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Novel hybrid AOA and ALO optimized supervised machine learning approaches to predict the compressive strength of admixed concrete containing fly ash and micro-silica

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MULTISCALE AND MULTIDISCIPLINARY MODELING EXPERIMENTS AND DESIGN 2022年第4期5卷 391-402页

作者： Zhu, Weixiang Huang, Lihua Zhang, Zhijun Zhejiang Guangsha Vocat & Tech Univ Construct Sch Civil Engn Architecture Dongyang 322100 Peoples R China Zhejiang Guangsha Vocat & Tech Univ Construct Sch Management Engn Dongyang 322100 Peoples R China Southwest China Architectural Design & Res Inst C Chengdu 610042 Peoples R China

Detecting the impact of admixtures like fly ash and micro-silica on the mechanical property of concrete, especially the compressive strength (CS), earned a lot of attention not only in the concrete industry but also in future extended research and analysis. In this study, two innovative methods of hybrid support vector regression optimized by arithmetic optimization algorithm and Antlion optimization algorithm named AOSVR and ALSVR were developed to generate accurately a trustable relationship between the feeding input (eight ingredients) of the model and the target values that optimizers by finding key variables of SVR lead to model precisely. These models applied to perform a prediction process of CS values for 170 High-Performance Concrete (HPC) samples. It can be concluded that the coefficient of determination values showed 0.9872 and 0.9850 for AOSVR and ALSVR, respectively. Moreover, the hybrid AOSVR model outperformed the most premier accuracy in the prediction of CS. Also, using these hybrid models helps diminish the cost of concrete testing and further the analysis of concrete mechanical characterization.

关键词： High-performance concrete Micro-silica arithmetic optimization algorithm Antlion optimization algorithm

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Fractional Order PD(1+ PI) Controller for Frequency Control of Power System with Renewable Sources and Electric Vehicle

ELECTRICA

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ELECTRICA 2024年第2期24卷 406-424页

作者： Baliarsingh, Asini Kumar Mohapatra, Sangram Keshori Dash, Pabitra Mohan Govt Coll Engn Dept Elect Engn Keonjhar Odisha India BEC Dept Elect Engn Burla Odisha India

This article introduces hybrid arithmetic optimization algorithm (AOA) and Local Unimodal Sampling (hAOA-LUS)-based fractional order (FO) proportional derivative (PD) cascaded with one plus proportional integral (1 + PI) controller for automatic generation control of power system with renewable energy sources (RES) and electric vehicle (EV). The control -area 1 has thermal, hydro, gas, and wind power generators and the same true for control area 2, which uses thermal, hydro, gas, and solar energy sources. Initially, Propo rtion al-In tegra l-Der ivati ve (PID) controllers are taken into consideration and it has been demonstrated that hAOA-LUS outperforms as compared to the AOA, Particle Swarm optimization, and Generic algorithm (GA). The assessing of overshoots, undershoots, and different integral errors of frequency and tie -line power deviations after step load perturbations in each area allows for performance comparison of the proposed power system. In the next stage, EVs are considered in each area and the controller parameters are optimized by hAOA-LUS techniques in the presence of RES and EV. A comparative analysis is carried out by hAOA-LUS-tuned FO PD(1+PI) controller with PID as well integer ordered PD(1+PI) for various cases so as to validate the superiority of the anticipated controller. The results from MATLAB and OPAL-RT are compared in order to verify the authenticate feasibility of method.

关键词： arithmetic optimization algorithm automatic generation control fractional order (FO) local unimodal sampling PD(1+ PI) controller

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Comparative Analysis of Sliding Mode Controllers for Trajectory Tracking and Vibration Reduction in a Two-Link Flexible Manipulator with Different Sliding Surfaces

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2024年第12期49卷 16711-16728页

作者： Thakur, Sanjay Barai, Ranjit Kumar Bhattacharya, Anagha Ghosh, Anumoy Jadavpur Univ Dept Elect Engn Kolkata India NIT Mizoram Dept Elect Engn Aizawl India NIT Mizoram Dept Elect & Commun Engn Aizawl India

The sliding mode controller has been designed for joint position tracking and link vibration reduction of the two-link flexible manipulator. In this work, to track the desired trajectory, sliding mode controller has been developed with two different types of sliding surfaces, to show the effect of sliding surfaces on the system's performance. Sliding surface plays an important role in the stability and control of the system. The sliding surface also acts as a dynamic equilibrium surface where the system states converge to track the desired behavior with minimum error. In this work, the PID sliding surface and PD sliding surface have been considered. A comparative analysis has been presented. The proportional, integral, and derivative gains for each sliding surface have been tuned using optimization techniques. arithmetic optimization algorithm and FOX optimization algorithm have been used here. In this work, mass of the payload has been considered an uncertain parameter. To demonstrate the efficiency of the suggested controllers, the value of the uncertain parameter has been altered.

关键词： arithmetic optimization algorithm FOX optimization algorithm Sliding mode controller Sliding surface

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A Novel Long Short-Term Memory Seq2Seq Model with Chaos-Based optimization and Attention Mechanism for Enhanced Dam Deformation Prediction

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BUILDINGS 2024年第11期14卷 3675-3675页

作者： Wang, Lei Wang, Jiajun Tong, Dawei Wang, Xiaoling Tianjin Univ State Key Lab Hydraul Engn Intelligent Construct & Tianjin 300354 Peoples R China

The accurate prediction of dam deformation is essential for ensuring safe and efficient dam operation and risk management. However, the nonlinear relationships between deformation and time-varying environmental factors pose significant challenges, often limiting the accuracy of conventional and deep learning models. To address these issues, this study aimed to improve the predictive accuracy and interpretability in dam deformation modeling by proposing a novel LSTM seq2seq model that integrates a chaos-based arithmetic optimization algorithm (AOA) and an attention mechanism. The AOA optimizes the model's learnable parameters by utilizing the distribution patterns of four mathematical operators, further enhanced by logistic and cubic mappings, to avoid local optima. The attention mechanism, placed between the encoder and decoder networks, dynamically quantifies the impact of influencing factors on deformation, enabling the model to focus on the most relevant information. This approach was applied to an earth-rock dam, achieving superior predictive performance with RMSE, MAE, and MAPE values of 0.695 mm, 0.301 mm, and 0.156%, respectively, outperforming conventional machine learning and deep learning models. The attention weights provide insights into the contributions of each factor, enhancing interpretability. This model holds potential for real-time deformation monitoring and predictive maintenance, contributing to the safety and resilience of dam infrastructure.

关键词： dam deformation prediction long short-term memory sequence-to-sequence model attention mechanism arithmetic optimization algorithm chaotic optimization

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An improved algorithm optimization algorithm based on RungeKutta and golden sine strategy

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EXPERT SYSTEMS WITH APPLICATIONS 2024年 247卷

作者： Li, Mingying Liu, Zhilei Song, Hongxiang Dalian Polytech Univ Sch Mech Engn & Automat Dalian 116034 Peoples R China

To overcome the shortcomings of the algorithm optimization algorithm (AOA), such as its slow convergence speed and poor global search ability, an improved AOA based on RungeKutta and golden sine strategy (RGAOA) is proposed. In this algorithm, the improved r1 based on the sine factor is proposed and compared with the math optimizer accelerated (MOA) values for each iteration. In this way the weighting of the exploration phase and the exploitation phase of the optimization process is reconstructed. Then, the gold sine strategy is used to guide individuals to approach the optimal solutions. After obtaining the current optimal solution, the quality of the current optimal solution is further enhanced by the Enhanced Solution Quality (ESQ) of the RungeKutta optimizer (RUN). Then, twenty benchmark test functions, the CEC2017, CEC2019 test functions (2017 and 2019 IEEE Congress on Evolutionary Computation test functions) and the practical engineering application problems were selected to test the overall performance of the improved algorithm, and the results were compared with other algorithms and other improved versions. The experimental results show an 89.19% improvement in convergence speed, a 90.07% improvement in convergence accuracy and a 67.99% improvement in stability compared to AOA.

关键词： arithmetic optimization algorithm RungeKutta Golden sine Sine factor Meta-heuristic algorithm

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A multi-level thresholding image segmentation method using hybrid arithmetic optimization and Harris Hawks Optimizer algorithms

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EXPERT SYSTEMS WITH APPLICATIONS 2024年 241卷

作者： Qiao, Li Liu, Kai Xue, Yanfeng Tang, Weidong Salehnia, Taybeh Lyuliang Univ Dept Comp & Sci Technol Lyuliang 033000 Shanxi Peoples R China Shanxi Univ Sch Comp & Informat Technol Taiyuan 030006 Shanxi Peoples R China Guangxi Minzu Univ Coll Artificial Intelligence Nanning 530006 Guangxi Peoples R China Razi Univ Dept Comp Engn & Informat Technol Kermanshah Iran Univ Isfahan Fac Comp Engn Esfahan Iran

Today, image segmentation methods are widely used for various applications, including object detection. Multilevel Thresholding Image Segmentation (MTIS) methods are among the efficient methods for image seg-mentation. In MTIS methods, it is very important to find the thresholds that gives the best performance for the MTIS and better separate and detect the objects on the image from the image background. Meta-Heuristic (MH) algorithms are among the strategies that can achieve good results in obtaining optimal thresholds to solve this problem. In this paper, we use the combination of arithmetic optimization algorithm (AOA) and Harris Hawks Optimizer (HHO) to improve AOA in exploitation phase, and achieve an optimal threshold vector for MTIS. Therefore, our new hybrid AOA-HHO algorithm solves the MTIS problem with better quality than both AOA and HHO algorithms and some other MH algorithms, and can obtain better thresholds that increase the performance of the MTIS system than AOA and HHO. AOA is powerful in the exploration, and HHO in exploitation phase is powerful. Therefore, AOA-HHO uses the features of both algorithms to search the entire search space locally and globally to find the best find the solution, the high power of the AOA exploration phase, and the high power of the HHO exploitation phase. Also, we use a mathematical equation as the fitness function, that is obtained by using image features. A series of experiments were performed using seven different threshold levels on the test images. Experiments show that AOA-HHO method is better than the compared algorithms and even HHO and AOA in terms of image segmentation accuracy, fitness function value, Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), and execution time.

关键词： Meta-Heuristic algorithm Multi-level thresholding image segmentation arithmetic optimization algorithm Harris Hawks Optimizer

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Estimating the ultra-high-performance concrete compressive strength with a machine learning model via meta-heuristic algorithms

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MULTISCALE AND MULTIDISCIPLINARY MODELING EXPERIMENTS AND DESIGN 2024年第3期7卷 1807-1818页

作者： Liu, Bing Chongqing Vocat Coll Transportat Coll Intelligent Mfg & Automobile Chongqing 402247 Peoples R China

In recent years, many experimental articles have been conducted to study ultra-high-performance concrete (UHPC). Thus, the relationship between its blend composition and the mechanical properties of UHPC is highly non-linear and challenging to utilize conventional statistical approaches. A robust and sophisticated method is needed to rationalize the variety of relevant experimental datasets, provide insight into aspects of non-linear materials science, and make estimative tools of desirable accuracy. Machine learning (ML) is a potent strategy that can reveal underlying patterns in complex datasets. This study aims to employ state-of-the-art ML methods for predicting the UHPC compressive strength (CS) by operating 165 previously published samples with 8 input characteristics via support vector regression (SVR). In addition, a novel approach has been used based on meta-heuristic algorithms to enhance accuracy, including Dynamic arithmetic optimization algorithm (DAOA), arithmetic optimization algorithm (AOA), and Black Widow optimization (BWO). Furthermore, the models evaluated the prediction input dataset by some criteria indicators. The results indicated that the represented models obtained suitable estimative efficiency and can be reliable on ML methods in saving time and energy. In general, in comparing hybrid models, SVDA has a more acceptable performance than other hybrid models.

关键词： Ultra high-performance concrete Support vector regression Black widow optimization arithmetic optimization algorithm Dynamic arithmetic optimization algorithm

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Prediction of thermal conductivity of granitic rock: an application of arithmetic and salp swarm algorithms optimized ANN

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EARTH SCIENCE INFORMATICS 2022年第4期15卷 2303-2317页

作者： Lawal, Abiodun Ismail Kwon, Sangki Kim, Minju Aladejare, Adeyemi Emman Onifade, Moshood Inha Univ Yong Hyun Dong Dept Energy Resources Engn Incheon South Korea Fed Univ Technol Akure Dept Min Engn Akure Nigeria Univ Oulu Oulu Min Sch Oulu Finland Univ Nambia Dept Civil & Min Engn POB 3624 Ongwediva Namibia

Thermal conductivity (TC) is an important rock property as it determines its energy transfer potential. Compared with other rock properties like uniaxial comprehensive strength (UCS), it is rarely investigated. Hence, novel arithmetic and Salp swarm optimized artificial neural network (ANN) models are used to predict the thermal conductivity of granitic rock based on the results of non-destructive tests. Fifty (50) core samples were obtained from the study location and tested in the laboratory. The results obtained from the laboratory investigations were used to perform the ordinary ANN and the optimized ANN models. The outcomes showed that the performances of the optimized ANN models are better than the ordinary ANN model. The results were also compared with the multiple linear regression model (MLR) although the predictive strength of the MLR model is extremely low. The proposed models were mathematically transformed into simple mathematical models, and a graphic user interface (GUI) prepared with the Visual basic programming language was developed. The proposed models can be practically implemented for TC prediction.

关键词： Thermal conductivity Energy transfer arithmetic optimization algorithm Salp swarm

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Modeling an Enhanced Modulation Classification Approach using arithmetic optimization with Deep Learning for MIMO-OFDM Systems

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MEASUREMENT SCIENCE REVIEW 2024年第2期24卷 47-53页

作者： Venkatramanan, M. Chinnadurai, M. EGS Pillay Engn Coll Nagapattinam Tamil Nadu India

In a Multiple-Input Multiple-Output Orthogonal Frequency Division Multiplexing (MIMO-OFDM) method, multiple antennas can be used on either the transmitter or receiver end to improve the system capacity, data throughput, and robustness. OFDM has been used as the modulation system that divides the data stream into multiple parallel low-rate subcarriers. MIMO enhances the system by utilizing spatial diversity and multiplexing abilities. Modulation classification in the MIMO-OFDM systems describes the process of recognizing the modulation scheme used by the communicated signals in a MIMO-OFDM communication system. This is a vital step in receiver design as it enables proper demodulation of the received signals. In this paper, an Enhanced Modulation Classification Approach using an arithmetic optimization algorithm with Deep Learning (EMCA-AOADL) is developed for MIMO-OFDM systems. The goal of the presented EMCAAOADL technique is to detect and classify different types of modulation signals that exist in MIMO-OFDM systems. To accomplish this, the EMCA-AOADL technique performs a feature extraction process based on the Sevcik Fractal Dimension (SFD). For modulation classification, the EMCA-AOADL technique uses a Convolution Neural Network with Long Short-Term Memory (CNN-LSTM) approach. Finally, the hyperparameter values of the CNN-LSTM algorithm can be chosen by using AOA. To highlight the better recognition result of the EMCA-AOADL approach, a comprehensive range of simulations was performed. The simulation values illustrate the better results of the EMCA-AOADL algorithm.

关键词： Modulation classification MIMO-OFDM system deep learning arithmetic optimization algorithm

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Performance Evaluation of Bundle Adjustment with Population Based optimization algorithms Applied to Panoramic Image Stitching

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SENSORS 2021年第15期21卷 5054-5054页

作者： Aguiar, Maria Julia R. Alves, Tiago da Rocha Honorio, Leonardo M. Junior, Ivo C. S. Vidal, Vinicius F. Univ Fed Juiz de Fora Dept Elect Engn BR-36036900 Juiz De Fora Brazil

The image stitching process is based on the alignment and composition of multiple images that represent parts of a 3D scene. The automatic construction of panoramas from multiple digital images is a technique of great importance, finding applications in different areas such as remote sensing and inspection and maintenance in many work environments. In traditional automatic image stitching, image alignment is generally performed by the Levenberg-Marquardt numerical-based method. Although these traditional approaches only present minor flaws in the final reconstruction, the final result is not appropriate for industrial grade applications. To improve the final stitching quality, this work uses a RGBD robot capable of precise image positing. To optimize the final adjustment, this paper proposes the use of bio-inspired algorithms such as Bat algorithm, Grey Wolf Optimizer, arithmetic optimization algorithm, Salp Swarm algorithm and Particle Swarm optimization in order verify the efficiency and competitiveness of metaheuristics against the classical Levenberg-Marquardt method. The obtained results showed that metaheuristcs have found better solutions than the traditional approach.

关键词： metaheuristics bundle adjustment panorama image bat algorithm grey wolf optimizer arithmetic optimization algorithm Salp Swarm algorithm particle swarm optimization

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