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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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Short-term Wind Power Prediction based on STL-AOA-Transformer algorithm 9

Short-term Wind Power Prediction based on STL-AOA-Transforme...

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9th International Conference on Power and Renewable Energy

作者： Li, Zhifeng Ye, Yurun Liu, Wenxin Lu, Ang Huadian Liaoning Energy Dev Co Ltd Shenyang Peoples R China Huazhong Univ Sci & Technol State Key Lab Adv Elect Technol Wuhan Peoples R China

ISBN: (纸本)9798350377477;9798350377460

Precise short-term wind power forecasting plays a crucial role in ensuring the effective and secure functioning of wind power installations. To improve the accuracy of short-term wind power forecasts, in this paper, a hybrid short-term wind power forecasting model STL-AOA-Transformer based on seasonal and trend decomposition using Loess (STL), arithmetic optimization algorithm (AOA) and transformer is proposed. AOA is used to optimize the parameters of transformer. STL is employed for the purpose of breaking down the initial data into modal components characterized by reduced volatility, in order to effectively capture the data features. These modal components are then fed into the AOA-Transformer for the prediction of short-term wind power and the subsequent derivation of the ultimate forecasted value. The efficacy of the model was assessed through testing and validation using authentic data spanning four seasons and four stations, and compared to three other models. The results show that the model fits the data well and has high prediction accuracy, which has important practical value.

关键词： short-term wind power prediction arithmetic optimization algorithm transformer model

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Two-stage structural damage detection using modal strain energy and natural frequencies: Numerical and experimental study

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Journal of Building Pathology and Rehabilitation 2024年第2期9卷 1-25页

作者： Rokhsati, Moeinoddin Mohamadi Dehcheshmeh, Mohamad Ghodrati Amiri, Gholamreza Raissi Dehkordi, Morteza Department of Civil Engineering Shahrekord Branch Islamic Azad University Natural Disasters Prevention Research Center School of Civil Engineering Iran University of Science and Technology School of Civil Engineering Iran University of Science and Technology

The present study suggests a new damage index using natural frequency and modal strain energy called NFMSEBI for two-stage structural damage localization and quantification. The goal is to improve the performance of conventional modal strain energy-based index (MSEBI) for damage localization of different structures. For this purpose, three different structures including a planar frame, a planar truss, and an experimental 6-story shear frame were studied. For the optimization stage, three different objective functions are suggested using the combination of mode shapes and natural frequencies, generalized flexibility matrix and natural frequencies, and static displacements and natural frequencies. The performance of the mentioned objective functions is compared utilizing a nature-based metaheuristic algorithm named marine predator algorithm (MPA). Moreover, the performance of MPA has been compared to that of artificial hummingbird algorithm (AHA) and arithmetic optimization algorithm (AOA) using the most accurate objective function selected for each structure. The results showed that the influence of damage is more reflected in NFMSEBI than in MSEBI, whereas in some cases of the planar frame, MSEBI failed to identify damaged elements. Furthermore, the first objective function produces the most accurate results for the truss and frame models, while the second one produces the most reliable identifications for the experimental structure. For the experimental structure MPA and AHA have superior performance over AOA, while in numerical models MPA has the most accurate and stable results. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： arithmetic optimization algorithm Artificial hummingbird algorithm Experimental shear frame Finite element model updating Marine predator algorithm Modal strain energy Structural damage detection

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A New Fitness Function for Sparse Linear Array Evaluation Based on the Point Spread Function

A New Fitness Function for Sparse Linear Array Evaluation Ba...

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IEEE UFFC Latin America Ultrasonics Symposium (LAUS)

作者： Eduardo de Souza, Julio Cesar Prado, Vander Teixeira Martinez-Graullera, Oscar Higuti, Ricardo Tokio Univ Estadual Paulista UNESP Dept Engn Elect Ilha Solteira Brazil Univ Tecno Fed Parana UTFPR Dept Engn Elect Cornelio Procopio Brazil CSIC Inst Tecno Fis & Informac Madrid Spain

ISBN: (纸本)9781665443593

This work proposes a new fitness function for sparse linear array design using the Point Spread Function (PSF), where the energy and entropy are combined. The arithmetic optimization algorithm is used as a search mechanism, and a new strategy to find sparse linear configurations is proposed. This new strategy eliminates the necessity of penalization functions to control the number of elements in sparse arrays. The algorithm is used to find two sparse linear arrays, one using a fitness function based on the radiation pattern, and the other with the proposed fitness function. Comparing the results, the configuration found using the proposed fitness function presented better lateral resolution and contrast.

关键词： Sparse Linear Array Fitness Function Ultrasound Imaging arithmetic optimization algorithm

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A Novel Hybrid Deep Learning Model for Forecasting Ultra-Short-Term Time Series Wind Speeds for Wind Turbines

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PROCESSES 2023年第11期11卷 3247页

作者： Yang, Jianzan Pang, Feng Xiang, Huawei Li, Dacheng Gu, Bo Powerchina Guiyang Engn Corp Ltd Guiyang 550081 Peoples R China North China Univ Water Resources & Elect Power Sch Elect Engn Zhengzhou 450011 Peoples R China

Accurate forecasting of ultra-short-term time series wind speeds (UTSWS) is important for improving the efficiency and safe and stable operation of wind turbines. To address this issue, this study proposes a VMD-AOA-GRU based method for UTSWS forecasting. The proposed method utilizes variational mode decomposition (VMD) to decompose the wind speed data into temporal mode components with different frequencies and effectively extract high-frequency wind speed features. The arithmetic optimization algorithm (AOA) is then employed to optimize the hyperparameters of the model of the gated recurrent unit (GRU), including the number of hidden neurons, training epochs, learning rate, learning rate decay period, and training data temporal length, thereby constructing a high-precision AOA-GRU forecasting model. The AOA-GRU forecasting model is trained and tested using different frequency temporal mode components obtained from the VMD, which achieves multi-step accurate forecasting of the UTSWS. The forecasting results of the GRU, VMD-GRU, VMD-AOA-GRU, LSTM, VMD-LSTM, PSO-ELM, VMD-PSO-ELM, PSO-BP, VMD-PSO-BP, PSO-LSSVM, VMD-PSO-LSSVM, ARIMA, and VMD-ARIMA are compared and analyzed. The calculation results show that the VMD algorithm can accurately mine the high-frequency components of the time series wind speed, which can effectively improve the forecasting accuracy of the forecasting model. In addition, optimizing the hyperparameters of the GRU model using the AOA can further improve the forecasting accuracy of the GRU model.

关键词： variational mode decomposition arithmetic optimization algorithm gated recurrent unit ultra-short-term forecasting time series wind speed

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Application of AOA algorithm for optimal placement of electric vehicle charging station to minimize line losses

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ELECTRIC POWER SYSTEMS RESEARCH 2023年第PartA期214卷

作者： Kathiravan, K. Rajnarayanan, P. N. Theni Kammavar Sangam Coll Technol Dept Elect & Elect Engn Koduvilarpatti PO Virapandi Tamilnadu India

The conventional transportation system uses fossil fuels and it emits greenhouse gases which affect the envi-ronment. A new kind of transportation must be created immediately because of the growing population. Electric Vehicles (EVs) have less impact on environmental pollution and they will become the base for future transport systems. The battery's specific energy is very low and it needs frequent charging. The long-distance trans-portation using EVs needs charging stations and it leads to placing Electric Vehicle Charging Station (EVCS) in the power grid. The placement of EVCS in the grid increases network losses which have an adverse impact on the grid. In this research work, network loss minimization by the optimum placement of EVCS along with Distributed Generation (DG) is considered. The proposed approach has been validated on the IEEE 33 bus system. The analysis was carried out using the Loss Sensitivity Factor (LSF) approach considering the variable network pa-rameters in the Radial Distribution Network (RDN). The EVCS optimal placement was resolved by arithmetic optimization algorithm (AOA). The results are compared with Particle Swarm optimization (PSO) and Harris Hawks optimization (HHO) approaches. The findings show that the optimal placement of EVCS along with DGs reduces network losses considerably.

关键词： Integrated electric vehicle charging station with distributed generation Loss sensitivity factor Real power loss arithmetic optimization algorithm Radial network

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Optimal control design and management of complete DC- renewable energy microgrid system

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AIN SHAMS ENGINEERING JOURNAL 2023年第5期14卷

作者： Abou El-Ela, A. A. Mosalam, Hanan A. Amer, Ragab A. Menoufia Univ Fac Engn Elect Engn Dept Shibin Al Kawm Egypt Mansoura Higher Inst Engn & Technol Dept Elect Engn Mansoura Egypt

This paper presents an optimal control design for a DC-renewable Microgrid (DC-MG) system optimized by applying the arithmetic optimization algorithm(AOA). This approach is proposed in this study to get the optimal PI controller design parameters for the DC-MG when connecting and disconnecting from the grid. The suggested system comprises a solar photovoltaic system(PVS), an Energy Storage System (ESS), a DC-AC inverter, DC load, LC filter, variable AC load, and a single-phase induction motor. The ESS is used to store energy in hydrogen form and is made up of a fuel cell system (FCS), an electrolyser, and a hydrogen tank module. The results are compared with those obtained from control system design using Particle Swarm optimization(PSO), Cuckoo Search(CS), and Gray Wolf Optimizer (GWO) methodologies for a vari-ety of test situations. The simulated results using AOA are more accurate and have less settling time, rise time, and overshoot compared to other techniques. (c) 2022 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Ain Shams Uni-versity. This is an open access article under the CC BY-NC-ND license (http://***/licenses/ by-nc-nd/4.0/).

关键词： DC-microgrid system Constant power generation Particle Swarm optimization Cuckoo Search technique Gray Wolf optimization arithmetic optimization algorithm

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On the problem formulation for parameter extraction of the photovoltaic model: Novel integration of hybrid evolutionary algorithm and Levenberg Marquardt based on adaptive damping parameter formula

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ENERGY CONVERSION AND MANAGEMENT 2022年 256卷 115403-115403页

作者： Ridha, Hussein Mohammed Hizam, Hashim Q. Mirjalili, Seyedali Othman, Mohammad Lutfi Ya'acob, Mohammad Effendy Ahmadipour, Masoud Ismaeel, Nooruldeen Q. Univ Putra Malaysia Dept Elect & Elect Engn Fac Engn Serdang 43400 Malaysia Univ Putra Malaysia Adv Lightning Power & Energy Res ALPER Fac Engn Serdang 43400 Malaysia Univ Al Mustansiriyah Dept Comp Engn Baghdad 10001 Iraq Torrens Univ Australia Ctr Artificial Intelligence Res & Optimizat Brisbane Qld 4006 Australia Yonsei Univ Yonsei Frontier Lab Seoul South Korea Univ Putra Malaysia Dept Proc & Food Engn Fac Engn Serdang 43400 Selangor Malaysia Univ Teknol MARA Sch Elect Engn Coll Engn Shah Alam 40450 Selangor Malaysia Ibn Sina Univ Med & Pharmaceut Sci Baghdad Iraq

The estimation of the unknown parameters of the photovoltaic (PV) model is crucial for accurately verifying its real performance precisely under a wide range of climatic conditions. This paper presents an approach to determine the nine parameters of the three diode (TD) PV model based on the integration of the guaranteed convergence arithmetic optimization algorithm and Levenberg-Marquardt with adaptive damping nonlinear parameter method named as GCAOA(AdLM). The keystone of the GCAOA(AdLM) model is accomplished by efficaciously enhancing the exploiter-explorer tendency with inclusion of various powerful hybrid strategies in terms the methodology itself. In addition, the objective function is newly designed leveraging on Levenberg-Marquardt with adaptive damping parameter method to accurately determine the initial roots parameters of the TD PV model. The experimental results demonstrate that the proposed GCAOA(AdLM) can reduce the root mean square error (RMSE), mean bias error (MBE), deviation of solar radiation's levels (d(i)), test statistical (TS), and absolute error (AE) to zero and the determination coefficient (R-2) to 1 for all environmental conditions, with statistical reasons and comparisons against well-published approaches available in the literature.

关键词： Photovoltaic cell optimization arithmetic optimization algorithm Newton Raphson Levenberg-Marquardt Lambert W function

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Predicting the consumed heating energy at residential buildings using a combination of categorical boosting (CatBoost) and Meta heuristics algorithms

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JOURNAL OF BUILDING ENGINEERING 2023年 71卷

作者： Dasi, He Ying, Zhang Yang, Boyuan Zhongyuan Univ Technol Sch Energy & Environm Zhengzhou 450007 Peoples R China Xijing Univ Xian 710123 Shaanxi Peoples R China

The main purpose of this study is to investigate the amount of the daily consumed heating at residential buildings. In order to improve the predictions, the Categorical Boosting (CatBoost) method combined with six other meta-heuristics algorithms, and six different hybrid models were made. During the network training, the K-Fold cross validation algorithm has been used to pre-vent overfitting. Also, characteristics of the building as well as the temperature outside the building are considered as the main inputs of the problem. The results showed that the proposed hybrid model can improve the predictions of consumed heating with acceptable accuracy. The results confirm that optimizing the hyper-parameters of Catboost can be very useful in improving the predictions. The results showed that the Catboost model which its hyper-parameters opti-mized by Artificial Bee Colony algorithm, has the best performance among all investigated hybrid models. On the other hand, the hybrid Catboost-ABC model has the weakest performance among all models. For example, based on the test dataset, the R2 values of the hybrid Catboost-AOA model and the hybrid Catboost-ABC model are respectively equal to 0.9851 and 0.9770, which are the highest and lowest values of this index.

关键词： Consumed heating energy Residential building Categorical boosting Meta-heuristic algorithms arithmetic optimization algorithm

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Enhancing LightGBM for Industrial Fault Warning: An Innovative Hybrid algorithm

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PROCESSES 2024年第1期12卷 221页

作者： Li, Shuai Jin, Nan Dogani, Azadeh Yang, Yang Zhang, Ming Gu, Xiangyun 76 Chongmingdao East Rd Qingdao 266000 Peoples R China Ferdowsi Univ Mashhad Fac Agr Dept Agr Econ Mashhad *** Iran

The reliable operation of industrial equipment is imperative for ensuring both safety and enhanced production efficiency. Machine learning technology, particularly the Light Gradient Boosting Machine (LightGBM), has emerged as a valuable tool for achieving effective fault warning in industrial settings. Despite its success, the practical application of LightGBM encounters challenges in diverse scenarios, primarily stemming from the multitude of parameters that are intricate and challenging to ascertain, thus constraining computational efficiency and accuracy. In response to these challenges, we propose a novel innovative hybrid algorithm that integrates an arithmetic optimization algorithm (AOA), Simulated Annealing (SA), and new search strategies. This amalgamation is designed to optimize LightGBM hyperparameters more effectively. Subsequently, we seamlessly integrate this hybrid algorithm with LightGBM to formulate a sophisticated fault warning system. Validation through industrial case studies demonstrates that our proposed algorithm consistently outperforms advanced methods in both prediction accuracy and generalization ability. In a real-world water pump application, the algorithm we proposed achieved a fault warning accuracy rate of 90%. Compared to three advanced algorithms, namely, Improved Social Engineering Optimizer-Backpropagation Network (ISEO-BP), Long Short-Term Memory-Convolutional Neural Network (LSTM-CNN), and Grey Wolf Optimizer-Light Gradient Boosting Machine (GWO-LightGBM), its Root Mean Square Error (RMSE) decreased by 7.14%, 17.84%, and 13.16%, respectively. At the same time, its R-Squared value increased by 2.15%, 7.02%, and 3.73%, respectively. Lastly, the method we proposed also holds a leading position in the success rate of a water pump fault warning. This accomplishment provides robust support for the timely detection of issues, thereby mitigating the risk of production interruptions.

关键词： fault warning LightGBM hybrid algorithm hyperparameter optimization arithmetic optimization algorithm

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