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dynamic arithmetic optimization algorithm for Truss optimization Under Natural Frequency Constraints

IEEE ACCESS 2022年 10卷 16188-16208页

作者： Khodadadi, Nima Snasel, Vaclav Mirjalili, Seyedali Iran Univ Sci & Technol Dept Civil Engn Tehran *** Iran VSB Tech Univ Ostrava Fac Elect Engn & Comp Sci Dept Comp Sci Ostrava 70800 Czech Republic Torrens Univ Australia Ctr Artificial Intelligence Res & Optimizat Adelaide SA 5003 Australia Yonsei Univ Yonsei Frontier Lab Seoul 03722 South Korea

Metaheuristic algorithms have successfully been used to solve any type of optimization problem in the field of structural engineering. The newly proposed arithmetic optimization algorithm (AOA) has recently been presented for mathematical problems. The AOA is a metaheuristic that uses the main arithmetic operators' distribution behavior, such as multiplication, division, subtraction, and addition in mathematics. In this paper, a dynamic version of the arithmetic optimization algorithm (DAOA) is presented. During an optimization process, a new candidate solution change to regulate exploration and exploitation in a dynamic version in each iteration. The most remarkable attribute of DAOA is that it does not need to make any effort to preliminary fine-tuning parameters relative to the most present metaheuristic. Also, the new accelerator functions are added for a better search phase. To evaluate the performance of both the AOA and its dynamic version, minimizing the weight of several truss structures under frequency bound is tested. These algorithms ' efficiency is obtained by five classical engineering problems and optimizing different truss structures under various loading conditions and limitations.

关键词： Heuristic algorithms arithmetic Metaheuristics Search problems Time-frequency analysis Licenses Genetic algorithms dynamic arithmetic optimization algorithm DAOA frequency constraints optimal design truss structures optimization benchmark

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Predicting The California Bearing Ratio Applying The Automated Framework Of Regression Model

JOURNAL OF APPLIED SCIENCE AND ENGINEERING

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JOURNAL OF APPLIED SCIENCE AND ENGINEERING 2025年第7期28卷 1435-1447页

作者： Hu, Pan Jin, Jing Yun, Yu Yellow River Conservancy Tech Inst Civil Engn & Transportat Engn Kaifeng 475004 Peoples R China

The construction of flexible pavement on expansive soil subgrade necessitates the precise determination of the California Bearing Ratio (CBR) value, a crucial aspect of flexible pavement design. However, the conventional laboratory determination of CBR often demands considerable human resources and time. As a result, there is a need to explore alternative methods, such as developing dependable models to estimate the CBR of modified expansive soil subgrade. In this research, a machine learning (ML) model, specifically a Random Forest (RF) machine model, was developed to forecast the CBR of an expansive soil subgrade mixed with sawdust ash, ordinary Portland cement, and quarry dust. The models' performance was assessed using several error indices, and the findings revealed that the RFAO model exhibited superior predictive capability when compared to the RFDA and RFSM machine models. Specifically, the R2 values for the training and testing data for the RFAO model were 0.9952 and 0.9988, respectively. In addition, RFAO obtained the most suitable RMSE equal to 0.4878. The RFAO model generally indicated an acceptable predictive ability and more desirable generalization ability than the other developed models.

关键词： California bearing ratio Random Forest dynamic arithmetic optimization algorithm Slime Mould algorithm Aquila Optimizer

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ACBiGRU-DAO: Attention Convolutional Bidirectional Gated Recurrent Unit-based dynamic arithmetic optimization for Air Quality Prediction

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ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 2023年第37期30卷 86804-86820页

作者： Panneerselvam, Vinoth Thiagarajan, Revathi Mepco Schlenk Engn Coll Dept Comp Sci & Engn Sivakasi India Mepco Schlenk Engn Coll Dept Informat Technol Sivakasi India

Over the past decades, air pollution has turned out to be a major cause of environmental degradation and health effects, particularly in developing countries like India. Various measures are taken by scholars and governments to control or mitigate air pollution. The air quality prediction model triggers an alarm when the quality of air changes to hazardous or when the pollutant concentration surpasses the defined limit. Accurate air quality assessment becomes an indispensable step in many urban and industrial areas to monitor and preserve the quality of air. To accomplish this goal, this paper proposes a novel Attention Convolutional Bidirectional Gated Recurrent Unit based dynamic arithmetic optimization (ACBiGRU-DAO) approach. The Attention Convolutional Bidirectional Gated Recurrent Unit (ACBiGRU) model is determined in which the fine-tuning parameters are used to enhance the proposed method by dynamic arithmetic optimization (DAO) algorithm. The air quality data of India was acquired from the Kaggle website. From the dataset, the most-influencing features such as Air Quality Index (AQI), particulate matter namely PM2.5 and PM10, carbon monoxide (CO) concentration, nitrogen dioxide (NO2) concentration, sulfur dioxide (SO2) concentration, and ozone (O-3) concentration are taken as input data. Initially, they are preprocessed through two different pipelines namely imputation of missing values and data transformation. Finally, the proposed ACBiGRU-DAO approach predicts air quality and classifies based on their severities into six AQI stages. The efficiency of the proposed ACBiGRU-DAO approach is examined using diverse evaluation indicators namely Accuracy, Maximum Prediction Error (MPE), Mean Absolute Error (MAE), Mean Square Error (MSE), Root Mean Square Error (RMSE), and Correlation Coefficient (CC). The simulation result inherits that the proposed ACBiGRU-DAO approach achieves a greater percentage of accuracy of about 95.34% than other compared methods.

关键词： Air Quality Prediction Attention Convolutional Bidirectional Gated Recurrent Unit dynamic arithmetic optimization algorithm Accuracy

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Hybrid dynamic arithmetic city council optimization for improved rainfall prediction

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INTERNATIONAL JOURNAL OF SYSTEM ASSURANCE ENGINEERING AND MANAGEMENT 2024年第7期15卷 3182-3192页

作者： Lathika, P. Singh, D. Sheeba Noorul Islam Ctr Higher Educ Dept Math Kumarakovil Tamil Nadu India

In the meteorological department rainfall prediction is one of the complex tasks because it is directly linked to human life and the Indian economy. There is a significant demand for accurate and effective rainfall prediction methods to make better decisions regarding precautionary measures. To predict rainfall amounts effectively, this study proposed a novel rainfall prediction method named the Hybrid dynamic arithmetic City Council optimization (HDACO) algorithm. The proposed HDACO method is a combination of two algorithms namely the dynamic arithmetic optimization (DAO) algorithm and the City Councils Evolution (CCE) algorithm. The study utilizes preprocessing steps namely data cleaning, filling missing values, and data normalization. After preprocessing, the features closely related to rainfall prediction are selected by the computation of the correlation matrix. Finally, based on the features selected the HDACO algorithm predicts the amount of rainfall. The HDACO algorithm is evaluated using an open weather dataset and the effectiveness of the HDACO algorithm is validated using measures such as rainfall rate, Mean Absolute Error (MAE), coefficient of determination (R2), and Root Mean Square Error (RMSE). As a result, the HDACO algorithm achieved RMSE of 0.272, MAE of 0.184, and R2 of 0.97 respectively. The performance of the HDACO algorithm is compared with existing methods and the results demonstrate the better performance of the HDACO algorithm in rainfall prediction.

关键词： Rainfall prediction dynamic arithmetic optimization algorithm City councils evolution algorithm Correlation matrix Open weather dataset

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An adaptive learning paradigm: event detection through a novel dynamic arithmetic optimization-based ensemble SVM for data stream classification

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International Journal of Information Technology (Singapore) 2024年第5期16卷 3049-3055页

作者： Vidya, R. Mary Ramakrishna, M. Vemana Institute of Technology Visvesvaraya Technological University Karnataka Belagavi India

Data stream mining is the process of generating continuous data stream records such as internet search, phone conversations, sensor data, etc. However it performs huge tasks such as frequency counting, clustering, analysis as well as classification. Mining information from data streams is often considered as a complicated process due to the rapid change in the underlying concept which is often referred to as concept drift and the high speed of data arrival. Moreover the data stream classification process is not stationary where each transmission is evolved with time. In addition to this, it cannot able to handle imbalanced data and is not able to accommodate new classes. To overcome this problem, an Ensemble Learning model based Support Vector Machine (ESVM) is proposed to perform the data stream classification. To achieve higher diversity, each base SVM is trained with different feature subsets and updated during the presence of new data instances. However, the selection of optimal feature subsets from high dimensional data streams is complex due to the increase in size and computational cost. Hence dynamic Accelerated Function (DAF) and dynamic Candidate Solution (DCS) approaches are developed that diminish the classification error and improve the performance with the best fitness value. The performances of the proposed methods is validated based on accuracy, precision, F-score, kappa, and relative error. The experimental result demonstrates that the proposed model is efficient when evaluated in terms of classification accuracy, rapid training, processing time, kappa score and attained an accuracy of 91.45%. © Bharati Vidyapeeth's Institute of Computer Applications and Management 2024.

关键词： Candidate feature subset Data stream dynamic arithmetic optimization algorithm Ensemble support vector machine Error minimization Feature selection

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An efficient task offloading and resource allocation using dynamic arithmetic optimized double deep Q-network in cloud edge platform

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PEER-TO-PEER NETWORKING AND APPLICATIONS 2023年第2期16卷 958-979页

作者： Kumaran, K. Sasikala, E. SRM Inst Sci & Technol Dept Comp Sci & Engn Chennai India Easwari Engn Coll Dept Informat Technol Chennai India SRM Inst Sci & Technol Dept Data Sci & Business Syst Chennai India

The dramatic increase in the number of the Internet of Things (IoT) devices resulted in massive data being generated. This complexity mainly increases the need to offload the IoT tasks to minimize the higher latency, computation, and storage complexities of resourceful architectures such as cloud and edge computing. Even though edge computing minimizes latency-related issues, the model deployment adds new challenges when different offloading schemes or service architectures are utilized. The main aim of this paper is to minimize the latency of high-priority healthcare applications that needs immediate service using different steps. The improved Variational mode decomposition (VMD)-Random Forest (RF) architecture is used to classify the edge device application tasks into computationally intensive, time-sensitive, and priority-sensitive workloads. The tasks are mainly classified by taking different parameters as input such as the task length, network demand, delay sensitivity, and Virtual Machine (VM) utilization parameters. This step reduces the processing time of edge-based applications. For task offloading, a novel dynamic arithmetic optimized double deep Q-network (DAO-DDQN) architecture is developed, which determines task offloading decisions based on the classification results from the VMD-optimized RF design. A Computational Access Point (CAP) has been formed using interconnected wireless access points and the CAP is used for executing the application requests sent from mobile edge devices. To improve the task processing and computational capabilities of edge devices, the dynamic arithmetic optimization algorithm (DAOA) is employed to choose the optimal CAP for task offloading. These steps help to minimize the edge latency by simultaneously improving the edge network performance. The results show that the proposed methodology is efficient in improving the service parameters when terms of different parameters such as average delivery time, schedulability, comput

关键词： Task offloading Edge computing Double deep Q-network Internet of Things (IoT) Random Forest dynamic arithmetic optimization algorithm Computational Access Point selection

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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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Predicting CBR values using gaussian process regression and meta-heuristic algorithms in geotechnical engineering

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MULTISCALE AND MULTIDISCIPLINARY MODELING EXPERIMENTS AND DESIGN 2024年第4期7卷 3799-3813页

作者： Wu, Xu Yang, Feng Huang, Shuchen Nantong Inst Technol Dept BIM Res Nantong 226002 Peoples R China Krirk Univ Dept Engn Management Res Bangkok 10220 Thailand

The California Bearing Ratio (CBR) test is a vital tool in geotechnical engineering and is useful in controlled laboratory settings and dynamic field applications. It plays a pivotal role in determining the load-bearing properties of subgrade soil, which is essential for various construction projects, including retaining walls, highway embankments, bridge abutments, and earth dams. CBR values obtained from this test are fundamental for assessing soil strength and integrity, making it a cornerstone of geotechnical engineering. This paper presents an innovative method for predicting CBR values with high precision. It employs Gaussian Process Regression (GPR) to develop complex and highly accurate predictive models. These models encompass a wide range of soil characteristics, such as particle distribution, plasticity, linear shrinkage, and the type and quantity of stabilizing additives. The GPR model significantly improves predictive modeling accuracy by establishing robust relationships between these soil attributes and CBR values. Additionally, the study incorporates two advanced meta-heuristic algorithms, the dynamic arithmetic optimization algorithm (DAOA) and Leader Harris Hawk's optimization (LHHO), to enhance the precision of the predictive model. This collaborative effort resulted in the creation of three models: GPR + LHHO (GPLH), GPR + DAOA (GPDA), and GPR. The GPDA model stands out with exceptional predictive capabilities, achieving a remarkable R2 value of 0.989 during training and an optimal RMSE of 1.488, confirming its precision and consistency. This innovative approach advances CBR prediction and reinforces the reliability of geotechnical engineering practices across diverse soil conditions, making it a significant contribution to the field.

关键词： California bearing ratio Gaussian process regression dynamic arithmetic optimization algorithm Leader Harris Hawk's optimization

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Investigating Cooling Load Estimation via Hybrid Models Based on the Radial Basis Function

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2024年第4期15卷 1006-1018页

作者： Zhang, Sirui Zheng, Hao HeBei Petr Univ Technol Chengde 067000 Hebei Peoples R China

To advance energy conservation in cooling systems within buildings, a pivotal technology known as cooling load prediction is essential. Traditional industry computational models typically employ forward or inverse modeling techniques, but these methods often demand extensive computational resources and involve lengthy procedures. However, artificial intelligence (AI) surpasses these approaches, with its models exhibiting the capability to autonomously discern intricate patterns, adapt dynamically, and enhance their performance as data volumes increase. AI models excel in forecasting cooling loads, accounting for various factors like weather conditions, building materials, and occupancy. This results in agile and responsive predictions, ultimately leading to heightened energy efficiency. The dataset of this study, which comprised 768 samples, was derived from previous studies. The primary objective of this study is to introduce a novel framework for the prediction of Cooling Load via integrating the Radial Basis Function (RBF) with 2 innovative optimization algorithms, specifically the dynamic arithmetic optimization algorithm (DAO) and the Golden Eagle optimization algorithm (GEO). The predictive outcomes indicate that the RBDA prediction model outperforms RBF in cooling load predictions, with RMSE=0.792, approximately half as much as those of RBF. Furthermore, the RBDA model's performance, especially in the training phase, confirmed the optimal value of R-2=0.993.

关键词： Cooling load estimation machine learning building energy consumption radial basis functions dynamic arithmetic optimization algorithm golden eagle optimization algorithm

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