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Artificial Intelligence in Agriculture 2025年第3期15卷 508-525页

作者： Yang, Hang Feng, Qi Xia, Shibin Wu, Zhenbin Zhang, Yi Institute of Hydrobiology Chinese Academy of Sciences Wuhan430072 China Hubei Key Laboratory of Mineral Resources Processing and Environment Wuhan University of Technology Wuhan430070 China University of Chinese Academy of Sciences Beijing100049 China

The integration of artificial intelligence (AI) in aquaculture has been identified as a transformative force, enhancing various operational aspects from water quality management to genetic optimization. This review provides a comprehensive synthesis of recent advancements in AI applications within the aquaculture sector, underscoring the significant enhancements in production efficiency and environmental sustainability. Key AI-driven improvements, such as predictive analytics for disease management and optimized feeding protocols, are highlighted, demonstrating their contributions to reducing waste and improving biomass outputs. However, challenges remain in terms of data quality, system integration, and the socio-economic impacts of technological adoption across diverse aquacultural environments. This review also addresses the gaps in current research, particularly the lack of robust, scalable AI models and frameworks that can be universally applied. Future directions are discussed, emphasizing the need for interdisciplinary research and development to fully leverage AI potential in aquaculture. This study not only maps the current landscape of AI applications but also serves as a call for continued innovation and strategic collaborations to overcome existing barriers and realize the full benefits of AI in aquaculture. © 2024 The Authors

关键词： Aquaculture AI integration Predictive analytics Sustainable aquaculture AI disease management AI feeding optimization AHPND Acute Hepatopancreatic Necrosis Disease AI Artificial Intelligence AlphaFamImpute Alpha Family Impute AMA-Net Appearance-Motion Architecture Network ANN Artificial Neural Network ASST Acoustic Spectrogram Signal Transformer AUC Area Under the Curve BFT Biofloc Technology BP Back Propagation BPNN Back Propagation Neural Network CBAM Convolutional Block Attention Module CNN Convolutional Neural Network CVS Computer Vision System DM-Count Density Map Count DCNN Deep Convolutional Neural Network DL Deep Learning DO Dissolved Oxygen EEMD Ensemble Empirical Mode Decomposition EER Equal Error Rate FLOPs Floating Point Operations Per Second FPS Frames Per Second FFINet Feature Fusion Interactive Network FT-IR Fourier Transform Infrared Spectroscopy GA Genetic algorithm GBLUP Genomic Best Linear Unbiased Prediction GELU Gaussian Error Linear Unit GIS Geographic Information System GRU Gated Recurrent Unit GWAS Genome-Wide Association Studies HPO Hyperparameter optimization HSI Hyperspectral Imaging IoT Internet of Things KHV Koi Herpes Virus LCA Linear Calibration algorithm LCNN Local Convolutional Neural Network LDA Linear Discriminant Analysis LGBM Light Gradient Boosting Machine LIA Linear Integration algorithm LSTM Long Short-Term Memory MAE Mean Absolute Error mAP Mean Average Precision MCC Matthews Correlation Coefficient ML Machine Learning ML-KAML Machine Learning - Kernel Additive Model Learning MLP Multilayer Perceptron MSE Mean Squared Error NB Naïve Bayes NIR Near-Infrared NMR Nuclear Magnetic Resonance NSE Nash-Sutcliffe Efficiency PBLUP Pedigree-Based Best Linear Unbiased Prediction PCA Principal Component Analysis PCC Pearson Correlation Coefficient PNN Probabilistic Neural Network PSO Particle Swarm optimization QSAR Quantitative Structure-Activity Relationship QUEST Quick Unbiased Efficient Statistical Tree RAS Recirculating Aquaculture System RF Random Forest RFerns Random Ferns RMSE

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Feature Selection and Parameter optimization of Optimized Extreme Learning Machine for Motor Fault Detection 17

Feature Selection and Parameter Optimization of Optimized Ex...

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IEEE 17th International Conference on the Experience of Designing and Application of CAD Systems (CADSM)

作者： Wu, Dade Hubei Mech & Elect Res & Design Inst Co Ltd Wuhan Peoples R China

ISBN: (纸本)9798350310856

In view of the disadvantage of existing motor fault detection algorithms that they cannot adapt to complex and nonlinear fault categories and the accuracy is relatively low, we proposed one method for feature selection and parameter optimization based on simulated annealing-based whale optimization algorithm (SAWOA) optimized extreme learning machine (ELM) are proposed. Based on the ELM classification model, SAWOA is used to optimize the selection of network input variables and parameters of hidden layer nodes of ELM. To verify the effectiveness of the proposed method, it is applied to the real motor fault detection example, and compared with the existing methods under the same conditions. The experiment results show that the method achieves good performance in aspect of the classification accuracy and reliability, and show its effectiveness and application potential.

关键词： Motor Fault Detection whale optimization algorithm Simulated annealing Feature Selection

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Enhanced path planning algorithm via hybrid WOA-PSO for differential wheeled mobile robots

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SYSTEMS SCIENCE & CONTROL ENGINEERING 2024年第1期12卷

作者： Najm, Huda Talib Ahmad, Nur Syazreen Al-Araji, Ahmed Sabah Univ Sains Malaysia Sch Elect & Elect Engn George Town Malaysia Univ Technol Baghdad Biomed Engn Dept Baghdad Iraq Univ Technol Baghdad Comp Engn Dept Baghdad Iraq

This study introduces an enhanced algorithm for global path planning of Differential Wheeled Mobile Robots (DWMRs) that merges the whale optimization algorithm (WOA) and Particle Swarm optimization (PSO). This hybrid strategy, termed HWPSO, is designed to leverage WOA's exploration strength with PSO's efficient exploitation, specifically targeting the challenges of non-holonomic constraints in complex terrains. To validate the effectiveness of the proposed algorithm, its performance is evaluated across five diverse environments and compared against PSO, WOA, and Grey Wolf optimization which is widely used for mobile robot path planning. Moreover, the comparison broadens to encompass four established environments from the literature where algorithms based on firefly, ant colony, A*, and other PSO variants have previously exhibited optimal performance. Additionally, a new environment is introduced to analyze the efficacy of the proposed approach for path planning for two DWMRs. Simulation results consistently demonstrate the superiority of the proposed HWPSO, manifesting performance improvements of up to 19.3% for path length reduction and up to 12.7% for DWMR travel duration reduction when compared to other methods. This underscores the efficacy of the proposed hybrid approach in achieving enhanced path planning outcomes for DWMRs in diverse scenarios.

关键词： Differential wheeled mobile robot metaheuristic particle swarm optimization path planning whale optimization algorithm

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A novel hybrid methodology for wind speed and solar irradiance forecasting based on improved whale optimized regularized extreme learning machine

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SCIENTIFIC REPORTS 2024年第1期14卷 1-27页

作者： Syama, S. Ramprabhakar, J. Anand, R. Meena, V. P. Guerrero, Josep M. Amrita Vishwa Vidyapeetham Amrita Sch Engn Dept Elect & Elect Engn Bengaluru India Natl Inst Technol Jamshedpur Dept Elect Engn Jharkhand 831014 India Tech Univ Catalonia Ctr Res Microgrids UPC CROM Dept Elect Engn Barcelona 08019 Spain ICREA Catalan Inst Res & Adv Studies Pg Lluis Companys 23 Barcelona 08010 Spain Aalborg Univ Ctr Res Micro CROM AAU Energy DK-9220 Aalborg Denmark

With rising demand for electricity, integrating renewable energy sources into power networks has become a key challenge. The fast incorporation of clean energy sources, particularly solar and wind power, into the existing power grid in the last several years has raised a major problem in controlling and managing the power grid due to the intermittent nature of these sources. Therefore, in order to ensure the safe RES integration providing high-quality power at a fair price and for the secure and reliable functioning of electrical systems, a precise one-day-ahead solar irradiation and wind speed forecast is essential for a stable and safe hybrid energy system. Here, we propose a novel hybrid methodology for wind speed and solar irradiance forecasting. The proposed integrated model employs complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) to decompose time series data into a sequence of intrinsic mode functions of lower complexity. Further, permutation entropy is employed to extract the complexity of IMFs for filtering and reconstruction of decomposed components to alleviate the difficulty of direct modeling. Then, a unique swarm intelligence technique, the non-linear dimension learning Hunting whale optimization algorithm (NDLHWOA), is devised to optimize regularized extreme learning machine model parameters to capture the implicit information of each reconstructed sub-series. By integrating a non-linear convergence parameter and the dimension learning hunting approach, the performance of WOA can be drastically enhanced, leading to premature convergence, enhanced population variety, and effective global search. The final prediction outcome is obtained by summing the individual reconstructed sub-series prediction outcomes. To evaluate its efficacy, the proposed model is compared to five well-established models. The evaluation criteria demonstrate that the suggested method outperforms the existing methods in terms of prediction accuracy and s

关键词： Regularized Extreme Learning Machines (RELM) Permutation Entropy (PE) Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) Dimension Learning Hunting (DLH) whale optimization algorithm Solar Irradiance forecasting Wind Speed forecasting

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A Hybrid Data-Driven Deep Learning Prediction Framework for Lake Water Level Based on Fusion of Meteorological and Hydrological Multi-source Data

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NATURAL RESOURCES RESEARCH 2024年第1期33卷 163-190页

作者： Yao, Zhiyuan Wang, Zhaocai Wu, Tunhua Lu, Wen Shanghai Ocean Univ Coll Informat Shanghai 201306 Peoples R China China Inst Water Resources & Hydropower Res State Key Lab Simulat & Regulat River Basin Water Beijing 100038 Peoples R China

Accurate prediction of lake water level is of great significance for flood prevention, reservoir scheduling, and ecological protection. However, the change in lake water level is influenced by multiple factors, and water level data as a time series also have the characteristics of complexity, which leads to difficulty in water level prediction. In view of this, a hybrid CEEMDAN-BiGRU-SVR-MWOA (CBSM) framework is proposed here for lake water level prediction based on multiple sources of environmental, hydrological and meteorological factors. Firstly, the lake water level is decomposed into modal data with different frequencies using complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN). Then, the modal data are divided into internally and externally controlled items using the maximum information coefficient (MIC). Then, multivariate prediction is performed by combining the external data using bi-directional gated recurrent unit (BiGRU). The prediction results are combined using support vector regression (SVR) for prediction with higher accuracy. A modified whale optimization algorithm (MWOA) is used to optimize the parameters of CEEMDAN and the hyperparameters of BiGRU based on permutation entropy and mean square error, respectively. The proposed CBSM was applied to Dongting Lake, China, and the Nash-Sutcliffe efficiency of the prediction results reached 0.997, which was better than those of other benchmark models or frameworks, and the Diebold Mariano (DM) test further demonstrated the superiority of the proposed CBSM. Thus, this research provides a new and effective method for accurately simulating and predicting lake water levels.

关键词： Water level prediction Complete ensemble empirical mode decomposition with adaptive noise Multi-source fusion Maximum information coefficient Bi-directional gated recurrent unit whale optimization algorithm

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Identification of Pulmonary Hypertension Animal Models Using a New Evolutionary Machine Learning Framework Based on Blood Routine Indicators

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Journal of Bionic Engineering 2023年第2期20卷 762-781页

作者： Jiao Hu Shushu Lv Tao Zhou Huiling Chen Lei Xiao Xiaoying Huang Liangxing Wang Peiliang Wu Department of Computer Science and Artificial Intelligence Wenzhou UniversityWenzhou 325035People'sRepublicof China Department of Dermatology Beijing Tongren HospitalCapital Medical UniversityBeijing 100730People's Republicof China The First Clinical College Wenzhou Medical UniversityWenzhou 325000People's Republic of China Department of Pulmonary and Critical Care Medicine The First Affiliated Hospital of Wenzhou Medical UniversityWenzhou 325000People's Republic of China

Pulmonary Hypertension(PH)is a global health problem that affects about 1%of the global *** models of PH play a vital role in unraveling the pathophysiological mechanisms of the *** present study proposes a Kernel Extreme Learning Machine(KELM)model based on an improved whale optimization algorithm(WOA)for predicting PH mouse *** experimental results showed that the selected blood indicators,including Haemoglobin(HGB),Hematocrit(HCT),Mean,Platelet Volume(MPV),Platelet distribution width(PDW),and Platelet–Large Cell Ratio(P-LCR),were essential for identifying PH mouse models using the feature selection method proposed in this ***,the method achieved 100.0%accuracy and 100.0%specificity in classification,demonstrating that our method has great potential to be used for evaluating and identifying mouse PH models.

关键词： Feature selection Pulmonary hypertension whale optimization algorithm Extreme learning machine

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Fault diagnosis of rotating machines based on modified hierarchical fluctuation dispersion entropy and multi-cluster feature selection

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JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY 2023年第12期37卷 6257-6270页

作者： Li, Baoyue Yu, Yonghua Hu, Jia Cao, Bingxin Yao, Yangfeng Xu, Defeng Wuhan Univ Technol Sch Naval Architecture Ocean & Energy Power Engn Wuhan 430063 Peoples R China Wuhan Univ Technol Key Lab Marine Power Engn & Technol Minist Transport Peoples Republ China Wuhan 430063 Peoples R China Natl Engn Lab Ship & Marine Engn Power Syst Wuhan 430063 Peoples R China

The vibration signal of rotating mechanical equipment contains a large amount of information that can be used for the fault diagnosis of rotating mechanical equipment. However, the vibration information is distributed in multiple dimensions, and a single-scale analysis cannot effectively reflect its damage characteristics, reducing the accuracy of fault diagnosis. Accordingly, an improved hierarchical fluctuation dispersion entropy (MHFDE) method based on the improved hierarchical processing is proposed. MHFDE can simultaneously mine low- and high-frequency features in the time series, avoiding information omission. Comparison results of the simulated signals show that the proposed method has the advantages of high stability and accurate measurement of complexity. In combination with the multi-cluster feature selection (MCFS) and kernel limit learning machine (KELM) optimized by whale optimization algorithm (WOA), a rotating machinery damage recognition method based on MHFDE-MCFS and WOA-KEM was proposed. Three sets of typical rotating machinery datasets are used to verify the effectiveness of the proposed method. The results show that this method can not only accurately and stably identify the damage types of the three selected machinery but also have a higher accuracy of damage recognition compared with the existing feature extraction methods.

关键词： Fault diagnosis Kernel extreme learning machine Modified hierarchical fluctuation dispersion entropy Multi-cluster feature selection Rotating machinery whale optimization algorithm

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Modeling and Hybrid PSO-WOA-Based Intelligent PID and State-Feedback Control for Ball and Beam Systems

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IEEE ACCESS 2023年 11卷 137866-137880页

作者： Ahmad, Nur Syazreen Univ Sains Malaysia Sch Elect & Elect Engn Nibong Tebal 14300 Penang Malaysia

The ball and beam (BnB) system serves as a benchmark in control engineering as it provides a foundational concept applicable to addressing stabilization challenges of various underactuated nonlinear systems. This includes tasks like maintaining the balance of goods carried by mobile robots and controlling the attitude of unmanned aerial vehicles. In this study, the focus is on enhancing control optimization strategies for BnB systems that take into account inherent nonlinearities arising from actuator constraints and state measurements. The work introduces a novel intelligent control approach, termed hybrid PSO-WOA, which combines Particle Swarm optimization (PSO) and whale optimization algorithm (WOA) to automate optimal parameter search for proportional-integral-derivative (PID) and state feedback (SF) controllers. The collaborative technique between PSO and WOA is formulated to strike a balance between exploration and exploitation phases, and to mitigate premature convergence risks due to the system's complexities. Additionally, three control schemes, namely cascade PID-PID, cascade PID-SF, and cascade PID-observer are introduced, each with tailored cost functions for optimization through the hybrid PSO-WOA algorithm, accommodating both measurable and unmeasurable state scenarios. Simulation results consistently demonstrate the superior performance of the hybrid approach compared to individual PSO and WOA methods, as well as conventional PID and linear quadratic regulator approaches. Notably, the hybrid approach exhibits a significant improvement in error metrics, reducing integral-time absolute error by 18.99%, integral squared error by 35.37%, and steady-state error by 92.86%. This substantial enhancement suggests promising directions for future research in automated control parameter tuning for underactuated nonlinear systems.

关键词： Ball and beam feedback control hybrid intelligent control particle swarm optimization proportional-integral-derivative whale optimization algorithm

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Exploration and comparison of the effectiveness of swarm intelligence algorithm in early identification of cardiovascular disease

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SCIENTIFIC REPORTS 2025年第1期15卷 1-21页

作者： Bai, Tiantian Xu, Mengru Zhang, Taotao Jia, Xianjie Wang, Fuzhi Jiang, Xiuling Wei, Xing Bengbu Med Univ Sch Nursing Bengbu 233000 Anhui Peoples R China Bengbu Med Univ Sch Publ Hlth Bengbu 233000 Anhui Peoples R China Bengbu Med Univ Sch Mental Hlth Bengbu 233000 Anhui Peoples R China Bengbu Med Univ Dept Physiol Bengbu 233000 Anhui Peoples R China

Due to the aging of the global population and lifestyle changes, cardiovascular disease has become the leading cause of death worldwide, causing serious public health problems and economic pressures. Early and accurate prediction of cardiovascular disease is crucial to reducing morbidity and mortality, but traditional prediction methods often lack robustness. This study focuses on integrating swarm intelligence feature selection algorithms (including whale optimization algorithm, cuckoo search algorithm, flower pollination algorithm, Harris hawk optimization algorithm, particle swarm optimization algorithm, and genetic algorithm) with machine learning technology to improve the early diagnosis of cardiovascular disease. This study systematically evaluated the performance of each feature selection algorithm under different population sizes, specifically by comparing their average running time and objective function values to identify the optimal feature subset. Subsequently, the selected feature subsets were integrated into ten classification models, and a comprehensive weighted evaluation was performed based on the accuracy, precision, recall, F1 score, and AUC value of the model to determine the optimal model configuration. The results showed that random forest, extreme gradient boosting, adaptive boosting and k-nearest neighbor models performed best on the combined dataset (weighted score of 1), where the feature set consisted of 9 key features selected by the cuckoo search algorithm when the population size was 25;while on the Framingham dataset, the k-nearest neighbor model performed best (weighted score of 0.92), and its feature set was derived from 10 features selected by the whale optimization algorithm when the population size was 50. The results of this study show that swarm intelligence algorithms can effectively screen key and informative feature sets, significantly improve model classification accuracy, and provide strong support for the early diagnosis o

关键词： Cardiovascular disease Cuckoo search algorithm whale optimization algorithm Machine learning

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Improved Particle Swarm optimization With Spiral Updating Mechanism for Multimodal Image Registration

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INTERNATIONAL JOURNAL OF SWARM INTELLIGENCE RESEARCH 2022年第1期13卷 1-20页

作者： Saxena, Sanjeev Pohit, Mausumi Amity Univ Dept Elect & Commun Engn Noida India Gautam Buddha Univ Dept Appl Phys Greater Noida Uttar Pradesh India

A modified particle swarm optimization (PSO) algorithm is proposed with a spiral updating mechanism (spiral-PSO) to optimize the balance between exploration and exploitation phases of the standard PSO. The spiral-PSO approach reduces the possibility of the local optima trapping issue during the optimization process. The proposed algorithm has been tested with 23 standard benchmark functions to compare the spiral-PSO with the standard PSO algorithm. The authors report the application of the spiral-PSO for registration of multimodal remote sensing images and compared the registration results with that of the standard PSO. The proposed approach performs successful image registration and provides better optimization of objective functions as compared to conventional PSO.

关键词： Image Registration Metaheuristic algorithms Particle Swarm optimization Swarm Intelligence whale optimization algorithm

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