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JOURNAL OF APPLIED SCIENCE AND ENGINEERING 2025年第5期28卷 1095-1105页

作者： Ma, Chenyue Shao, Qiang Yin, Mengtian Hebei Univ Water Resources & Elect Engn Fdn Dept Cangzhou 061001 Peoples R China

The California bearing ratio (CBR) as a widespread index is employed in Soil mechanics and geotechnical structures such as bridge abutments, earth dams, highway embankments, and so on. Generally, this index can be determined through laboratory or field tests. However, CBR determining procedure is time and cost-consuming. Therefore, the present study represents different hybrid models established on Least Square Support Vector Regression (LSSVR) combined with three meta-heuristic algorithms (namely, arithmetic optimization algorithm, Bald eagle search optimization, and Seagull optimization algorithm) to estimate CBR values in a way that is cheap and quick to perform, and more accurate in solving real-world problems. 70% of developed hybrid models were allocated to the train stage and remained 30% were defined as testing models. The impression of five inputs (lime sludge percentage, lime percentage, maximum dry density, curing period, and optimum moisture content) on the predicted CBR values were considered. Finally, to assess the accuracy of the two-category created models, a comparison study between predicted and observed results is made by using five statistical indexes. The observations have shown that LSBE (LSSVR combined with Bald Eagle search optimization) model has strong potential for predicting the CBR.

关键词： California Bearing Ratio Least Square Support Vector Regression arithmetic optimization algorithm Bald eagle

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Improving Heating Load Prediction With LSSVR: Comparative Analysis Of Optimized Models

JOURNAL OF APPLIED SCIENCE AND ENGINEERING

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JOURNAL OF APPLIED SCIENCE AND ENGINEERING 2025年第5期28卷 1131-1145页

作者： Deshen, L., V Liang, Chengquan Lu, Xiao Nanning Univ Sch Intelligent Mfg Nanning 530200 Peoples R China Guilin Univ Technol Sch Informat Sci & Engn Guilin 541006 Peoples R China

At present, energy usage is one of the critical components of the global economy and population growth in the construction sector. Buildings play a crucial role in global energy consumption, and it is vital to forecast the heating needs of this sector thoroughly. This necessity is driven by various significant factors, including improving energy efficiency, financial responsibility, promoting environmental health, and developing sustainable and long-lasting solutions. Accurately estimating the heating load of buildings is incredibly important. Machine learning (ML) is one of the most effective techniques among the various methods employed for this purpose. This approach involves the analysis of historical data and an evaluation of the present conditions within the building to deliver precise predictions regarding heating load requirements. This study aims to apply the Least Squares Support Vector Regression (LSSVR) method, a frequently used ML algorithm for predicting continuous numerical values for determining building heating load. The application of the arithmetic optimization algorithm (AOA) and the Ebola optimization Search algorithm (EOSA) is geared toward improving accuracy and reducing overall losses in heating load estimation. The research provides significant insights into predicting building heating loads and recommends that employing an LSSVR+EOSA (LSEO) model is the most efficient strategy for optimizing energy consumption. This hybrid model achieved a maximum determination coefficient of 0.985 and a root mean square error of 1.223.

关键词： Heating Load Least Squares Support Vector Regression arithmetic optimization algorithm Ebola

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Optimal power flow using a hybridization algorithm of arithmetic optimization and aquila optimizer

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EXPERT SYSTEMS WITH APPLICATIONS 2024年 235卷

作者： Ahmadipour, Masoud Othman, Muhammad Murtadha Bo, Rui Javadi, Mohammad Sadegh Ridha, Hussein Mohammed Alrifaey, Moath Univ Teknol MARA Coll Engn Sch Elect Engn Shah Alam 40450 Selangor Malaysia Missouri Univ Sci & Technol Dept Elect & Comp Engn Rolla MO 65409 USA Inst Syst & Comp Engn Technol & Sci INESC TEC Porto Portugal Univ Putra Malaysia UPM Fac Engn Dept Elect & Elect Engn Adv Lightning Power & Energy Res ALPER Serdang 43400 Selangor Malaysia Univ Al Mustansiriyah Dept Comp Engn Baghdad Iraq Univ Putra Malaysia Fac Engn Dept Mech & Mfg Engn Serdang 43400 Selangor Malaysia

In this paper, a hybridization method based on arithmetic optimization algorithm (AOA) and Aquila optimizer (AO) solver namely, the AO-AOA is applied to solve the Optimal Power Flow (OPF) problem to independently optimize generation fuel cost, power loss, emission, voltage deviation, and L index. The proposed AO-AOA algorithm follows two strategies to find a better optimal solution. The first strategy is to introduce an energy parameter (E) to balance the transition between the individuals' procedure of exploration and exploitation in AOAOA swarms. Next, a piecewise linear map is employed to reduce the energy parameter's (E) randomness. To evaluate the performance of the proposed AO-AOA algorithm, it is tested on two well-known power systems i.e., IEEE 30-bus test network, and IEEE 118-bus test system. Moreover, to validate the effectiveness of the proposed (AO-AOA), it is compared with a famous optimization technique as a competitor i.e., Teaching-learning-based optimization (TLBO), and recently published works on solving OPF problems. Furthermore, a robustness analysis was executed to determine the reliability of the AO-AOA solver. The obtained result confirms that not only the AO-AOA is efficient in optimization with significant convergence speed, but also denotes the dominance and potential of the AO-AOA in comparison with other works.

关键词： arithmetic optimization algorithm Aquila optimizer Map of piecewise linear Optimal power flow optimization

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Levy arithmetic algorithm: An enhanced metaheuristic algorithm and its application to engineering optimization

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EXPERT SYSTEMS WITH APPLICATIONS 2024年 241卷

作者： Barua, Sujoy Merabet, Adel St Marys Univ Div Engn Halifax NS Canada St Marys Univ Div Engn 923 Robie St Halifax NS B3H 3C3 Canada

Levy arithmetic algorithm is an upgraded metaheuristic optimization approach proposed to enhance the arithmetic optimization algorithm using the Levy random step. arithmetic optimization algorithm addresses diverse optimization problems by employing arithmetic operators. However, its linear search capability might hinder attaining optimal solutions, which leads to stagnation. In this study, the Levy arithmetic algorithm is introduced by combining the arithmetic optimization algorithm and the Levy random step to enhance search capabilities and minimize computational demands for improved outcomes. The evaluation encompassed ten CEC2019 benchmark functions, four established real-world engineering problems, and the Economic Load Dispatch of microgrids with renewable energy integration. Using evaluation metrics such as standard deviation and mean, along with hypothesis tests, the study conducts a thorough comparison between the performance of the proposed algorithm and that of the conventional arithmetic optimization algorithm. The results show that the Levy arithmetic algorithm achieves optimization with a minimized number of evaluations in terms of standard deviation and mean, compared to the arithmetic optimization algorithm. Additionally, the proposed method was compared with various well-known and recent metaheuristics algorithms, and the Levy arithmetic algorithm consistently demonstrates superior performance, especially in contrast to the arithmetic optimization algorithm.

关键词： arithmetic optimization algorithm Levy arithmetic algorithm Metaheuristic algorithm CEC Benchmarks Engineering Problems Microgrid ELD

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Gaussian quantum arithmetic optimization-based histogram equalization for medical image enhancement

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MULTIMEDIA TOOLS AND APPLICATIONS 2023年第22期82卷 34725-34748页

作者： Pashaei, Elnaz Pashaei, Elham Istanbul Aydin Univ Dept Software Engn Istanbul Turkiye Indiana Univ Sch Med Dept Med & Mol Genet Indianapolis IN 46202 USA Istanbul Gelisim Univ Dept Comp Engn Istanbul Turkiye

The quality of medical images is critical for accurate diagnosis. This paper introduces a novel Quantum-behaved arithmetic optimization algorithm (QAOA) for medical images. A mutation operator with Gaussian probability distribution is used in the proposed QAOA as a powerful strategy to enhance QAOA performance in preventing premature convergence to local optima. Gaussian QAOA (GQAOA) is tailored for medical image enhancement and hybridized with Contrast Limited Adaptive Histogram Equalization (CLAHE) to boost the information contents and details of medical images. GQAOA computes the optimal clip limit and other parameters of CLAHE using a new multi-objective fitness function. A combination of five image quality measurements including contrast, information entropy, edge information, Structural Similarity Index Measure (SSIM), and sharpness is suggested as an efficient fitness function to help the proposed framework produce good results. A comparative study is conducted with well-known histogram-based process techniques and state-of-art methods to demonstrate the efficiency of the suggested algorithm. The experimental results prove that the suggested approach performs better than the most current well-established enhancement strategies in the terms of visual interpretation, information entropy, SSIM, Peak Signal to Noise Ratio (PSNR), Naturalness Image Quality Evaluator (NIQE), Absolute Mean Brightness Error (AMBE), and Quality Index (QI) metrics.

关键词： Contrast enhancement arithmetic optimization algorithm Medical image Histogram equalization

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arithmetic optimization With Ensemble Deep Learning SBLSTM-RNN-IGSA Model for Customer Churn Prediction

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IEEE ACCESS 2023年 11卷 93111-93128页

作者： Jajam, Nagaraju Challa, Nagendra Panini Prasanna, Kamepalli S. L. Deepthi, C. H. Venkata Sasi VIT AP Univ Sch Comp Sci & Engn SCOPE Amaravati 522237 Andhra Pradesh India Shri Vishnu Engn Coll Women Autonomous Dept Comp Sci & Engn Bhimavaram 534202 India

Companies in a wide variety of industries use the customer churn prediction (CCP) process to keep their current clientele happy. Insurance companies need to be able to forecast churn to enhance the potency and functionality of deep learning methods. Deep learning techniques have a significant impact on improving and forecasting customer retention. Numerous studies employ standard machine learning and Deep Learning strategies to enhance customer retention, despite the fact that these strategies have a number of accuracy issues. In light of this need, this piece is dedicated to the development of a stacked bidirectional long short-term memory (SBLSTM) and RNN model for arithmetic Optimisation algorithm (AOA) in CCP. The proposed AOA-SBLSTM-RNN model intends to proficiently forecast the occurrence of Customer Churn in the Insurance industry. Initially, the AOA model performs pre-processing to transform the original data into a useful format. In addition, the SBLSTM-RNN model is used to distinguish between churning and non-churning customers. To improve the CCP outcomes of the SBLSTM-RNN model, an optimal Hyperparameters tuning process using Improved Gravitational Search optimization algorithm (IGSA) is used in this study. In this work, Three Health Insurance datasets were used to evaluate performance, and four sets of experiments were conducted. The Measures of true churn, false churn, specificity, precision, and accuracy are employed to assess the efficacy of the proposed approach. Experimental result shows that the Ensemble Deep Learning model AOA-SBLSTM-RNN with IGSA produces accuracy value of 97.89 and 97.67 on dataset 2 and dataset 1. which is better and had higher predictability levels in compared with all other models.

关键词： Customer churn insurance industry ensemble deep learning arithmetic optimization algorithm feature extraction stacked bidirectional long short-term memory RNN improved gravitational search optimization algorithm

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arithmetic optimization for Coinciding Diabetic Retinopathy and Diabetic Macular Edema Grading based on Self-Attention Convolutional Neural Network

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2024年第PartB期95卷

作者： Devi, T. M. Karthikeyan, P. Syed Ammal Engn Coll Dept Comp Sci & Engn Madurai Rameswaram Rd Landai Tamil Nadu India Thiagarajar Coll Engn Dept Informat Technol V3JJ VJ3 Thiruparankundram Tamil Nadu India

This paper proposes a Self-Attention Convolutional Neural Network (SACNN) optimized with arithmetic optimization algorithm (AOA) for coinciding Diabetic Retinopathy (DR) and Diabetic Macular Edema Grading (DMEG) (SACNN-AOA-DR-DMEG). Initially, the input image is collected from 2 openly available benchmark datasets, namely Messidor and ISBI 2018 IDRiD. Then the input image is pre-processing using Altered Phase Preserving Dynamic Range Compression (APPDRC) for reducing noise from the imageries. SACNN receives the pre-processed imageries. The SACNN has three modules: (i) plane attention module, (ii) depth attention module, (iii) Attention Fusion Module. DR and DME features are extracted by plane attention module and depth attention module of SACNN. Attention Fusion Module receives extracted characteristics for categorizing and grading DR and DME disorders. SACNN does not adopt any optimization techniques to guarantee accurate DR and DME grading disorders. That's why, arithmetic optimization algorithm (AOA) is deemed to optimize the SACNN weight parameters. The proposed technique is implemented in Python. The proposed SACNN-AOA-DR-DMEG method provides 11.18%, 18.99% and 23.76% higher accuracy for diabetic retinopathy grading;11.52%, 29.62% and 20.38% higher accuracy for DMEG;33.39%, 22%, 39.26% lower computation time on Messidor data compared with the existing methods, such as AMGNN-DR-DMEG, LCNN-DR-DMEG, and FFN-DR-DMEG respectively.

关键词： arithmetic optimization algorithm Messidor dataset Self -attention convolutional neural network

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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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An improved remora optimization algorithm with autonomous foraging mechanism for global optimization problems

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MATHEMATICAL BIOSCIENCES AND ENGINEERING 2022年第4期19卷 3994-4037页

作者： Zheng, Rong Jia, Heming Abualigah, Laith Liu, Qingxin Wang, Shuang Sanming Univ Sch Informat Engn Sanming 365004 Peoples R China Amman Arab Univ Fac Comp Sci & Informat Amman 11953 Jordan Univ Sains Malaysia Sch Comp Sci George Town 11800 Malaysia Sanming Univ Sch Educ & Mus Sanming 365004 Peoples R China

The remora optimization algorithm (ROA) is a newly proposed metaheuristic algorithm for solving global optimization problems. In ROA, each search agent searches new space according to the position of host, which makes the algorithm suffer from the drawbacks of slow convergence rate, poor solution accuracy, and local optima for some optimization problems. To tackle these problems, this study proposes an improved ROA (IROA) by introducing a new mechanism named autonomous foraging mechanism (AFM), which is inspired from the fact that remora can also find food on its own. In AFM, each remora has a small chance to search food randomly or according to the current food position. Thus the AFM can effectively expand the search space and improve the accuracy of the solution. To substantiate the efficacy of the proposed IROA, twenty-three classical benchmark functions and ten latest CEC 2021 test functions with various types and dimensions were employed to test the performance of IROA. Compared with seven metaheuristic and six modified algorithms, the results of test functions show that the IROA has superior performance in solving these optimization

关键词： remora optimization algorithm arithmetic optimization algorithm metaheuristic algorithm swarm intelligence global optimization

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Modeling the pile settlement using the Integrated Radial Basis Function (RBF) neural network by Novel optimization algorithms: HRBF-AOA and HRBF-BBO

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022年第6期43卷 7009-7022页

作者： Zhang, Ming Du, Qian Yang, Jianxun Liu, Song Shenzhen Bur Geol Shenzhen 518023 Guangdong Peoples R China China Construct First Grp Fifth Construct Co Ltd Beijing Peoples R China

The Pile movement is one of the most crucial matters in designing piles and foundations that need to be estimated for any project failure. Over the variables used in forecasting Pile Settlement, many methods have been introduced to appraise it. However, existing a wide range of theoretical strategies to investigate the pile subsidence, the soil-pile interactions are still ambiguous for academic researchers. Most studies have tried to work out the subsidence rate in piles after loading passing time by artificial intelligence methods. Generally, the Artificial Neural Network (ANN) has drawn attention to show the actual views of pile settlement over the loading phase vertically. This research aims to present the Hybrid Radial Basis Function neural network integrated with the Novel arithmetic optimization algorithm and Biogeography-Based optimization to calculate the optimal number of neurons embedded in hidden layers. The transportation network of Klang Valley, Mass Rapid Transit in Kuala Lumpur, Malaysia, was chosen to analyze the piles' settlement and earth features using HRBF-AOA and HRBF-BBO scenarios. Over the prediction process, the R-values of HRBF-AOA and HRBF-BBO were obtained at 0.9825 and 0.9724, respectively. The MAE also shows a similar trend as 0.2837 and 0.323, respectively.

关键词： Pile in rock settlement prediction radial basis function biogeography-based optimization arithmetic optimization algorithm r-value correlation

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