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Hybrid stochastic and robust optimization of a hybrid system with fuel cell for building electrification using an improved arithmetic optimization algorithm

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SCIENTIFIC REPORTS 2025年第1期15卷 1-35页

作者： Duan, Fude Eslami, Mahdiyeh Okati, Mustafa Jasim, Dheyaa J. Mahmood, Arsalan Khadim Nanjing Vocat Coll Informat Technol Sch Intelligent Transportat Nanjing 210000 Jiangsu Peoples R China Islamic Azad Univ Elect Engn Dept Kerman Branch Kerman Iran Islamic Azad Univ Dept Elect Engn Zabol Branch Zabol Iran Al Amarah Univ Coll Dept Petr Engn Maysan Iraq AL Mustaqbal Univ Coll Hlth & Med Tech Radiol Tech Dept Hillah 51001 Babil Iraq

This paper proposes a hybrid stochastic-robust optimization framework for sizing a photovoltaic/tidal/fuel cell (PV/TDL/FC) system to meet an annual educational building demand based on hydrogen storage via unscented transformation (UT), and information gap decision theory-based risk-averse strategy (IGDT-RA). The hybrid framework integrates the strengths of UT for scenario generation and IGDT-RA (hybrid UT-IGDT-RA) for optimizing the system robustness and maximum uncertainty radius (MRU) of building energy demand and renewable resource generation. The deterministic model focuses on minimizing the cost of energy production over the project's lifespan (CEPLS) and considers a reliability constraint defined as the demand shortage probability (DSHP). The study utilizes an improved arithmetic optimization algorithm (IAOA) to optimize component sizes and MRUs, incorporating a neighborhood search operator to enhance performance and prevent premature convergence. The deterministic findings revealed that the PV/TDL/FC system configuration offers the lowest CEPLS and the highest reliability level (lowest DSHP) compared to the hybrid PV/FC and TDL/FC configurations. Additionally, these results indicated that enhancing the reliability of the energy supply for the educational building entails higher CEPLS, particularly due to increased costs associated with hydrogen storage. The robust framework findings for the PV/TDL/FC system using IGDT-RA show that for an uncertainty budget of 21%, the MRUs for educational building demand and renewable generation are obtained at 10.34% and 2.65%, respectively, which are higher compared to other configurations. This indicates that the hybrid PV/TDL/FC system is more robust in handling worst-case scenario uncertainties. Furthermore, the hybrid UT-IGDT-RA outcomes found that the stochastic scenarios incorporated to simulate a range of uncertainties beyond the conventional IGDT-RA based-nominal scenario, and it provides a broader range of robust

关键词： Hybrid energy system Hybrid stochastic-robust sizing Information gap decision theory Risk-averse strategy improved arithmetic optimization algorithm

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improved arithmetic optimization algorithm for design optimization of fuzzy controllers in steel building structures with nonlinear behavior considering near fault ground motion effects

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ARTIFICIAL INTELLIGENCE REVIEW 2022年第5期55卷 4041-4075页

作者： Azizi, Mahdi Talatahari, Siamak Univ Tabriz Dept Civil Engn Tabriz Iran Near East Univ Engn Fac Mersin 10 Nicosia North Cyprus Turkey

In recent decades, near fault ground motions has been of great importance due to the difference in characteristics of earthquake records in the regions near to the active faults. Most of the developed control systems for structural vibration control have difficulties in dealing with these kinds of strong ground motions regarding the deficiencies of the human knowledge-based control systems such as fuzzy logic controllers for this purpose. Hence, the optimization of these controllers has been concerned in recent years. The main aim of this paper is to optimize the fuzzy controllers implemented in steel structures with nonlinear behavior in which the arithmetic optimization algorithm (AOA) is utilized as the main optimization algorithm while an improved version of this algorithm as IAOA is also proposed for performance enhancement of the standard algorithm. In the IAOA, a new parameter identification process is proposed in which the Levy flight as a well-known stochastic process with step length determined by levy distribution is implemented in the main loop of the AOA. The IAOA and AOA are utilized for optimization of the membership functions and the rule base of the fuzzy controllers implemented in a large-scale building structure. The overall performance of the IAOA is compared with the standard AOA and other metaheuristics. The obtained results of the improved method demonstrate the capability of this method in providing very competitive solutions which results in decreasing structural responses and damages of the considered building in dealing with the near-fault strong ground motions.

关键词： improved arithmetic optimization algorithm Fuzzy logic controller Near-fault earthquake Ground motion optimization

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improved arithmetic optimization algorithm for Parameters Extraction of Photovoltaic Solar Cell Single-Diode Model

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2022年第8期47卷 10435-10451页

作者： Abbassi, Abdelkader Ben Mehrez, Rached Bensalem, Yemna Abbassi, Rabeh Kchaou, Mourad Jemli, Mohamed Abualigah, Laith Altalhi, Maryam Univ Kairouan Inst Appl Sci & Technol Kasserine ISSATKas POB 471 Kasserine 1200 Tunisia Univ Carthage Not Engn Sch Carthage Res Lab Smart Elect & ICT Tunis Tunisia Univ Gabes High Inst Ind Syst Gabes ISSIG MACS LR16ES22 Gabes 6072 Tunisia Univ Hail Coll Engn Dept Elect Engn POB 2440 Hail Saudi Arabia Univ Tunis Natl Higher Engn Sch Tunis ENSIT Engn Lab Ind Syst & Renewable Energies LISIER 5 Ave Taha HusseinPOB 56 Tunis 1008 Tunisia Amman Arab Univ Fac Comp Sci & Informat Amman 11953 Jordan Univ Sains Malaysia Sch Comp Sci George Town 11800 Malaysia Taif Univ Coll Business Adm Dept Management Informat Syst POB 11099 At Taif 21944 Saudi Arabia

The accurate model of the solar PV system is the principal organ that describes the performance of this resource. Several approaches based on optimizing algorithms were considered valuable tools to illustrate the I-V curve for improving the photovoltaic models. Their electrical parameters are estimated using optimization algorithms referring to the experimental database or manufacturer's datasheet. This paper proposes a novel developed a photovoltaic model based on improved arithmetic optimization algorithm (IAOA) to extract the solar cell parameters. Also, an experimental test bench is presented for obtaining the measured illustration of the I-V characteristics. Thus, the root mean square error value that describes the difference between measured and estimated results is considered the objective function for two different models, the simple-diode model and the one-diode model. The proposed IAOA results are compared with other research papers and optimization algorithms. Furthermore, the evaluation of the proposed IAOA has been discussed considering several statistical analysis tests. The presented results show that the effectiveness and accuracy of IAOA results are excellent, and their I-V characteristics coincide with experimental data. Moreover, the results obtained by the proposed algorithm show its high superiority in optimizing the solar cell parameters under a variety of operating conditions.

关键词： Single diode model Photovoltaic solar cell improved arithmetic optimization algorithm Statistical analysis Performance

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improved arithmetic optimization algorithm and its application to discrete structural optimization

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STRUCTURES 2022年 35卷 748-764页

作者： Kaveh, Ali Hamedani, Kiarash Biabani Iran Univ Sci & Technol Sch Civil Engn Tehran Iran

The arithmetic optimization algorithm (AOA) is a newly developed metaheuristic search technique that simulates the distribution characteristics of the basic arithmetic operations of addition, subtraction, multiplication, and division, and has been employed to solve some real-world optimization problems. However, it has been found that the AOA suffers from poor exploration and prematurely converges to non-optimal solutions, especially when applied to multi-dimensional optimization problems. In this paper, to overcome the shortcomings of the standard AOA, an improved variant of the AOA, called improved arithmetic optimization algorithm (IAOA), is proposed and then employed for optimization of skeletal structures with discrete design variables. Compared to the standard AOA, two major improvements are made in the proposed IAOA: (1) The original formulation of the AOA is modified to enhance the exploration and exploitation capabilities;(2) The proposed IAOA requires fewer algorithm-specific parameters compared with the standard AOA, making it easy to be implemented. To examine the efficiency and robustness of the proposed IAOA, three benchmark structural optimization problems with discrete design variables are investigated and the results are compared to those of the standard AOA and other methods available in the literature. To the best of our knowledge, this is the first attempt to apply AOA to structural optimization. The IAOA not only promotes the exploration capability of the search space but also overcomes the shortcoming of the premature convergence of the standard AOA. Experimental results indicate that IAOA significantly surpasses the standard AOA and achieves results comparable or superior to other state-of-the-art metaheuristics.

关键词： Metaheuristics arithmetic optimization algorithm improved arithmetic optimization algorithm Structural optimization Skeletal structures Discrete design variables Truss Double-layer barrel vault Steel frame

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improved arithmetic optimization algorithm and its application to carbon fiber reinforced polymer-steel bond strength estimation

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COMPOSITE STRUCTURES 2023年第1期306卷

作者： Shi, Xiaoling Yu, Xinping Esmaeili-Falak, Mahzad Lyuliang Univ Dept Min Engn Luliang Peoples R China Zhejiang Guangsha Vocat & Tech Univ Construction Sch Intelligent Mfg Dongyang Peoples R China Islamic Azad Univ Dept Civil Engn North Tehran Branch Tehran Iran

In order to restore steel structures, bonding carbon fiber reinforced polymer (CFRP) laminates have been widely used. The bond strength (PU) between the CFRP and steel, along with the mechanical characteristics of the CFRP, is frequently crucial to the final strengthened effectiveness. However, the bond behavior at the CFRP-steel (CS) interface is incredibly complex, with several potential sources of failure, making it difficult to predict the PU and the stability of the CFRP-enhanced steel structure. In this specific instance, effective techniques were developed using a hybridized Random Forests (RF) methodology on collected CS single-shear experiment data to predict the PU of CS. The RF hyperparameters were tuned using the COOT optimizer (COOT), arithmetic optimization algorithm (AOA), and improved arithmetic optimization algorithm (IAOA). The IAOA was developed in this article by combining AOA with Aquila optimizer (AO) in order to overcome the shortage of it. When the training, testing, and data collection phases of each algorithm were executed in parallel, the results were uniformly excellent. The proposed IAOA -RF was the preferred approach, while other methods were also dependable in the prediction of CS interfacial PU, as determined by evaluating established designs using different aspects of analysis, such as different error criteria, the Taylor diagram, uncertainty analysis, scatter index analysis, and error distribution.

关键词： Carbon fiber reinforced polymer-steel interface Bond strength Random forests improved arithmetic optimization algorithm

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Optimal model of the combined cooling, heating, and power system by improved arithmetic optimization algorithm

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ENERGY SOURCES PART A-RECOVERY UTILIZATION AND ENVIRONMENTAL EFFECTS 2021年第1期47卷 10469-10491页

作者： Guo, Haibing Sun, Zhi Sun, Haixia Ebrahimian, Homayoun Jiangsu Ocean Univ Sch Sci Lianyungang Jiangsu Peoples R China Lianyungang Power Supply Co Jiangsu Elect Power Co State Grid Lianyungang 222000 Jiangsu Peoples R China Islamic Azad Univ Ardabil Branch Dept Engn Ardebil Iran

Due to the scarcity of energy sources, there is a requirement for a system that, besides saving energy, produces energy on its own. One way to meet this need is to utilize combined cooling, heating, and power (CCHP) systems. The CCHP system is the concurrent and thermodynamic production of two or more energy forms from a clear initial origin. However, the method of determining the chiller properly and the power generation unit capacity is always one of the problems for optimal designing of the CCHP systems. In this study, a new optimized model of the yearly hourly dynamic simulation is proposed for a CCHP system. To get better results, a newly developed design of the newly presented arithmetic Optimizer algorithm is designed and conducted. Afterward, the suggested improved arithmetic Optimizer was selected to optimize the CCHP system for its installed volume. The proposed method is confirmed by a case study from a main business region of Nanjing, China. The final analysis is done by comparison of the method with the real CCHP system, which indicates a proper satisfaction between them. The project has been established by considering a 4260 kW absorption chiller and a 4000-kW engine installed capacity. The power generation unit installed capacity is obtained 1321 kW during the variations of the CDER, PES, and LCCR indexes in various absorption cooling to the highest ratio of cooling load. By improving the absorption chiller installed capacity from 0.1 to 0.6, the CEI is increased. By exceeding the power above 5000 kW, the life cycle cost reduction has been anticipated to be less than 0. Also, simulation results indicate that different results can be obtained by different indexes.

关键词： Combined cooling heating power systems model optimization carbon dioxide emission reduction coefficient of performance life cycle cost reduction power generation unit improved arithmetic optimization algorithm

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A hybridization of growth optimizer and improved arithmetic optimization algorithm and its application to discrete structural optimization

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COMPUTERS & STRUCTURES 2024年 303卷

作者： Kaveh, Ali Hamedani, Kiarash Biabani Iran Univ Sci & Technol IUST Sch Civil Engn Tehran *** Iran

This paper proposes an improved hybrid growth optimizer (IHGO) to solve discrete structural optimization problems. The growth optimizer (GO) is a recent metaheuristic that has been successfully used to solve numerical and real-world optimization problems. However, it has been found that GO faces challenges with parameter tuning and operator refinement. We also noticed that the formulation of GO has some drawbacks, which may cause degradation in optimization performance. Compared to the original GO, four improvements are introduced in IHGO. First, the learning phase of GO is improved to avoid useless search and reinforce exploration. To do this, the exploration phase of an improved metaheuristic called IAOA is incorporated into the learning phase of GO. Second, the replacement strategy of GO is modified to prevent the loss of the best-so-far solution. Third, the hierarchical structure of GO is modified. Fourth, some adjustments are made to the reflection phase of GO to promote the exploitation of promising regions. To demonstrate the performance of the proposed IHGO, four discrete optimization problems of skeletal structures are provided. The results are compared with those of the original GO and some other metaheuristics in the literature. The source codes of IHGO are available at https://gi ***/K-BiabaniHamedani/improved-Hybrid-Growth-Optimizer.

关键词： Metaheuristic algorithms Growth optimizer improved growth optimizer improved arithmetic optimization algorithm Discrete structural optimization Skeletal structures

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Entropy-optimal image segmentation model based on an improved arithmetic optimization algorithm

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JOURNAL OF OPTICS-INDIA 2024年第4期53卷 3108-3118页

作者： Xu, Zhi Zhu, Ping Hebei North Univ Coll Informat Sci & Engn Zhangjiakou 075000 Hebei Peoples R China Hebei North Univ Dept Sci Dept Sci Res Zhangjiakou 075000 Hebei Peoples R China

Entropy-optimal image segmentation model is a technology used for image segmentation, aiming at obtaining the optimal segmentation result by optimizing the algorithm. It is difficult to segment the entropy-optimal image target that users are interested in effectively because of the uneven illumination distribution and the occlusion of the target. Therefore, an entropy-optimal image segmentation model based on improved arithmetic optimization algorithm is proposed. The entropy-optimal image is preprocessed by local visual saliency, and the visual saliency region is extracted. The information of the entropy-optimal image is comprehensively processed by conditional random field, and an entropy-optimal image segmentation method is designed. According to the flow chart and structure diagram of the improved arithmetic optimization algorithm, the entropy-optimal image segmentation model is constructed. The experimental results show that the average segmentation accuracy of the model is 98.54%, which has good segmentation accuracy. The signal-to-noise ratio is always above 95 dB, which can effectively segment the original image, and the edge of the segmented image is clear, the noise points are effectively removed, and the definition is high.

关键词： improved arithmetic optimization algorithm Entropy-optimal image Segmentation model Visual saliency area Conditional random field Noise points Image segmentation methods

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Parameterization of photovoltaic solar cell double-diode model based on improved arithmetic optimization algorithm

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OPTIK 2022年 253卷 168600-168600页

作者： Abbassi, Abdelkader Ben Mehrez, Rached Touaiti, Bilel Abualigah, Laith Touti, Ezzeddine Northern Border Univ Coll Engn Dept Elect Engn Ar Ar 1321 Saudi Arabia Univ Carthage Not Engn Sch Carthage Res Lab Smart Elect & ICT Carthage Tunisia Univ Tunis Natl Higher Engn Sch Tunis ENSIT Engn Lab Ind Syst & Renewable Energies LISIER 5 Ave Taha HusseinPOB 56 Tunis 1008 Tunisia Amman Arab Univ Fac Comp Sci & Informat Amman 11953 Jordan Univ Sains Malaysia Sch Comp Sci George Town 11800 Malaysia

After avoiding the Standard Test Condition (STC) related to the manufacturer of PV cells, accurate solar system modeling is considered the most important task to study this system. Several mathematical developments based-approaches or algorithms were considered valuable tools to parameterize the electrical circuit of the photovoltaic models. Thus, these parameters are well determined using optimization algorithms. To achieve this goal, an experimental database or manufacturer's datasheet is requested. This paper proposes a novel improved arithmetic optimization algorithm (IAOA) to extract the solar cell/panel parameters. Also, practical tests are presented for obtaining the measured illustration of the I-V and P-V characteristics. Thus, the Double Diode Model (DDM) of the electrical circuit of PV panel is studied considering various statistical analyzes, to know the Minimum (Min(Fobj), (DDM)), the Maximum (Max(Fobj), (DDM)). DDm ), the Average (Ave(Fobj), (DDM)), and the Standard Deviation (SDFobj, (DDM)). The estimated I-V and P-V curves using IAOA is compared with eight other recently published algorithms. In addition, the evaluation of the proposed IAOA proves that this proposal has a satisfactory performance than all tested algorithms. The obtained results show that the IAOA is considered first in terms of effectiveness and accuracy by 9.537e(-06) of standard deviation. Moreover, considering the variability of operating conditions, the proposed IAOA shows its high superiority to optimize the electrical parameters of the solar cell TDM Where it has the lowest errors by 3.623e(-05) of RMSE, 4.183e(-06) of MAE, and 1.397ee(-05) of RMSD.

关键词： Double diode model optimization improved arithmetic optimization algorithm Parameters identification Standard deviation Evaluation

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Exploring the Potential of Machine Learning in Predicting Soil California Bearing Ratio Values

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PERIODICA POLYTECHNICA-CIVIL ENGINEERING 2025年第2期69卷 551-566页

作者： Wu, Xu Lu, Feng He, Tao Nantong Inst Technol Dept BIM Res Nantong 226002 Peoples R China Nantong Huarong Construct Grp Co Ltd Nantong Jiangsu Peoples R China

Accurately predicting the California Bearing Ratio (CBR) of soil is vital for civil engineering projects as it determines soil strength and stability, crucial for designing safe and durable infrastructure. Conventional methods for calculating CBR values are both expensive and time-consuming, prompting the need for more efficient approaches. This study explores the use of advanced machine learning (ML) techniques to improve workflow and productivity in CBR prediction. Specifically, the improved arithmetic optimization algorithm (IAOA) and the Bonobo optimization algorithm (SBOA) are applied to enhance the Stochastic Gradient Boosting Regression (SGBR) model for predicting CBR values. The SGBR model, known for its ability to handle complex datasets and nonlinear interactions, is optimized to improve predictive accuracy. Performance metrics such as the coefficient of determination (R2), n10-index, and Root Mean Squared Error (RMSE) are used to assess the model's performance. After training, testing, and validation with relevant data, the optimized SGIA model (SGBR enhanced by IAOA) achieves impressive results, including an n10-index of 1.000, a root mean square error of 0.161, and an R2 value of 0.981. These metrics demonstrate the SGIA model's capability to accurately forecast CBR values, offering a reliable, cost-effective alternative to traditional methods for soil evaluation in engineering applications.

关键词： regression analysis California bearing ratio stochastic gradient boosting regression self-adaptive bonobo optimization algorithm improved arithmetic optimization algorithm

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