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2008 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, Pages 477-948

作者： Xiaoxiong Liu Jingping Shi Weiguo Zhang Yan Wu College of Automation Northwestern Polytechnical University Xi'an Shaanxi Province 710072 China

ISBN: (纸本)9781424424948

Fault identification method provides a great enhancement by using evolutionary algorithms in complex mechatronics systems. A Mutation-based bayesian optimization algorithm is presented to improve the efficiency of bayesian optimization algorithm (BOA). The mutation operator which makes full use of local information is combined into BOA by diversity function. The original objective is to combine the global information and local information in order to avoid local optimum. According to the fault analysis of aircraft actuation systems, the program of BOA for fault identification is introduced. The scheme is illustrated through simulations applying the flight control system of a fighter. The simulation result show fault identification is achieved.

关键词： Fault identification bayesian optimization algorithm flight control system actuation systems

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iBOA: the incremental bayesian optimization algorithm 08

iBOA: the incremental bayesian optimization algorithm

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Proceedings of the 10th annual conference on Genetic and evolutionary computation

作者： Martin Pelikan Kumara Sastry David E. Goldberg University of Missouri in St. Louis St. Louis MO USA University of Illinois at Urbana-Champaign Urbana IL USA

ISBN: (纸本)9781605581309

This paper proposes the incremental bayesian optimization algorithm (iBOA), which modifies standard BOA by removing the population of solutions and using incremental updates of the bayesian network. iBOA is shown to be able to learn and exploit unrestricted bayesian networks using incremental techniques for updating both the structure as well as the parameters of the probabilistic model. This represents an important step toward the design of competent incremental estimation of distribution algorithms that can solve difficult nearly decomposable problems scalably and reliably.

关键词： bayesian optimization algorithm estimation of distribution algorithms evolutionary computation iBOA incremental boa BOA incremental model update EDA

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Prediction and optimization of key factors for catalytic O3 degradation of antibiotics based on Catboost model coupled bayesian optimisation algorithm

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JOURNAL OF WATER PROCESS ENGINEERING 2025年 72卷

作者： Wang, Xiaoxia Zheng, Xinnan Huang, Zipeng Hou, Cheng Song, Yuanbo Jin, Mengyu Shi, Yang Zhang, Yalei Shen, Zheng Tongji Univ Inst New Rural Dev Sch Elect & Informat Engn Shanghai 201804 Peoples R China Tongji Univ Coll Environm Sci & Engn State Key Lab Pollut Control & Resources Reuse Shanghai 200092 Peoples R China Tongji Univ Shanghai Res Inst Intelligent Autonomous Syst Shanghai 200092 Peoples R China Minist Agr & Rural Affairs Key Lab Rural Toilet & Sewage Treatment Technol Shanghai 201804 Peoples R China

Machine learning (ML) has the ability to improve the efficiency of heterogeneous catalytic ozone (O3) degradation of antibiotics through the potential for rapid identification of key factors in the catalytic system. However, there is still a lack of research on the reverse regulation of catalyst metal composition and reaction conditions for specific antibiotics. This study combined ML model and the bayesian optimization algorithm (BOA) to predict and optimize the key influencing factors in the catalytic O3 degradation of antibiotic systems. The results indicate that the CatBoost model presented the optimal training efficiency and accuracy, with an R2 value of 0.9482 and an RMSE of 0.0401. SHAP analysis revealed that antibiotic type, catalyst metal composition, and O3 flow rate are the three most critical features in model training, with significant interactions observed between O3 flow rate, pH, and catalyst dosage. Experimental validation demonstrated that the experimental TOC removal rate of antibiotics reached 87.33 % under optimized conditions, while the model-optimized TOC removal rate achieved 90 %, with an error of only 2.97 %. The present work significantly improved the efficiency and accuracy of O3 catalytic degradation process design for antibiotics and provided a novel approach for selecting and predicting reaction parameters in O3 catalytic degradation of specific antibiotics.

关键词： Antibiotic bayesian optimization algorithm CatBoost O 3 catalysis TOC

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A bayesian optimization-based Evolutionary algorithm for Flexible Job Shop Scheduling

A Bayesian Optimization-based Evolutionary Algorithm for Fle...

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Conference on Engineering Cyber Physical Systems - Machine Learning, Data Analytics and Smart Systems Architecting

作者： Sun, Lu Lin, Lin Wang, Yan Gen, Mitsuo Kawakami, Hiroshi Dalian Univ Technol Dalian 116620 Peoples R China Fuzzy Log Syst Inst Tokyo 8200067 Japan Tokyo Univ Sci Tokyo 1138656 Japan Kyoto Univ Kyoto 6068306 Japan

Flexible Job-shop Scheduling Problem (fJSP) is a typical and important scheduling problem in Flexible Manufacturing System (FMS). The fJSP is an extended version of Job-shop Scheduling (JSP) that is NP hard problem. Due to it according with the real production system, we adopt a hybrid evolutionary computation algorithm to solve the fJSP problems. Among them, the bayesian optimization algorithm (BOA) is introduced to the characteristics of scheduling and uncertainty characteristics of the time in the fJSP. On this basis, we propose a distributed evolutionary algorithm and parameter adaptive mechanism. Finally, through experiments, we conclude that the proposed hybrid evolutionary algorithm based on BOA with grouping mechanism get better solution than original algorithm and improve robustness of algorithm. Meanwhile, the paper also have objective perspective, that is we can group the data different from each other, make the whole population into sub-populations, and then make the experiment separately on different and parallel machines in distributed environment, so that not only optimizes the best solution, but also enhance the efficiency and shortened the time. (C) 2015 The Authors. Published by Elsevier B.V.

关键词： bayesian optimization algorithm Flexible Job-shop Scheduling Problem Evolutionary algorithms

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Time Series Forecasting Method Based on Multi-Scale Feature Fusion and Autoformer

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APPLIED SCIENCES-BASEL 2025年第7期15卷 3768-3768页

作者： Ma, Xiangkai Zhang, Huaxiong Zhejiang Sci Tech Univ Sch Informat Sci & Engn Hangzhou 310000 Peoples R China Zhejiang Sci Tech Univ Sch Comp Sci & Technol Sch Artificial Intelligence Hangzhou 310000 Peoples R China

Accurate time series forecasting is crucial in fields such as business, finance, and meteorology. To achieve more precise predictions and effectively capture the potential cycles and stochastic characteristics at different scales in time series, this paper optimizes the network structure of the Autoformer model. Based on multi-scale convolutional operations, a multi-scale feature fusion network is proposed, combined with date-time encoding to build the MD-Autoformer time series forecasting model, which enhances the model's ability to capture information at different scales. In forecasting tasks across four fields-apparel sales, meteorology, finance, and disease-the proposed method achieved the lowest RMSE and MAE. Additionally, ablation experiments demonstrated the effectiveness and reliability of the proposed method. Combined with the TPE bayesian optimization algorithm, the prediction error was further reduced, providing a reference for future research on time series forecasting methods.

关键词： time series forecasting multi-scale feature fusion network date-time encoding Autoformer bayesian optimization algorithm

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Ensemble learning predicts glass-forming ability under imbalanced datasets

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COMPUTATIONAL MATERIALS SCIENCE 2025年 248卷

作者： Cheng, Duan-jie Liang, Yong-chao Pu, Yuan-wei Chen, Qian Guizhou Univ Coll Adv Optoelect Mat & Technol Sch Big Data & Informat Engn Guiyang 550025 Peoples R China

With the development of artificial intelligence, machine learning (ML) is widely used to predict glass-forming ability (GFA). However, GFA experimental data usually exhibits a long-tailed distribution, and the similarity between the enhanced dataset and the original dataset is unclear. In terms of modeling, although model fusion provides better prediction results than individual learners, it also faces the risk of overfitting. Therefore, two preprocessing methods designed for regression problems WEighted Relevance-based Combination Strategy (WERCS) and Synthetic Minority Over-sampling technique with Gaussian Noise (SMOGN) are employed. The best strategy is selected by Pairwise correlation difference (PCD). Based on the screening results, this paper further proposes a multi-layer stacking ensemble learning model (MLS) for predicting GFA. Considering model accuracy and diversity together, the base model and meta-model combinations are optimized by bayesian optimization algorithm (BOA). The results show that MLS achieves R2 = 0.79 in prediction accuracy, which is better than other models and criteria discussed in this paper. In addition, the generalization ability of the MLS model is verified in the Cu-Mg-Ca alloy system. To explain the MLS model, SHapley Additive exPlanation (SHAP) is introduced. With the help of MLS and SHAP methods, the formation law of bulk metallic glasses (BMGs) is revealed, and the BMGs of Zr-Cu-Al-Ag series alloys are successfully designed.

关键词： Glass-forming ability Data Enhancement Ensemble learning bayesian optimization algorithm Design of bulk metallic glasses

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bayesian algorithm optimized deep model for multi-signal fault identification of hydraulic pumps

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ALEXANDRIA ENGINEERING JOURNAL 2025年 121卷 465-483页

作者： Tang, Shengnan Jiang, Yixuan Zhou, Tao Zhu, Yong Lim, Kian Meng Jiangsu Univ Inst Adv Mfg & Modern Equipment Technol Sch Mech Engn Zhenjiang 212013 Peoples R China Zhejiang Univ State Key Lab Fluid Power & Mechatron Syst Hangzhou 310013 Peoples R China Natl Univ Singapore Dept Mech Engn Singapore 117575 Singapore Jiangsu Univ Natl Res Ctr Pumps Zhenjiang 212013 Peoples R China LEO Grp Co Ltd Wenling 317500 Peoples R China

Hydraulic pump (HP) supplies the power for a hydraulic transmission system. It is critical for the fault diagnosis of HP to guarantee the healthy operation of systems. The rapid progress of artificial intelligence and measuring technique empowers data-driven methods. Diagnosis methods based on machine learning are limited in timeconsuming parameter adjustment. Moreover, current methods based on one measured signal achieve fault diagnosis only by using limited feature information. To address the two problems, this research proposes an intelligent fault diagnosis method for HP. First, original signals from multiple sources are obtained using multiple sensors. Due to the effective solution of frequency aliasing and the good robustness to the background noise of the signals, an intelligent method combining an improved deep model and synchrosqueezing wavelet transform is further established. The introduction of bayesian algorithm completes the adaptive selection of main parameters and contributes to reduce the computing time. The optimal model is selected by updating hyperparameter groups and evaluating the performance. The optimized diagnostic model presents good convergence and stability. The diagnostic accuracies achieve 96.69 %, 97.58 %, and 99.89 % for three signals. The proposed diagnostic method presents the effectiveness in mining fault characteristic information and intelligent identification of common fault types.

关键词： Hydraulic pump Intelligent defect identification Deep model bayesian optimization algorithm Synchrosqueezing wavelet transform

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Prediction of compressive strength of multiple types of fiber-reinforced concrete based on optimized machine learning models

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2025年 152卷

作者： Zhao, Ning Zhang, Haonan Xie, Peilun Chen, Xiaowei Wang, Xuewei Sichuan Agr Univ Sch Civil Engn Chengdu 611830 Peoples R China Southeast Univ Sch Civil Engn Nanjing 211189 Peoples R China

The accurate prediction of compressive strength of fiber-reinforced concrete (FRC) is essential for its design optimization and performance assessment, as it can significantly reduce testing costs. However, the high variability of FRC's compressive strength poses considerable prediction challenges. Current research has predominantly focused on developing prediction models for single-type FRC, while the prediction of compressive strength across multiple types of FRC remains a critical and unresolved issue in the field. To address this gap, this study proposes a novel hybrid approach integrating Deep Neural Networks (DNN), Generalized Regression Neural Networks (GRNN), and Extreme Gradient Boosting (XGBoost) with optimization techniques-Particle Swarm optimization (PSO), bayesian optimization (BO), and Bald Eagle Search (BES). A comprehensive dataset of 386 peer-reviewed compressive strength measurements was utilized, with K-means++ algorithm ensuring balanced training and testing set distributions. Hyperparameter optimization for DNN, GRNN, and XGBoost was conducted by combining PSO, BO, and BES with five-fold cross-validation. Results demonstrate strong model performance, with the BES-XGBoost model achieving the highest accuracy, exhibiting deviations of approximately 15 % between actual and predicted values. Additionally, Shapley Additive Explanations (SHAP) and partial dependence plots were employed to analyze the feature importance on compressive strength and the coupling effects of fiber characteristics. The proposed approach not only provides enhanced prediction accuracy for multiple types of FRC but also delivers valuable insights for FRC proportioning design, advancing the field of FRC performance evaluation.

关键词： Fiber-reinforced concrete Neural network Compressive strength prediction Particle swarm optimization algorithm bayesian optimization algorithm Bald eagle search optimizer

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Multi-output discrete grey model tailored for electricity consumption forecast

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APPLIED MATHEMATICAL MODELLING 2025年 139卷

作者： Zhang, Xu Dang, Yaoguo Ding, Song Wang, Haowei Ding, Feipeng Nanjing Univ Aeronaut & Astronaut Coll Econ & Management Nanjing 211106 Peoples R China Zhejiang Univ Finance & Econ Sch Econ Hangzhou 310018 Peoples R China Natl Univ Singapore Dept Ind Syst Engn & Management Singapore Singapore Jiangxi Univ Finance & Econ Sch Stat & Data Sci Nanchang 330013 Peoples R China

To address the limitation of many conventional grey forecasting methods that prioritize temporal dynamics analysis while often overlooking the essential spatial characteristics, we developed a multi-output discrete grey model tailored for forecasting electricity consumption in China's Yangtze River Delta, incorporating spatial effects. Specifically, we design a dynamic spatial interaction matrix to precisely capture the spatial correlations among neighboring areas, and integrate multiple sets of fixed-effect parameters to adeptly address spatial heterogeneity. Additionally, we implement regularization technique to prevent overfitting and utilize the bayesian optimization algorithm for hyper-parameter adjustment, considerably enhancing the model's stability of parameter estimation and its resilience to noise. Finally, a comprehensive case study, benchmarked against seven other models, highlights our model's outstanding multi-step predictive accuracy and robustness against input data uncertainty, exceeding the performance of existing methods.

关键词： Grey forecasting Spatial correlation and spatial heterogeneity bayesian optimization algorithm Regularization Electricity consumption forecasting

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A Novel Deep Learning Based Classification of Building Roof Types Using Point Cloud Data

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JOURNAL OF THE INDIAN SOCIETY OF REMOTE SENSING 2025年第3期53卷 815-826页

作者： Yildirim, Merve Karsli, Fevzi Karadeniz Tech Univ Engn Fac Dept Geomat Engn Trabzon Turkiye

The classification of various building roof types within point clouds has become a significant research topic, as it constitutes a crucial step for numerous applications, including the automatic detection and extraction of buildings, urban area mapping, and energy potential analysis. This study aims to develop a novel deep learning architecture that uses point clouds as input data to classify different types of building roofs. For this purpose, the proposed architecture, whose hyperparameters are optimized using the bayesian optimization algorithm, is trained and tested on the RoofN3D dataset. This architecture classified three different types of roof on the test data with an overall classification accuracy of 95%. In addition, the classification performance of the proposed architecture is compared with the well-known PointNet architecture. The outputs indicate that the classification performance of the two architectures is almost identical, with only a small difference due to the fact that the original design of the proposed architecture has fewer layers and thus a shorter training time. Therefore, the proposed architecture is an important method for automatically classifying different building roof types from point cloud data.

关键词： Point cloud Roof type classification Deep learning bayesian optimization algorithm

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