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Prediction of bond strength of reinforced concrete structures based on feature selection and GWO-SVR model

CONSTRUCTION AND BUILDING MATERIALS 2023年第1期400卷

作者： Fan, Congcong Zheng, Yuanxun Wang, Shaoqiang Ma, Junjie Zhengzhou Univ Sch Water Conservancy & Civil Engn Zhengzhou 450001 Peoples R China Zhengzhou Univ Yellow River Lab Zhengzhou 450001 Peoples R China

Bond strength, as a mechanical property of reinforced concrete (RC) structures, is a crucial factor affecting the force characteristics of RC structures. In order to assess the load-bearing and deformation capacity of RC structures, it is essential to develop a model that can accurately predict the bond strength of RC structures. Therefore, a prediction model based on Random Forest (RF) feature selection and grey wolf algorithm optimized (GWO) support vector regression (GWO-SVR) is synthesized in this paper. An extensive database containing 1008 bonded slip test samples was first collected, and various feature parameters were finally preprocessed to filter 935 sets of test data. Next, the optimized support vector machine was trained using the training set data using the GWO-optimized SVR method. Meanwhile, the prediction accuracy of the model was evaluated using the integrated absolute error (IAE), the coefficient of determination (R2), and the mean absolute error (MAE). The model with a good prediction effect can predict the bond strength of unknown tests. When the sample size is small and the number of features is extensive, redundant features must be filtered out before the model prediction. The model prediction after feature selection is significantly better than that before training. The results show that the GWO-SVR model proposed in this study has a higher prediction accuracy than the existing bond strength model. The prediction results of the GWO-SVR model (Test set: R2 = 0.9547, MAE = 1.2487 MPa, IAE = 12.761%) outperform benchmark models such as the multilayer perceptron (MLP) and linear regression (LR). According to the training accuracy of the regression model, it can be found that the GWO-SVR model has good generalization performance (Training set: R2 = 0.9562, MAE = 1.1203 MPa, IAE = 12.182%). In addition, different optimization-seeking algorithms, kernel function types, and machine learning models have significant effects on the prediction results of

关键词： Reinforced concrete structure Bond tests Random forest feature selection grey wolf algorithm Support vector regression prediction

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Single- and Multi-Objective Optimization Frameworks of Shape Design of Tubular Linear Synchronous Motor

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ENERGIES 2023年第5期16卷 2409页

作者： Mahdy, Araby Shaheen, Abdullah El-Sehiemy, Ragab Ginidi, Ahmed Al-Gahtani, Saad F. Suez Univ Fac Engn Dept Mech Engn Suez 43533 Egypt Suez Univ Fac Engn Dept Elect Engn Suez 43533 Egypt Kafrelsheikh Univ Fac Engn Dept Elect Engn Kafrelsheikh 33516 Egypt King Khalid Univ Fac Engn Dept Elect Power Engn Abha 61421 Saudi Arabia

The shape design of the Tubular Linear Synchronous Motor (TLSM) is a critical engineeri ng optimization problem which was handled as single- and multi-objective optimization frameworks. However, the different practical constraints for the TLSM design must be efficiently guaranteed. This paper proposes a developed multi-objective shape design of the TLSM to maximize the operating force and minimize the flux saturation. In this regard, a Multi-objective grey wolf Optimizer (MGWO) is developed, including an outside archive with a predetermined size that is integrated for storing and retrieving Pareto optimal solutions. Using this knowledge, the grey wolf social structure would then be established, and, in the multi-objective searching environments, grey wolf hunting behavior would then be replicated. The superiority and effectiveness of the developed MGWO is assessed in comparison to the Multi-objective Flower Pollination algorithm (MFPA), Multi-objective Lichtenberg algorithm (MOLA), and Multi-objective Grasshopper Optimization algorithm (MGOA). The outcomes illustrate that the developed MGWO provides an average improvement of 73.46%, 19.07%, and 15.15% compared to MFPA, MOLA, and MGOA, respectively. The validation of the developed MGWO is extended for a multi-objective form of welded beam design (WBD) by simultaneously minimizing the deflection and the manufacturing costs. Similar findings are obtained with different reference points, the developed MGWO provides an average improvement of 2.8%, 0.7%, and 3.04% compared to MFPA, MOLA, and MGOA, respectively.

关键词： grey wolf algorithm pareto archive multi-objective shape design of tubular linear synchronous motor maximizing the operating force minimizing the flux saturation

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Carbon market price prediction based on sequence decomposition-reconstruction-dimensionality reduction and improved deep learning model

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JOURNAL OF CLEANER PRODUCTION 2023年 425卷

作者： Wang, Huaqing Tan, Zhongfu Zhang, Amin Pu, Lei Zhang, Junjian Zhang, Zhi North China Elect Power Univ Sch Econ & Management Baoding 102206 Peoples R China Yanan Univ Yanan 716000 Shaanxi Peoples R China Cent Southern China Elect Power Design Inst Co Ltd China Power Engn Consulting Grp Wuhan 430064 Peoples R China Shanxi Elect Power Co SGCC Econ & Elect Res Inst Taiyuan 30021 Peoples R China

Identifying carbon market price signals and accurately predicting carbon prices is of great significance in the development of carbon markets and carbon trading management. In this study, a fast ensemble empirical mode decomposition (FEEMD) method was used to analyze the fluctuation patterns of carbon market prices and obtain their characteristic components. Then the characteristic components of carbon prices were reconstructed using sample entropy theory (SRSE), and a partial autocorrelation analysis (PACF) technique was used to screen the data points exerting large impacts on carbon market prices. The FEEMD-SRSE-PACF-ILSTM hybrid model constructed. The main innovation points are as follows: The FEEMD is proposed to solves the common modal aliasing problem during carbon price decomposition and improves the efficiency of the carbon price sequence decomposition;the sample entropy theory and partial autocorrelation analysis methods are applied to reconstruct the characteristic components of carbon prices;the LSTM neural network model was optimized by the grey wolf algorithm to enhance its stability and adaptability. The analyzed price changes of large regional carbon markets in China, the European Union, and the United States can be effectively used to predict prices in the carbon market and guide its participants towards low-carbon investment and trading.

关键词： Carbon price prediction Sequence reconstruction grey wolf algorithm Enhanced deep learning model

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A novel economic dispatch in the stand-alone system using improved butterfly optimization algorithm

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ENERGY STRATEGY REVIEWS 2023年 49卷

作者： Alhasnawi, Bilal Naji Jasim, Basil H. Bures, Vladimir Sedhom, Bishoy E. Alhasnawi, Arshad Naji Abbassi, Rabeh Alsemawai, Majid Razaq Mohamed Siano, Pierluigi Guerrero, Josep M. Al Furat Al Awsat Tech Univ Al Samawah Tech Inst Dept Elect Tech Al Samawah 66001 Iraq Basrah Univ Elect Engn Dept Basrah 61001 Iraq Univ Hradec Kralove Fac Informat & Management Hradec Kralove 50003 Czech Republic Mansoura Univ Fac Engn Elect Engn Dept Mansoura 35516 Egypt Al Muthanna Univ Coll Educ Pure Sci Dept Dept Biol Samawah 66001 Iraq Univ Hail Coll Engn Dept Elect Engn Hail City 81451 Saudi Arabia Imam Jaafar Al Sadiq Univ Coll Informat Technol Dept Comp Tech Engn Al Muthanna 66002 Iraq Salerno Univ Management & Innovat Syst Dept I-84084 Salerno Italy Univ Johannesburg Dept Elect & Elect Engn Sci ZA-2006 Johannesburg South Africa Aalborg Univ AAU Energy Dept Ctr Res Microgrids CROM DK-9220 Aalborg Denmark

Distributed renewable energy systems are now widely installed in many buildings, transforming the buildings into 'electricity prosumers'. Additionally, managing shared energy usage and trade in smart buildings continues to be a significant difficulty. The main goal of solving such problems is to flatten the aggregate power consumption-generation curve and increase the local direct power trading among the participants as much as possible. This study provides a coordinated smart building energy-sharing concept for smart neighborhood buildings integrated with renewable energy sources and energy storage devices within the building itself. This neighborhood energy management model's primary objective is to reduce the total power cost of all customers of smart buildings in the neighborhood by increasing the use of locally produced renewable energy. In the first stage, a group of optimum consumption schedules for each HEMS is calculated by an Improved Butterfly Optimization algorithm (IBOA). A neighborhood energy management system (NEMS) is established in the second stage based on a consensus algorithm. A group of four smart buildings is used as a test system to evaluate the effectiveness of the suggested neighborhood smart building energy management model. These buildings have varying load profiles and levels of integration of renewable energy. In this paper, the proposed framework is evaluated by comparing it with the grey wolf optimization (GWO) algorithm and W/O scheduling cases. With applying GWO, the total electricity cost, peak load, PAR, and waiting time are improved with 3873.723 cents, 21.6005 (kW), 7.162225 (kW), and 87 s respectively for ToU pricing and 11217.57 (cents), 18.0425(kW), 5.984825 (kW), and 98 s respectively for CPP tariff. However, using the IBOA Improves the total electricity cost, peak load, PAR, and waiting time by 3850.61 (cents), 20.1245 (kW), 6.7922 (kW), and 53 s respectively, for ToU and 10595.8 (cents), 17.6765(kW), 5.83255(kW), and 74 s f

关键词： Butterfly optimization algorithm grey wolf algorithm Solar energy Wind turbine Demand shifting

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A multi-agent system for FJSP with setup and transportation times

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EXPERT SYSTEMS WITH APPLICATIONS 2023年第1期216卷

作者： Pal, Manojkumar Mittal, Murari Lal Soni, Gunjan Chouhan, Satyendra S. Kumar, Manish MNIT Jaipur Dept Mech Engn Jaipur 302017 India MNIT Jaipur Dept Comp Sci Engn Jaipur 302017 India

Flexible job shops are quite common in manufacturing, where the machines can perform a variety of operations on a job. The setup and transportation times are quite significant in flexible job shops, which recently have attracted attention. This paper deals with the problem of scheduling of flexible job shops, termed FJSP, with setup and transportation times. Several metaheuristics approaches have been proposed for the problem assuming centralized decision-making. The centralized approaches, however, may not be suitable for large complex problems due to higher computational effort and slower convergence. The decentralized approaches are more suitable for such problems. To the best of authors' knowledge, no work has been reported towards developing a decentralized approach for FJSP with setup and transportation times. To fill this gap a multi-agent system, a popular decentralized approach, is developed for FJSP with setup and transportation time. The performance of the proposed approach is compared with the three state-of-the-art (centralized) approaches by solving 20 problem instances. The proposed approach has been found to produce schedules with makespan lower (maximum of 35% and an average of 10.27%) than the comparison approaches. The algorithm has also been found to converge faster than the comparison algorithms.

关键词： Flexible job shop scheduling Multi-agent system Setup time Transportation time grey wolf algorithm Decentralized system

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Study on Crop Planting Strategies Based on Multi-Objective Optimization and Uncertainty Management 24

Study on Crop Planting Strategies Based on Multi-Objective O...

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Proceedings of the 2024 4th International Conference on Computational Modeling, Simulation and Data Analysis

作者： Xianyi Bao Xiangwei Dang Zhengjia Luo Xin Gan School of information North China University of Technology Beijing Beijing China

ISBN: (纸本)9798400711831

According to the current agricultural research in the mountainous areas of North China, there are significant deficiencies in coping with uncertain factors, especially planting planning strategies under the dual impact of climate change and market fluctuations. In this paper, the traditional linear programming model is improved, and an innovative multi-objective optimization model is proposed, which combines efficient algorithm application and uncertainty processing. Firstly, Gurobi optimizer is used to deal with the Optimization problem accurately, and then the method of combining Latin Hypercube Sampling (LHS) and Particle Swarm Optimization (PSO) is adopted. To deal with uncertainty in multidimensional parameter space. LHS is used to generate uniformly distributed initial samples to improve the search efficiency of PSO. At the same time, a rapid precursor elimination technique based on probabilistic distance is combined to further optimize the solution selection process. Finally, we use the grey wolf optimization algorithm to solve the multi-objective programming model and get the Pareto optimal solution set. In this method, the optimal planting scheme is formulated by considering the uncertainty factors. The improved model can incorporate many uncertainties and crop correlations, and project planting plans for the next few years with the goal of maximizing returns. The innovation of the model is to put forward a multi-stage optimization framework to provide new ideas and practical solutions for agricultural fine management and sustainable development.

关键词： grey wolf algorithm

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Application of digital technology in environmental art design

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APPLIED MATHEMATICS AND NONLINEAR SCIENCES 2023年第1期9卷

作者： Xu, Xuanyin Henan Univ Engn Sch Art & Design Zhengzhou 450052 Henan Peoples R China

In recent years, digital technology has rapidly penetrated various industries and brought various opportunities for developing these industries. Based on two digital technologies, BP neural network and the Gray wolf algorithm, this article analyzes the application of digital technology in environmental art design at Xi'an Eurasian National University, taking environmental art design as an example. The article uses these two algorithms to analyze the site and design principles of the campus, followed by detailed design and consideration of the campus environment design, mainly from greenery design, groundscape and road pavement design, and campus landscape lighting design. The analysis of the collected data in this paper shows that students' satisfaction with the central teaching area is generally low, at around 20%, and the satisfaction with the northwest teaching area is the highest, all above 40%, up to 61%. The students' satisfaction with the plant landscape on the campus was generally at a medium level. However, the satisfaction level for loblolly pine is not very high, and overall is around 30%, while the satisfaction level for the cedar is high, and everyone's satisfaction is higher than 60%. Therefore, this paper takes the students' satisfaction with the campus landscape environment into consideration in the landscape design, which is conducive to achieving the perfect integration of landscape art design and ecological environment and achieving the realistic goal of both aesthetic and environmental protection.

关键词： BP neural network grey wolf algorithm Environmental art Digital Campus landscape

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Separation Method of Main and Foreign Water for the Measuring Weirs of Danjiangkou Earth-Rock Dam

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WATER 2022年第22期14卷 3620-3620页

作者： Fang, Weihua Zhang, Weiping Zhang, Chenghan Xie, Zhiwen Yu, Tiantang Minist Water Resources Nanjing Res Inst Hydrol & Water Conservat Automat Nanjing 210012 Peoples R China Hanjiang Grp Corp Ltd Wuhan 430048 Peoples R China Hanjiang Grp Corp Ltd Danjiangkou Hydroelect Power Plant Danjiangkou 442700 Peoples R China Hohai Univ Dept Engn Mech Nanjing 211100 Peoples R China

Reservoir water and rainfall are the two main factors affecting the seepage of earth-rock dams, but the seepage measurement includes the seepage discharge caused by the reservoir water and rainfall. Only by obtaining the seepage discharge caused by the reservoir water can we evaluate the seepage safety state. In this paper, a statistical model for seepage monitoring of earth-rock dams is established. Normal distribution function and Rayleigh distribution function are used as the lag functions of reservoir water and rainfall, respectively. The grey wolf algorithm is used to solve the lag days, and the partial least square method is adopted to solve the regression coefficient of the statistical model. Then, the reservoir water (main water) and rainfall infiltration (foreign water) parts of the measuring weir are separated with the statistical model. The developed method is used to separate the main and foreign water parts of the three measuring weirs of the Danjiangkou earth-rock dam. The results show that the overall accuracy of the models is high (the multi-correlation coefficients are about 0.95), and the separated main and foreign water seepage discharge conforms to the seepage law of earth-rock dam, which verifies the effectiveness of the method.

关键词： seepage statistical model lag function partial least square method grey wolf algorithm measuring weir reservoir water rainfall infiltration Danjiangkou earth-rock dam

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Control Strategy and Parameter Optimization Based on Grid Side Current Dynamic Change Rate for Doubly-Fed Wind Turbine High Voltage Ride Through

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ENERGIES 2022年第21期15卷 7977页

作者： Deng, Jun Qi, Zhenghao Xia, Nan Gao, Tong Zhang, Yang Duan, Jiandong State Grid Shaanxi Elect Power Co Ltd Power Res Inst Xian 710100 Peoples R China Xian Univ Technol Sch Elect Engn Xian 710000 Peoples R China

High voltage ride through (HVRT) control strategies for doubly-fed induction wind turbines (DFIG) have mostly focused on the rotor side converter, while the reactive power compensation capability of the grid side converter and the impact of grid side converter current transients have often been ignored on the DC bus voltage and reactive power. Therefore, a control strategy based on grid side current dynamic change characteristics is proposed, which resets the reference values of the grid side active and reactive currents for wind turbine HVRT to ensure partial absorption of reactive power on the grid side. Secondly, the key parameter in the proposed control strategy is optimization to get the most suitable DC bus voltage value with the grey wolf algorithm. Finally, the grid-integrated wind turbine simulation model is built on the MATLAB/SIMULINK and RT-Lab platforms. The simulation test results show that the proposed HVRT control strategy and its parameter optimization method are effective, DFIG can achieve HVRT when the wind turbine voltage rises to 1.3 pu.

关键词： doubly-fed induction wind turbines current change rate grid side current high voltage ride through grey wolf algorithm

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Metaheuristic algorithm Based Maximum Power Point Tracking Technique Combined With One Cycle Control For Solar Photovoltaic Water Pumping Systems

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FRONTIERS IN ENERGY RESEARCH 2022年 10卷

作者： Nisha, R. Sheela, K. Gnana PJ Abdul Kalam Technol Univ Dept Elect & Elect Engn TIST Trivandrum India P J Abdul Kalam Kerala Technol Univ Dept Elect & Commun Engn TIST Trivandrum India

Solar photovoltaic technology has become eminent in the world because of its clean and abundant nature and can be effectively used for water pumping applications. A maximum power point tracking method is indispensable to achieve the best benefit from photovoltaic systems. Conventional maximum power tracking methods become successful only under uniform irradiance conditions and fail to track the maximum power under partial shading conditions (PSC). Hence, nature-inspired metaheuristic algorithms were proposed to track the optimum power under varying environmental conditions. This study proposes a method for improving the performance of the nature-inspired maximum power point tracking algorithms by using a nonlinear control technique called one cycle control. Based on the duty cycle obtained from the tracking algorithm the one cycle control technique generates pulses for the DC-DC converter, which is connected to a brushless DC motor pump system through a voltage source inverter. The performance of the proposed system under various PSCs is validated using the Cuckoo search, particle swarm optimization, and grey wolf algorithms. Simulation results for various shading patterns prove the supremacy of the system with respect to convergence speed, tracking efficiency, robustness, steady-state oscillations at maximum power point, and initial exploration oscillations in comparison with systems without one cycle control. In addition, the introduction of a KY converter as a DC-DC converter reduces the output voltage ripple in the system. Thus, the proposed system with one cycle control overcomes the disadvantages of the existing methods and can be effectively utilized for water pumping applications.

关键词： brushless DC motor cuckoo search algorithm grey wolf algorithm maximum power point tracking particle swarm optimisation one cycle control

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