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5th International Conference on Computing, Networks and Internet of Things (CNIOT)

作者： Li, Yihang Lv, Zhimin Univ Sci & Technol Beijing Collaborat Innovat Ctr Steel Technol Beijing Peoples R China

ISBN: (纸本)9798400716751

A recently developed swarm intelligence method that converges more quickly than some existing algorithms is called the dung beetle optimization algorithm (DBO). However, the algorithm still suffers from the disadvantage that the initial population is not uniform and easily falls into the global optimal solution. In this paper, a multistrategy fusion dung beetle optimization algorithm (MSFDBO) is proposed which is inspired by the sine search algorithm, whale optimization algorithm, and reverse learning algorithm. First, the initial population of dung beetles is initialized using the singer chaos function to obtain a more uniform initial population. In view of the rolling behavior of dung beetles, adaptive coefficients and sine search strategies are introduced to enhance the search range of the algorithm, and the amplitude of the sine function is changed through the adaptive coefficients to balance the local and global searches of the algorithm. In view of the foraging behavior, egg-laying behavior and stealing behavior of dung beetles, an adaptive spiral search strategy is introduced to improve the optimization ability of the algorithm in the solution space. For the optimal solution obtained in each iteration, a convex lens imaging reverse learning strategy is used to perturb the optimal solution to avoid the dung beetle population from falling into a local optimum during the iteration process. By comparing the benchmark test function and the Wilcoxon rank sum test, MSFDBO shows better convergence performance, optimization performance, and robust performance.

关键词： Dung beetle optimizer singer mapping sine optimization algorithm whale optimization algorithm swarm intelligence optimization algorithm reverse learning strategy

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A coordination of some basic heuristics using Q-learning for global optimization

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OPSEARCH 2024年 1-38页

作者： Mandal, Sudip Kumar Halim, Kazi Abdul Maiti, Manas Kumar Panskura Banamali Coll Dept Comp Sci Panskura RS Medinipur 721152 West Bengal India Panskura Banamali Coll Dept Math Panskura RS Purba Medinipur 721152 West Bengal India Mahishadal Raj Coll Dept Math Purba Medinipur 721628 West Bengal India

It is known that none of the existing heuristics on single-objective continuous optimization problems (SOCOPs) claims 100% success, rather unsuccessful on a small subset of the target class. However, the union of the solvable problems of a few heuristics can cover the whole class of SOCOPs. In this study, some basic heuristics of different categories on SOCOPs-particle swarm optimization (PSO), whale optimization algorithm (WOA), and artificial bee colony (ABC) are coordinated using Q-learning to develop a heuristic on the same target class having 100% success rate. The approach consists of two phases, where in the first phase all three algorithms are executed independently for some iterations on separate initial populations and according to their performance on the target problem, a Q-table is prepared. At the end of the first phase, three populations are combined and a population of the same size is prepared by selecting the better individuals, which is taken as the population of the next phase. In the second phase, one algorithm is selected concerning their performance reflected in the Q-table and is applied to improve the population. According to its performance, the corresponding entry of the Q-table is updated. This process is continued for another number of iterations to obtain the solution with the desired accuracy. The algorithm is tested and trusted against a large set of test instances and it is noted that it provides 100% accurate (desired accuracy) results for all the considered test instances. Its superiority compared to some state-of-the-art algorithms is also established using statistical tools.

关键词： Particle swarm optimization whale optimization algorithm Artificial bee colony Reward table Q-table

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Field Strength Prediction in High-Speed Train Carriages Using a Multi-Neural Network Ensemble Model with Optimized Output Weights

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APPLIED SCIENCES-BASEL 2025年第5期15卷 2709-2709页

作者： Fang, Zhou Zhao, Hengkai Feng, Yichen Wu, Yating Sun, Yanqiong Yang, Qi Zheng, Guoxin Shanghai Univ Key Lab Specialty Fiber Opt & Opt Access Networks Joint Int Res Lab Specialty Fiber Opt & Adv Commun Shanghai 200444 Peoples R China CASCO Signal Ltd Shanghai 200072 Peoples R China

Accurate path loss prediction within train carriages is crucial for deploying base stations along high-speed railway lines. The field strength at receiving points inside carriages is influenced by outdoor signal transmission, penetration through window glass, and multiple reflections within the carriage, making it challenging for traditional models to predict the field strength distribution accurately. To address this issue, this paper proposes a machine learning-based path loss prediction method that incorporates ensemble techniques of multiple neural networks to enhance prediction stability and accuracy. The whale optimization algorithm (WOA) is used to optimize the output weight configuration of each neural network in the ensemble model, thereby significantly improving the overall model performance. Specifically, on the test set, the WOA-optimized ensemble model reduces RMSE by 1.47 dB for CI, 0.47 dB for CNN, 0.93 dB for RNN, 1.38 dB for GNN, 0.1 dB for Transformer, 0.09 dB for AutoML, 0.33 dB for the GA-optimized ensemble model, and 0.18 dB for the PSO-optimized ensemble model.

关键词： high-speed train path loss multi-neural network ensemble whale optimization algorithm

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Adaptive Virtual Inertia in Doubly Fed Induction Generators for Enhanced Frequency Regulation

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IEEE ACCESS 2025年 13卷 55616-55626页

作者： Ding, Xianxu Zhang, Yihong Donghua Univ Coll Informat Sci & Technol Shanghai 201620 Peoples R China Minist Educ Engn Res Ctr Digitized Text & Apparel Technol Shanghai 201620 Peoples R China

The integration of large-scale renewable energy sources, such as wind and photovoltaic power, into the electrical grid significantly reduces system inertia, thereby diminishing the grid's capability to resist frequency deviations. During the frequency regulation process, the fixed virtual inertia provided by Doubly Fed Induction Generators is inadequate to support the system inertia requirements under large-scale integration of renewable energy. To address this issue, it is necessary to enhance the virtual inertia control of Doubly Fed Induction Generators by developing a fuzzy adaptive virtual control strategy. Under varying wind speeds and frequency fluctuations, the wind turbine is capable of adaptively adjusting the virtual inertia coefficient, enabling the renewable energy system to actively and adaptively participate in frequency regulation. Moreover, the pitch control strategy in the high wind speed region can be employed to provide additional frequency regulation power for wind turbines. During the frequency regulation process, the output relationship between the wind power system and the energy storage system should be carefully considered to achieve coordinated operation between the two systems. The whale optimization algorithm is utilized to determine the optimal output ratio, ensuring enhanced coordination and regulation performance. In a wind-storage frequency domain system model developed using MATLAB/Simulink simulation software, this strategy improves performance by at least 30% across different wind speed intervals compared to traditional control strategies, with enhancements of 38%, 24%, and 17% under various step loads. This approach effectively enhances the frequency characteristics of renewable energy systems.

关键词： Frequency control Wind turbines Wind speed Wind power generation Energy storage Renewable energy sources Power system stability Fluctuations Rotors Generators Frequency regulation fuzzy control inertia and droop control whale optimization algorithm wind-storage system

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Improved exponential smoothing grey-holt models for electricity price forecasting using whale optimization

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METHODSX 2024年 13卷 102926页

作者： Dibomaa, Benjamin Salomon Sapnkena, Flavian Emmanuel Hamaidi, Mohammed Wang, Yong Noumo, Prosper Gopdjim Tamba, Jean Gaston Univ Ebolowa Higher Inst Transport Logist & Commerce POB 22 Ambam Cameroon Univ Douala Univ Inst Technol POB 8698 Douala Cameroon Energy Insight Tomorrow Today POB 2043 Douala Cameroon Ziane Achour Univ Djelfa Fac Exact Sci & Comp Sci Dept Math Djelfa Algeria Southwest Petr Univ Sch Sci Chengdu 610500 Sichuan Peoples R China

This study introduces a ground-breaking approach, the whale optimization algorithm (WOA)based multivariate exponential smoothing Grey-Holt (GMHES) model, designed for electricity price forecasting. Key features of the proposed WOA-GMHES(1,N) model include leveraging historical data to comprehend the underlying trends in electricity prices and utilizing the WOA algorithm for adaptive optimization of model parameters to capture evolving market dynamics. Evaluating the model on authentic high- and low-voltage electricity price data from Cameroon demonstrates its superiority over competing models. The WOA-GMHES(1,N) model achieves remarkable performance with RMSE and SMAPE scores of 12.63 and 0.01 %, respectively, showcasing its accuracy and reliability. Notably, the model proves to be computationally efficient, generating forecasts in < 1.3 s. Three key aspects of customization distinguish this novel approach: center dot The WOA algorithm dynamically adjusts model parameters based on evolving electricity market dynamics. center dot The model employs a sophisticated GMHES approach, considering multiple factors for a comprehensive understanding of price trends. center dot The WOA-GMHES(1,N) model stands out for its computational efficiency, providing rapid and precise forecasts, making it a valuable tool for time-sensitive decision-making in the energy sector.

关键词： Electricity price forecasting whale optimization algorithm Grey modelling Computational efficiency

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An optimized dynamic model improved deep discriminative transfer learning network for fault detection in rotation vector reducers

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RELIABILITY ENGINEERING & SYSTEM SAFETY 2024年 251卷

作者： Wang, Hui Wang, Shuhui Yang, Ronggang Xiang, Jiawei Wenzhou Univ Coll Mech & Elect Engn Wenzhou 325035 Peoples R China

In recent years, recognized fault types using artificial intelligence (AI) models has gradually become one of the mainstream directions in the field of mechanical fault diagnosis. However, it is very difficult to obtain relatively complete fault samples, which limits the application of AI models for complex mechanical systems, such as rotation vector (RV) reducers. To address this issue, physical model-based fault sample generation methods attracted many attentions but still an open problem: the difference in fault samples between numerical simulation and measurement of a physical system needs to be well decreased. Therefore, this article proposes an improved deep discriminative transfer learning network (IDDTLN) for RV reducer fault diagnosis. The presented network is driven by an optimized dynamic model. Firstly, the measurement normal signal and whale optimization algorithm (WOA) are utilized to update the dynamic model parameters of the RV reducer. Secondly, the updated numerical model is employed to calculate simulation fault samples. Finally, simulation samples and unlabeled measurement fault samples are used to train IDDTLN. The trained IDDTLN can accurately identify the unknow measurement fault samples. The data obtained from RV reducer test rigs are utilized to explore the feasibility of the proposed method, and the classification accuracies reach 99.8 %.

关键词： RV Reducer Physical model Parameter update whale optimization algorithm Transfer learning network Fault detection

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Optimized smart home energy management system: Reducing grid consumption and costs through real-time pricing and hybrid architecture

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CASE STUDIES IN THERMAL ENGINEERING 2024年 64卷

作者： Ali, Abdelrahman O. Elmarghany, Mohamed R. Hamed, Ahmed M. Sabry, Mohamed Nabil Abdelsalam, Mohamed M. Mansoura Univ Fac Engn Mech Power Engn Dept Mansoura Egypt Mansoura Natl Univ Fac Engn Mansoura Egypt New Mansoura Univ Fac Engn Mech Engn Dept New Mansoura Egypt Ain Shams Univ Mech Power Engn Dept Cairo Egypt Mansoura Univ Fac Engn Comp Engn & Control Syst Dept Mansoura Egypt

Utility authorities utilize various methods to promote end-user energy conservation, including higher tariff rates and demand response (DR) strategies. This paper investigates an Optimized Smart Home Energy Management System (OSHEMS) designed to minimize grid dependence and energy bills while ensuring reliable load delivery. A hybrid architecture prototype was implemented, integrating a photovoltaic (PV) array, battery storage, and the electrical grid. The system combines Maximum Power Point Tracking (MPPT) solar chargers and Pure Sine Wave (PSW) inverters for efficient energy management. The Home Energy Management whale optimization algorithm (HEMWOA) was employed to optimize energy usage and achieve cost reduction while enhancing user comfort. Real-time pricing (RTP) tariffs incentivizing flexible energy consumption during peak hours were incorporated. OSHEMS manages, schedules, and monitors energy sources and appliances, determining the optimal consumption mix. Experimental results demonstrate a significant decrease in grid reliance (46.6 %) and energy costs (57.7 %) compared to nonscheduling scenarios. These findings highlight the potential of OSHEMS in promoting sustainable and cost-effective energy consumption in smart homes.

关键词： Photovoltaic (PV) Home energy management system whale optimization algorithm Real time price scheme Hybrid experimental prototype

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WOA-tuned supertwisted synergetic control of multipurpose on-board charger for G2V/V2G/V2V operational modes of electric vehicles

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CONTROL ENGINEERING PRACTICE 2025年 154卷

作者： Adil, Hafiz Mian Muhammad Khan, Hassan Abbas Lahore Univ Management Sci LUMS Dept Elect Engn Lahore 54792 Pakistan

On-board chargers within electric vehicles (EVs) must efficiently manage grid-to-vehicle (G2V), vehicle- to-grid (V2G), and vehicle-to-vehicle (V2V) modes for sustainable EV operation. This paper introduces a modified hybrid nonlinear control approach that utilizes the whale optimization algorithm-tuned supertwisted synergetic (WOA-ST-syn) technique fora multipurpose on-board charger (MP-OBC). The whale optimization algorithm(WOA) adjusts the parameters of supertwisted synergetic controller using the integral time absolute error, reducing the need for exhaustive trial-and-error adjustments. The controller employs the state space model of a two-stage on-board electric vehicle charging system, ensuring stability through the Lyapunov stability criterion. Simulations in MATLAB/Simulink evaluate the performance of the proposed controller across various operational modes, testing robustness against varying load currents and mode-switching conditions. Results indicate significant improvements over state-of-the-art nonlinear controllers, with minimal chattering, shortest rise time (0.0007 s for AC-DC, 1.5520 s for DC-DC), fastest settling time (0.0447 s for AC-DC, 2.0550 s for DC-DC), and minimal steady-state error (0.0010% for AC-DC, 0.0004% for DC-DC). Controller Hardware-in-the-Loop (C-HIL) experiments were also performed to confirm the real-time applicability of the controller.

关键词： whale optimization algorithm Grid-to-vehicle Vehicle-to-grid Vehicle-to-vehicle Robust nonlinear control Controller Hardware-in-the-Loop

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A WOA-SVMD and multi-scale CNN-transformer method for fault diagnosis of motor bearing

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MEASUREMENT & CONTROL 2025年

作者： Xu, Zejiang Tao, Jili Hu, Yuanmin Feng, Han Ma, Longhua ZheJiang Sci Tech Univ Sch Informat Sci & Engn Hangzhou Zhejiang Peoples R China NingboTech Univ Sch Informat Sci & Engn Ningbo Zhejiang Peoples R China Zhejiang CRRC Elect Vehicle Co Ltd Ningbo Zhejiang Peoples R China

Long-term operation of electric motor under complex and extreme conditions can lead to unpredictable failures, therefore, accurate diagnosis of electric motor failures has been valued by scholars and engineers. However, the noise in the vibration signals of the motor's rolling bearings has a profound impact on the diagnostic performance of a model in the process of feature extraction and fault classification. Aiming at vibration signal denoising and accurate fault classification, in this study, a novel method based on WOA-SVMD and multi-scale CNN-Transformer is proposed. Firstly, the whale optimization algorithm (WOA) is employed to obtain the optimal parameters of Successive Variational Mode Decomposition (SVMD), which is then used to decompose the signal into Intrinsic Mode Functions (IMFs). Secondly, uncorrelated components are removed based on the correlation coefficient method, the left IMFs are reconstructed into new signals. Thirdly, local and global features of the signal are adequately extracted using multi-scale Convolutional Neural Network (CNN) and transformer. Finally, fault type is classified using the softmax function. The experimental results show that the proposed method can effectively reduce the noise interference, and the accuracy of fault diagnosis reaches 99.24% on the CWRU dataset and 99.68% on the PU dataset.

关键词： Fault diagnosis whale optimization algorithm successive variational mode decomposition convolutional neural network transformer

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Discharge Voltage Prediction Model of Batteries in Different Degradation States Based on IWOA-ATCN

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PROCESSES 2025年第1期13卷 46-46页

作者： Yang, Jingwei Chen, Yitong Huang, Qiang Wu, Guilong Liu, Lin Yang, Zhimin Huang, Yu China Southern Power Grid Power Dispatching & Control Ctr Guangzhou 510525 Peoples R China

As a key technology for future decarbonization, storage batteries are widely used in areas such as electric vehicles and power systems. However, battery aging inevitably limits their broader application. To address the low accuracy in predicting discharge voltage under different aging states, this paper proposes the IWOA-ATCN method based on a TCN model with a sliding window mechanism. First, the improved whale optimization algorithm (IWOA) is employed to optimize the hyperparameters in the TCN model, including window size and sliding step, to obtain the optimal sample structure. Then, the temporal attention mechanism is introduced into the TCN model to accurately capture the temporal correlation of discharge voltage, thereby improving the prediction accuracy of long time series data. Finally, the model is tested on the NASA dataset with an RMSE of 0.0072, MAE of 0.0046, and R2 of 0.9984. The test results on the PL Sample dataset showed RMSE of 0.0081, MAE of 0.0040, and R2 of 0.9983. It is indicated that the prediction accuracy and stability of the IWOA-ATCN model are higher than other models, such as BP, RNN, and LSTM.

关键词： storage battery voltage prediction degraded state whale optimization algorithm TCN

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