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Metaheuristic optimization based- ensemble learners for the carbonation assessment of recycled aggregate concrete

APPLIED SOFT COMPUTING 2024年 159卷

作者： Golafshani, Emadaldin Mohammadi Behnood, Ali Kim, Taehwan Ngo, Tuan Kashani, Alireza Univ New South Wales Sch Civil & Environm Engn Sydney NSW 2052 Australia Univ Mississippi Dept Civil Engn University MS 38677 USA Univ Melbourne Infrastruct Engn Dept Melbourne Vic 3010 Australia

This study addresses the enhanced prevalence of carbonation, a process accelerating steel reinforcement corrosion in recycled aggregate concrete (RAC) compared to natural aggregate concrete. Traditional carbonation depth assessment methods in RAC are noted for being labor-intensive, costly, and requiring specialized expertise. There is a noted deficiency in the application of machine learning techniques for accurately predicting carbonation depth in RAC, a gap this study aims to fill. Utilizing the extreme gradient boosting (XGBoost) technique, recognized for its efficacy in ensemble machine learning, this study innovates in modeling carbonation depth in RAC. It emphasizes the criticality of hyperparameter optimization of the XGBoost algorithm for maximizing model accuracy. To achieve this, three novel metaheuristic optimization algorithms, including reptile search algorithm (RSA), Aquila optimizer (AO), and arithmetic optimization algorithm (AOA), were introduced as global optimizers for tunning the XGBoost hyperparameters. The study was underpinned by a comprehensive database compiled from extensive literature, facilitating the development of an accurate RAC carbonation depth model. Through rigorous evaluations, including sensitivity analyses, the Wilcoxon signed-rank test, and runtime comparisons, the synthesized models demonstrated exceptional accuracy, with coefficients of determination exceeding 0.95. The XGBoost-AO algorithm, in particular, showcased superior performance, with the XGBoost-RSA algorithm providing efficient predictions considering runtime. SHapley Additive exPlanations (SHAP) interpretation highlighted environmental conditions as significant carbonation depth influencers. A userfriendly graphical user interface was developed, enhancing the practical utility of the findings for predicting carbonation depth progression in RAC over time. This research significantly advances the predictive accuracy for carbonation depth in RAC, contributing to the

关键词： Recycled aggregate concrete Carbonation depth XGboost ensemble technique Equilibrium optimizer arithmetic optimization algorithm Reptile search algorithm

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FHAWS: fuzzy hybrid arithmetic war strategy for parametric optimization of toughened glass on toughening machine

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MULTIMEDIA TOOLS AND APPLICATIONS 2023年第20期83卷 58421-58447页

作者： Dohare, Sunil Rajput, R. S. Rajiv Gandhi Proudyogiki Vishwavidyalaya Univ Inst Technol Dept Mech Engn Bhopal India

In recent years, toughened glass has become widely used in architectural applications due to its high structural strength, shatter resistance, soundproofing, heat resistance, and durability. However, despite its strong capabilities, toughened glass can explode completely when subjected to heavy impact, causing security-related constraints in areas prone to smash-and-grab attempts. This issue can only be prevented by enhancing the quality of toughened glass, particularly its tensile strength, compressive strength, and durability. To achieve this goal, a novel technique called the "fuzzy hybrid arithmetic war strategy" (FHAWS) approach is proposed in this paper. The approach utilizes the "hybrid arithmetic war strategy" (HAWS) algorithm to determine the optimal solution with a faster convergence rate. The HAWS algorithm integrates the standard arithmetic optimization (AO) algorithm and War Strategy optimization (WSO) algorithm. The fuzzy rule concept is introduced for regulating the improper searching algorithm. The proposed FHAWS approach optimizes toughened glass characteristics such as splitting strength, flexural strength, tensile strength, compressive strength, stress, temperature, fragmentation, heating time, cooling time, visual light, durability, and production cost. The experimental analysis illustrates that the proposed FHAWS approach is more efficient and robust in enhancing the quality of toughened glass than other compared state-of-the-art approaches. Overall, the paper proposes a novel approach to optimize the characteristics of toughened glass, addressing a critical issue in the architectural industry. However, the specific details of the approach and experimental results should be examined in the paper to assess the method's effectiveness comprehensively.

关键词： Toughened glass arithmetic optimization algorithm War strategy optimization algorithm Fuzzy rules Tensile strength Compressive strength Durability

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Evaluation of compressive strength of concrete modified with admixtures using arithmetic optimization-based estimation algorithms

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MULTISCALE AND MULTIDISCIPLINARY MODELING EXPERIMENTS AND DESIGN 2024年第6期7卷 5097-5114页

作者： Zhao, Yaxuan Lanzhou Ecol Innovat City Dev Grp Co Ltd Lanzhou 730199 Peoples R China

In the present study, various hybrid models are applied for developing estimation techniques to forecast the compressive strength (CS\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$CS$$\end{document}) of concretes, including metakaolin (MK\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$MK$$\end{document}) and fly ash (FA\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$FA$$\end{document}). These models can be valuable for the construction and concrete industry by providing a means to estimate CS\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$CS$$\end{document} under different conditions and ages of concrete. The models developed in this study can have practical applications in the concrete industry, helping engineers, architects, and construction professionals to make informed decisions regarding concrete mix designs, strength assessments, and aging effects. For this purpose, four various algorithms have been considered, named multilayer perceptron (MLP\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$MLP$$\end{document}), radial basis function (RBF\documentclass[12pt]{minimal}

关键词： Compressive strength Metakaolin Fly ash Prediction arithmetic optimization algorithm Sensitivity analysis

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Support Vector Machine to Predict the Pile Settlement using Novel optimization algorithm

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GEOTECHNICAL AND GEOLOGICAL ENGINEERING 2023年第7期41卷 3861-3875页

作者： Ge, Qingyun Li, Caimei Yang, Fulian West Anhui Univ Luan Anhui Peoples R China Gates Winhere Automobile Water Pump Prod Yantai Co Yantai 712000 Shandong Peoples R China

Project Immunization, like piled construction, requires considerations that make them safe during the period of operation. Pile Settlement (PS), a vital issue in projects, has attracted many regards to avoid failure before commencing employing constructions. Several factors in appraising the pile movement can assist in understanding the future of the project in the loading stage. Many intelligent strategies to mathematically compute the pile motion are employed to simulate the PS. The present study aims to use Support vector regression (SVR) to predict the settlement of piles. In addition, to improve the accuracy of the related model, two meta-heuristic algorithms have been used, including the arithmetic optimization algorithm (AOA) and Grasshopper optimization algorithm (GOA), a hybrid format in the framework of SVR-AOA and SVR-GOA. Kuala Lumpur transportation network was chosen to investigate the pile motion according to the ground properties' condition with SVR-AOA and SVR-GOA developed frameworks. For the evaluation of each model's performance, five indices were employed. That, the values of RMSEs for SVR-AOA and SVR-GOA were obtained at 0.550 and 0.592, respectively, and MAE exhibited the values of 0.525 and 0.561 alternatively. The R-value for the SVR-AOA showed a desirable magnitude of 0.994, which is 0.10% higher than the SVR-GOA. Also, OBJ, including R, RMSE, and MAE, for SVR-GOA and SVR-AOA were computed at 0.541 and 0.586 mm, respectively. Models' results have had a similar performance to estimating the PS rate.

关键词： Pile settlement Support vector regression Grasshopper optimization algorithm arithmetic optimization algorithm RMSE

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Employing the optimization algorithms with machine learning framework to estimate the compressive strength of ultra-high-performance concrete (UHPC)

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MULTISCALE AND MULTIDISCIPLINARY MODELING EXPERIMENTS AND DESIGN 2024年第1期7卷 97-108页

作者： Zhang, Yajing An, Sai Liu, Hao Hebei Normal Univ Sci & Technol Coll Urban Construction Qinhuangdao 066004 Peoples R China

Ultra-high-performance concrete (UHPC), with the highest resistance capacity against axial loads, is composed of various ingredients compared to other typical concretes, including fly ash and silica fume;eco-friendly materials also have an economical price. Such high-resistant constructional materials are seen as a suitable aggregate to use in most practical projects. The compressive strength (CS) of concrete, as one of the important variables in engineering fields, can be estimated using smart approaches based on ingredients as inputs fed to the mathematical model. Consequently, the current study modeled the CS values using a machine learning technique of Support vector regression (SVR) accompanied by the grasshopper optimization algorithm (GOA) and arithmetic optimization algorithm (AOA), tunning the SVR to appraise the CS accurately. In developing AOA-SVR and GOA-SVR frameworks, several metrics were used to assess the ability and accuracy of models. As a result of the present study, it can be concluded that the machine learning method in hybrid form has the ability to predict for saving time and energy. In general, in comparing the results of hybrid models, AOA had the most suitable combination with SVR compared to GOA, with R-2 = 0.901 and RMSE = 9.986 in the training phase. In addition, AOA-SVR improved its performance by R-2 = 0.917 and RMSE = 9.525 in the test phase.

关键词： Ultra-high-performance concrete arithmetic optimization algorithm Support vector regression Grasshopper optimization algorithm Compressive strength

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optimization of Meixner Parameters Using the AOA algorithm for Signal and Image Analysis 4th

Optimization of Meixner Parameters Using the AOA Algorithm f...

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4th International Conference on Digital Technologies and Applications (ICDTA)

作者： Sayyouri, Adil Bencherqui, Ahmed Sayyouri, Mhamed Hafid, Abdelatif Bourkan, Abderrahim Cherkaoui, Abdeljabbar Abdelmalek Essaadi Univ Natl Sch Appl Sci Lab Innovat Technol LIT Tangier Morocco Sidi Mohamed Ben Abdellah Univ Natl Sch Appl Sci Engn Syst & Applicat Lab Fes Morocco Higher Sch Engn Appl Sci ESISA Analyt Lab Fes Morocco

ISBN: (纸本)9783031686597;9783031686603

In this paper, we introduce a novel approach to solving the problem of choosing the parameters of Meixner moments, recognized as robust descriptors for signal and image analysis. Our method is based on the application of the arithmetic optimization algorithm (AOA) to refine the local parameter values of the Meixner polynomials for optimal performance when calculating the corresponding moments. Our algorithm is evaluated on a set of signals of significant dimensions based on quantitative criteria such as mean square error and maximum signal-to-noise ratio. Our simulation results highlight the efficiency of the proposed algorithm, both in signal analysis and in the analysis of two- and three-dimensional images, compared to conventional methods. Therefore, this approach shows great potential in enhancing the precision and efficiency of extracting information from intricate data, thereby introducing fresh opportunities in the realm of signal and image analysis.

关键词： Meixner polynomials Meixner moments Signal reconstruction Signal compression arithmetic optimization algorithm

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A Hybrid Technique for EV Parking Lot optimization with Improved Power Quality 5

A Hybrid Technique for EV Parking Lot Optimization with Impr...

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5th IEEE Global Power, Energy and Communication Conference (GPECOM)

作者： Rao, Ch. S. V. Prasad Pandian, A. Reddy, Ch. Rami Bajaj, Mohit Jurado, Francisco Kamel, Salah Koneru Lakshmaiah Educ Fdn Elect & Elect Engn Guntur 522302 Andhra Pradesh India Joginpally BR Engn Coll Elect & Elect Engn Hyderabad 500075 India Graph Era Dept Elect Engn Dehra Dun 248002 Uttarakhand India Graph Era Hill Univ Dept Elect Engn Dehra Dun 248002 Uttarakhand India Univ Jaen Dept Elect Engn Jaen Spain Aswan Univ Fac Engn Dept Elect Engn Aswan Egypt

ISBN: (纸本)9798350301984

This paper presents a hybrid method for optimally locating the EV parking lot (PL) and improving power quality. This advanced hybrid method is the combined operation of Atomic orbital search (AOS) and arithmetic optimization algorithm (AOA) and is known as the Atomic orbital search arithmetic optimization algorithm AOSAOA method. This research shows that allotment of Electric Vehicle (EV) parking lot and capacitors are initiated to compensate for reactive power. This AOSAOA method was developed to optimize the parking lot size. The perfect location of the capacitor, calculation of the reactive and actual power losses, EVs parking lot under the grid and voltage profile is optimally obtained with proper control. The execution of this new AOSAOA method is done with MATLAB/Simulink and the efficiency of this new method is compared with present methods.

关键词： Electric vehicle parking lot power loss voltage arithmetic optimization algorithm Atomic orbital search

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Efficient latency-and-energy-aware IoT-fog-cloud task orchestration: novel algorithmic approach with enhanced arithmetic optimization and pattern search

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International Journal of Information Technology (Singapore) 2024年第5期16卷 3311-3324页

作者： Srichandan, Suresh Kumar Majhi, Santosh Kumar Jena, Sudarson Mishra, Kaushik Rao, D. Chandrasekhar Sambalpur University Institute of Information Technology Sambalpur University Jyotivihar Odisha Burla 768019 India Veer Surendra Sai University of Technology Odisha Burla 768018 India Department of Computer Science and Information Technology Guru Ghasidas Vishwavidyalaya Koni C.G. Bilaspur 495009 India Gandhi Institute of Technology and Management Andhra Pradesh Visakhapatnam 530045 India

Nowadays, the load on datacenters has become more and more due to the unprecedented growth of diversified data from many IoT devices;hence, resource utilization has become more difficult. So, Cloud computing emerged as an appealing solution to leverage IoT devices' computational, memory, capacity and resource constraints. However, the physical gap between IoT devices and the Cloud datacentre resulted in latency overhead and incurred high energy consumption due to the centralized nature of datacenter. Thus, Fog computing is introduced as a complementary to Cloud computing to minimize the physical gap and thus, latency by bringing the computations closer to the end devices. However, the placement of tasks to a particular Fog node (FN)/cloud virtual machine (VM) is an NP-hard problem with much higher computational time. Thus, metaheuristic algorithms are a way out as they have a polynomial time complexity. The arithmetic optimization algorithm (AOA) along with the Pattern search algorithm is one of the optimization algorithms, implemented in order to minimise the makespan and maximise resource utilization. This paper proposes an improved AOA when combined with a pattern search algorithm helps in migrating the tasks from an overloaded FN to an underloaded FN more effectively. Finally, the proposed algorithm is compared with similar optimization algorithms on various parameters. From the simulations, it is evident that the proposed AOA-PS-based task scheduling approach outperformed others with a percentage of improvement of 24.2%, 18.6%, 16%, 5.4%, and 23.4% for makespan, resource utilization, response time, energy consumption, and degree of imbalance, respectively. © The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of Computer Applications and Management 2024.

关键词： arithmetic optimization algorithm Cloud computing Fog computing IoT Pattern search algorithm Resource management Task orchestration

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Utilizing optimization Techniques in Feature Selection for Effective Cardiovascular Disease Prediction 24

Utilizing Optimization Techniques in Feature Selection for E...

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24th International Arab Conference on Information Technology, ACIT 2023

作者： Al Redhaei, Aneesa Fares, Wafa'Abu Al Betar, Mohammed Azmi College of Engineering and Information Technology Ajman University Ajman United Arab Emirates

ISBN: (纸本)9798350384307

Cardiovascular disease (CVD) is considered one of the significant global health challenges, affecting individuals and societies worldwide. This paper aims to address the challenge of predicting CVD by developing a prediction model based on optimal feature selection. The process begins with data preprocessing of the Z-Alizadeh Sani dataset. Three optimization algorithms were utilized: Genetic algorithm (GA), Particle Swarm optimization (PSO), and arithmetic optimization algorithm (AOA) to find the most important and relevant features to improve the heart disease prediction accuracy of some selected popular machine learning classifiers. Simulation results showed that the proposed work achieves 98.59 % of accuracy. © 2023 IEEE.

关键词： arithmetic optimization algorithm Feature Selection Genetic algorithm Machine Learning Particle Swarm optimization

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Impact analysis of capacitive energy storage integration on load frequency control performance of microgrids employing a new dual-stage controller

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ISA transactions 2025年

作者： Bhuvnesh Khokhar Krishna Pal Singh Parmar Department of Electrical Engineering Galgotias College of Engineering and Technology (GCET) Gautam Buddha Nagar Uttar Pradesh India. Electronic address: bhuvnesh.khokhar@galgotiacollege.edu. National Power Training Institute Ministry of Power (Govt. of India) Faridabad Haryana India. Electronic address: kpsparmar.npti@gov.in.

A microgrid (MG) with inherent low inertia and renewable energy sources (RES)-based generators may encounter difficulties in maintaining frequency stability due to load changes. In order to mitigate frequency stability concerns, it is imperative to use energy storage units (ESUs). Objective of this study is to analyze the impact of capacitive energy storage unit (CESU) integration on the load frequency control (LFC) performance of two distinct MG systems. A new dual-stage PI-(1+PDF) controller is proposed as a secondary controller for the LFC study. Parameters of the controller are optimized employing a recently developed arithmetic optimization algorithm (AOA). Performance of the suggested control approach is evaluated against several standard control approaches. Simulation findings demonstrate that with CESU integration the suggested controller markedly enhances frequency and tie-line power deviation responses and their transient characteristics for both the MG systems. A maximum improvement of 98.50% in peak overshoot, 94.88% in peak undershoot, and 77.18% in settling time is observed with the suggested control approach. This study further justifies the robustness of the proposed control approach against the MG systems parameter variations, impact of CESU integration through statistical analysis, small signal stability of both the MG systems, and convergence performance of the AOA.

关键词： arithmetic optimization algorithm Capacitive energy storage unit Dual-stage PI(λ) − (1+PD(μ)F) controller Load frequency control Microgrid

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