Machine Learning (ML) -based Gaussian Process Regression (GPR) is utilized for predicting the volatile RES power output while the uncertain energy demand is estimated with Monte Carlo Simulation (MCS).The problem is formulated as a cost-minimization problem and solved using the physics-based Artificial Electric Field Algorithm (AEFA) at different MG operating conditions. Results show that MG operation cost varies approximately by 5%, with demand variations around the mean, and BES energy contribution is 1.3 times higher for the case with additional thermal load. Moreover, analysis confirms that the BES depreciation cost is faster with a lower initial State of Charge (SOC), and the proposed strategy ensures a higher SOC by day's end. Additionally, GPR's applicability is validated through comparison with the Gated Recurrent Unit (GRU), revealing lower BES depreciation and MG operation costs due to higher prediction accuracy. Further, a comparative analysis with 2m Point Estimate Method is done to highlight the superiority and robustness of the proposed approach.

关键词： Battery storage Microgrid machine learning physics-based optimization renewable energy uncertainty

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Prediction of suspended sediment load in Sungai Semenyih using extreme learning machines and metaheuristic optimization approach

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JOURNAL OF ENVIRONMENTAL MANAGEMENT 2025年 380卷 124987页

作者： Saleh, Azlan Zulkifley, Mohd Asyraf Univ Kebangsaan Malaysia Fac Engn & Built Environm Dept Elect Elect & Syst Engn Bangi 43600 Selangor Malaysia Univ Sains Malaysia Sch Humanities GeoInformat Unit Geog Sect Gelugor 11800 Penang Malaysia

Suspended sediment load (SSL) refers to sediment particles, such as silt and clay, that are suspended in water. It plays a critical role in hydrology and water quality management, influencing factors such as water quality, river erosion, sedimentation rates, dam operations, and aquatic ecosystems. Therefore, accurate prediction and monitoring of SSL is crucial for efficient water resources and environmental management. The study aims to enhance SSL prediction performance by integrating Extreme Learning Machines (ELM) with metaheuristic optimizations. The selected metaheuristic optimizations, namely Particle Swarm optimization (PSO), Henry Gas Solubility optimization (HGSO), Electromagnetic Field optimization (EFO), and Nuclear Reaction optimization (NRO), are systematically employed in ELM models for predicting SSL in Sungai Semenyih, Selangor, for a period from 2015 to 2018. The results show that the ELM-HGSO model slightly better performs than the other optimized models, achieving a Willmott Index (WI) of 0.9986, a Confidence Index (CI) of 0.996, a Mean Absolute Percentage Error (MAPE) of 0.058, a Nash-Sutcliffe Efficiency (NSE) of 0.994, and a Root Mean Square Error (RMSE) of 4.082. The ELM-HGSO model improves NSE by approximately 10 % compared to the standalone ELM model. The Taylor diagram further confirms that ELM-HGSO best fits the observed data for both training and test datasets. This study concludes that ELM-HGSO is the most suitable model for SSL prediction in the study area. The findings are expected to advance SSL prediction research, contributing to improved water management and sustainable infrastructure development.

关键词： Suspended sediment load prediction Extreme learning machine physics-based optimization

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Multicontact Interaction Force Sensing From Whole-Body Motion Capture

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2018年第6期14卷 2343-2352页

作者： Tu-Hoa Pham Caron, Stephane Kheddar, Abderrahmane Univ Montpellier Montpellier Lab Informat Robot & Microelect Interact Digital Humans Grp UMR5506CNRS F-34090 Montpellier France CNRS AIST Joint Robot Lab RL UMI3218 Tsukuba Ibaraki 3058568 Japan Univ Montpellier Interact Digital Humans Grp Montpellier Lab Informat Robot & Microelect UMR5506CNRS F-34090 Montpellier France

We present a novel technique that unobtrusively estimates forces exerted by human participants in multicontact interaction with rigid environments. Our method uses motion capture only, thus circumventing the need to set up cumbersome force transducers at all potential contacts between the human body and the environment. This problem is particularly challenging, as the knowledge of a given motion only characterizes the resultant force, which can generally be caused by an infinity of force distributions over individual contacts. We collect and release a large-scale dataset on how humans instinctively regulate interaction forces on diverse multicontact tasks and motions. The force estimation framework we propose leverages physics-based optimization and neural networks to reconstruct force distributions that are physically realistic and compatible with real interaction force patterns. We show the effectiveness of our approach on various locomotion and multicontact scenarios.

关键词： Force sensing from motion capture multicontact neural networks physics-based optimization whole body

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Artificial electric field algorithm with inertia and repulsion for spherical minimum spanning tree

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APPLIED INTELLIGENCE 2022年第1期52卷 195-214页

作者： Bi, Jian Zhou, Yongquan Tang, Zhonghua Luo, Qifang Guangxi Univ Nationalities Coll Artificial Intelligence Nanning 530006 Peoples R China Guangxi High Sch Key Lab Complex Syst & Computat Nanning 530006 Peoples R China Guangxi Key Labs Hybrid Computat & IC Design Anal Nanning 530006 Peoples R China

Artificial electric field algorithm (AEFA) is a potential global optimization algorithm proposed in recent years and has been successfully applied to various engineering optimizations. However, precocious convergence tends to occur when solving complex engineering optimization problems. To avoid premature convergence to some extent, an artificial electric field algorithm with inertia and repulsion (IRAEFA) is proposed. The IRAEFA algorithm introduces the inertia mechanism and the repulsion between charges, expands the search space, increases the diversity of population, balances the exploration and development ability of the algorithm, and avoids the algorithm falling into the local optimal solution. Finally, the IRAEFA algorithm is used to solve the spherical mining spanning tree (MST) problem, and the results obtained are compared and analyzed with the results of other well-known metaheuristics optimization algorithms. Experimental results show that the proposed algorithm has better performance than other algorithms in solving spherical MST problems.

关键词： Artificial electric field algorithm Spherical MST Prü fer Spherical geometry physics-based optimization Metaheuristic

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An Improved Sine Cosine Algorithm for Solving optimization Problems

An Improved Sine Cosine Algorithm for Solving Optimization P...

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IEEE Conference on Systems, Process and Control (ICSPC)

作者： Suid, M. H. Ahmad, M. A. Ismail, M. R. T. R. Ghazali, M. R. Irawan, A. Tumari, M. Z. Univ Malaysia Pahang Fac Elect & Elect Engn Pekan Malaysia Univ Tekn Malaysia Melaka Fac Engn Technol Ayer Keroh Melaka Malaysia

ISBN: (纸本)9781538663271

Due to its simplicity and less tedious parameter tuning over other multi-agent-based optimization algorithms, Sine Cosine Algorithm (SCA) has gotten lots of attention from numerous researchers for resolving optimization problem. However, the existing SCA tends to have low optimization precision and local minima trapping effect due to the constraint in its exploration and exploitation mechanism. To overcome this drawback, an extensive version of SCA named Improved Sine Cosine Algorithm (iSCA) has been proposed in this work. The main concept is to introduce a nonlinear control strategy to the existing SCA's exploration and exploitation process in order to synthesize the algorithm's strength. The efficiency of this suggested algorithm is assessed using 23 classical well-known benchmark functions and the results are then verified by a comparative study with several other algorithms namely Ant Lion Optimizer (ALO), Multi-verse optimization (MVO), Spiral Dynamic optimization Algorithm (SDA) and Sine Cosine Algorithm (SCA). Experimental results show that the iSCA is very competitive compared to the stateof-the-art meta-heuristic algorithms.

关键词： optimization Meta-heuristic Algorithm physics-based optimization Sine Cosine Algorithm (SCA) Improved Sine Cosine Algorithm (iSCA)

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An Efficient Binary Equilibrium Optimizer Algorithm for Feature Selection

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IEEE ACCESS 2020年 8卷 140936-140963页

作者： Gao, Yuanyuan Zhou, Yongquan Luo, Qifang Guangxi Univ Nationalities Coll Informat Sci & Engn Nanning 530006 Peoples R China Key Labs Guangxi High Sch Complex Syst & Computat Nanning 530006 Peoples R China Guangxi Key Labs Hybrid Computat & IC Design Anal Nanning 530006 Peoples R China

Feature selection (FS) is a classic and challenging optimization task in the field of machine learning and data mining. An equilibrium optimizer (EO) is a novel physics-based optimization algorithm;it was inspired by controlled volume mass balance models for estimating dynamic and equilibrium states. This article presents two binary equilibrium optimizer algorithm and for selecting the optimal feature subset for classification problems. The first algorithm maps the continuous EO into a discrete type using S-shaped and V-shaped transfer functions (BEO-S and BEO-V). The second algorithm is based on the position of the current optimal solution (target) and position vector (BEO-T). To verify the performance of the proposed algorithm, 19 well-known UCI datasets are tested and compared with other advanced FS methods. The experimental results show that among the proposed binary EO algorithms, BEO-V2 has the best comprehensive performance and has better performance than other state-of-the-art metaheuristic algorithms in terms of the performance measures.

关键词： Binary equilibrium optimizer equilibrium optimizer feature selection physics-based optimization

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Novel knacks of chaotic maps with Archimedes optimization paradigm for nonlinear ARX model identification with key term separation

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CHAOS SOLITONS & FRACTALS 2023年第Part1期175卷

作者： Mehmood, Khizer Chaudhary, Naveed Ishtiaq Khan, Zeshan Aslam Cheema, Khalid Mehmood Raja, Muhammad Asif Zahoor Shu, Chi-Min Int Islamic Univ Dept Elect & Comp Engn Islamabad Pakistan Natl Yunlin Univ Sci & Technol Future Technol Res Ctr 123 Univ RdSect 3 Yunlin 64002 Taiwan Fatima Jinnah Women Univ Dept Elect Engn Rawalpindi 46000 Pakistan Natl Yunlin Univ Sci & Technol Dept Safety Hlth & Environm Engn 123 Univ RdSect 3 Yunlin 64002 Taiwan

Archimedes' optimization algorithm (AOA) is a recently proposed physics-based optimization algorithm inspired by Archimedes' Principle. However, for optimization of complex engineering problems, it shows slow convergence and can easily fall into local minima. Chaotic maps have non-repeatability properties they can effectively scan the search space with fast convergence in optimization algorithms. In this study, knacks of physics-based optimization along with chaotic maps are exploited for the parameter estimation of autoregressive exogenous (ARX), nonlinear input ARX (NI-ARX), LD-Didactic temperature process plant models and electrically stimulated muscle model. Ten chaotic maps have been applied to AOA. The fitness function of both models is constructed based on the square error between true and estimated response. The chaotic variants indicate that AOA with a chaotic Circle map (CAOA2) gives the best performance in terms of accuracy and convergence against reptile search heuristics, sine cosine optimization approach, harris hawks optimizer (HHO) and whale optimization algorithm (WOA) for different variations of generation and noise levels. Statistics based on mean square error, convergence plots, statistical plots, Taguchi test, average fitness, t-test, and Friedman test for repeated measures approve the reliability of CAOA2 for parameter estimation of ARX, NI-ARX, LD-Didactic temperature process plant models and electrically stimulated muscle model.

关键词： Nonlinear input models Chaotic maps System identification Archimedes' principle physics-based optimization

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New process optimization framework for laser assisted tape winding of composite pressure vessels: Controlling the unsteady bonding temperature

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MATERIALS & DESIGN 2020年 196卷

作者： Zaami, Amin Baran, Ismet Bor, Ton C. Akkerman, Remko Univ Twente Fac Engn Technol Chair Prod Technol NL-7500 AE Enschede Netherlands

This paper presents an effective process optimization methodology for laser assisted tape winding (LATW) of complex part geometries by means of a numerical optical-thermal model. A winding path on the cylindrical and ellipsoidal (dome) part of a pressure vessel is considered with varying tooling curvature. First, the process model output is verified with the literature data based on the laser intensity distribution. Then, the transient laser irradiation and temperature distributions on the tape and substrate are described comprehensively. It is shown that the maximum laser intensity increases approximately by 80% and the process (bonding) temperature changes by 80 degrees C at the intersection of the cylindrical and dome section of the pressure vessel. In order to keep the transient process temperature constant, a robust optimization scheme is utilized by means of a genetic algorithm. The design variable is determined as the total laser power and temperature constraints are defined. The proposed optimization methodology regulates the temperature within 1.5 degrees C variation with respect to the desired value. In order to compensate the transient local curvature effects on the process temperature, the total laser power varies approximately between 30% and 175% of the reference (non-optimized) case. (c) 2020 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http:// ***/licenses/by/4.0/).

关键词： Laser-assisted tape winding placement Process simulation physics-based optimization Curved lightweight products

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