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Biasing the transition of bayesian optimization algorithm between Markov chain states in dynamic environments

INFORMATION SCIENCES 2016年 334卷 44-64页

作者： Kaedi, Marjan Ghasem-Aghaee, Nasser Ahnh, Chang Wook Univ Isfahan Fac Comp Engn Hezar Jerib St Esfahan *** Iran Sungkyunkwan Univ Dept Comp Engn 2066 Seobu Ro Suwon 440746 South Korea

When memory-based evolutionary algorithms are applied in dynamic environments, the certainly use of uncertain prior knowledge for future environments may mislead the evolutionary algorithms. To address this problem, this paper presents a new, memory-based evolutionary approach for applying the bayesian optimization algorithm (BOA) in dynamic environments. Our proposed method, unlike existing memory-based methods, uses the knowledge of former environments probabilistically in future environments. For this purpose, the run of BOA is modeled as the movements in a Markov chain, in which the states become the bayesian networks that are learned in every generation. When the environment changes, a stationary distribution of the Markov chain is defined on the basis of the retrieved prior knowledge. Then, the transition probabilities of BOA in the Markov chain are modified (biased) to comply with the defined stationary distribution. To this end, we employ the Metropolis algorithm and modify the K2 algorithm for learning the bayesian network in BOA in order to reflect the obtained transition probabilities. Experimental results show that the proposed method achieves improved performance compared to conventional methods, especially in random environments. (C) 2015 Elsevier Inc. All rights reserved.

关键词： bayesian optimization algorithm Dynamic environments Markov chain Memory-based method

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Multi-Objective bayesian optimization algorithm for Real-Time Task Scheduling on Heterogeneous Multiprocessors

Multi-Objective Bayesian Optimization Algorithm for Real-Tim...

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IEEE Congress on Evolutionary Computation (CEC) held as part of IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： Biswas, Sajib K. Rauniyar, Amit Muhuri, Pranab K. South Asian Univ Dept Comp Sci New Delhi 110021 India

ISBN: (纸本)9781509006229

In this paper, we have proposed a bayesian optimization based novel approach for multi-objective task scheduling in real-time heterogeneous multiprocessor systems. Task scheduling problem in multi-processor real-time systems is a NP-hard problem. In such systems, scheduling of tasks becomes a huge challenge for the scheduler designers;especially when tasks are inter-dependent and have deadline constraints. Interdependent or precedence-constrained tasks are often represented as directed acyclic graphs. Most of the real life applications require state of the art planning and scheduling schemes for safer and efficient operations. Thus, we propose the algorithm `moBOA-RTS' (multi-objective bayesian optimization algorithm for real time scheduling) to find an optimal schedule satisfying all the constraints within reasonable time. Here, learning of task graph is made through bayesian networks. At first, tasks are allocated to different processors and then LDF (latest deadline first) based priority is used to determine the task execution on individual processors. The proposed approach can be applied on many processor real-time systems, where both the scenarios viz. homogenous and heterogeneous processing environments are prevalent. Experimental analysis shows that our approach produces optimal decisions for feasible scheduling that ensures the compliance of all real time and precedence related constraints.

关键词： Multi-objective task scheduling heterogeneous multiprocessor real-time systems bayesian optimization algorithm Precedence constraints

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Using and Interpreting the bayesian optimization algorithm to Improve Early Stage Design of Marine Structures

Using and Interpreting the Bayesian Optimization Algorithm t...

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作者： Devine, Thomas E. University of Michigan

学位级别：Ph.D.

Early stage naval structural design continues to advance as designers seek to improve the quality and speed of the design process. The early stages of design produce preliminary dimensions or scantlings which control the cost and structural performance of a vessel. Increased complexity in the evaluation of structural response has led to a need for efficient algorithms well suited to solving structural design specific optimization problems. As problem sizes increase, existing optimizers can become slow or inaccurate. The bayesian optimization algorithm (BOA) is presented as one solution to efficiently solve problems in the structural design optimization process. The bayesian optimization algorithm is an Estimation of Distribution algorithm (EDA) that uses a statistical sample of potential design solutions to create and train a bayesian network (BN). The application of BNs is well suited for nearly decomposable problem composition which closely matches rules based structural design evaluation. This makes the BOA well suited to solve complex early stage structural optimization problems. Additionally, the learning processes used to create and train the BNs can be analyzed and interpreted to capture design knowledge. This return of knowledge to the designer helps to improve designer intuition and model synthesis in the face of more complex and intricate models. The BNs are thus analyzed to augment design problem understanding and explore trade-offs within the design space. The result matches a paradigm shift in early stage optimization of naval structures. Designers gain better understanding of critical design variables and their interactions as compared to the previous focus on the single most optimal solution. This leads to efficient simulations which rapidly explore design spaces, document critical design variable relationships and enable the designer to create better early stage design solutions.

关键词： Articleral Design bayesian optimization algorithm Design optimization Naval Architecture Thesis

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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 new real-coded bayesian optimization algorithm based on a team of learning automata for continuous optimization

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GENETIC PROGRAMMING AND EVOLVABLE MACHINES 2014年第2期15卷 169-193页

作者： Moradabadi, Behnaz Beigy, Hamid Sharif Univ Technol Dept Comp Engn Tehran Iran

Estimation of distribution algorithms have evolved as a technique for estimating population distribution in evolutionary algorithms. They estimate the distribution of the candidate solutions and then sample the next generation from the estimated distribution. bayesian optimization algorithm is an estimation of distribution algorithm, which uses a bayesian network to estimate the distribution of candidate solutions and then generates the next generation by sampling from the constructed network. The experimental results show that the bayesian optimization algorithms are capable of identifying correct linkage between the variables of optimization problems. Since the problem of finding the optimal bayesian network belongs to the class of NP-hard problems, typically bayesian optimization algorithms use greedy algorithms to build the bayesian network. This paper proposes a new real-coded bayesian optimization algorithm for solving continuous optimization problems that uses a team of learning automata to build the bayesian network. This team of learning automata tries to learn the optimal bayesian network structure during the execution of the algorithm. The use of learning automaton leads to an algorithm with lower computation time for building the bayesian network. The experimental results reported here show the preference of the proposed algorithm on both uni-modal and multi-modal optimization problems.

关键词： Estimation of distribution algorithms bayesian optimization algorithm Learning automata

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Uncertainty Analysis for EMC Simulation Based on bayesian optimization

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IEEE TRANSACTIONS ON ELECTROMAGNETIC COMPATIBILITY 2024年第2期67卷 587-597页

作者： Bai, Jinjun Hu, Bing Duffy, Alistair Dalian Maritime Univ Coll Marine Elect Engn Dalian 116026 Peoples R China De Montfort Univ Sch Engn & Sustainable Dev Leicester LE1 9BH England

Nonintrusive uncertainty analysis methods are widely applied in the field of electromagnetic compatibility (EMC). When selecting deterministic EMC simulation sampling points as the training set, the existing methods are relatively mechanistic, which can seriously affect the computational efficiency of the uncertainty analysis process. In order to address this computational efficiency issue, this article proposes an EMC simulation uncertainty analysis method based on bayesian optimization algorithm. This method constructs a Gaussian process surrogate model through a more adaptive training set filtering strategy so as to achieve the goal of giving consideration to both computational efficiency and accuracy. Crosstalk prediction of parallel cables, electromagnetic interference of lightning electromagnetic pulse, and strong nonlinear uncertainty analysis are used to quantitatively verify that the proposed uncertainty analysis method is more efficient and accurate than the traditional non-intrusive method, especially when dealing with complex EMC simulation problems with strong nonlinearity.

关键词： Electromagnetic compatibility Uncertainty optimization Bayes methods Training Computational modeling Analytical models Accuracy Probabilistic logic Closed box bayesian optimization algorithm electromagnetic compatibility simulation nonintrusive uncertainty analysis methods

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Voltage abnormity prediction method of lithium-ion energy storage power station using informer based on bayesian optimization

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SCIENTIFIC REPORTS 2024年第1期14卷 1-17页

作者： Rao, Zhibo Wu, Jiahui Li, Guodong Wang, Haiyun Xinjiang Univ Engn Res Ctr Educ Minist Renewable Energy Generat & Grid Connec Urumqi 830049 Xinjiang Peoples R China Xinjiang Elect Power Co Ltd Elect Power Res Inst Urumqi 830049 Xinjiang Peoples R China

Accurately detecting voltage faults is essential for ensuring the safe and stable operation of energy storage power station systems. To swiftly identify operational faults in energy storage batteries, this study introduces a voltage anomaly prediction method based on a bayesian optimized (BO)-Informer neural network. Firstly, the temporal characteristics and actual data collected by the battery management system (BMS) are considered to establish a long-term operational dataset for the energy storage station. The Pearson correlation coefficient (PCC) is used to quantify the correlations between these data. Secondly, an Informer neural network with BO hyperparameters is used to build the voltage prediction model. The performance of the proposed model is assessed by comparing it with several state-of-the-art models. With a 1 min sampling interval and one-step prediction, trained on 70% of the available data, the proposed model reduces the root mean square error (RMSE), mean square error (MSE), and mean absolute error (MAE) of the predictions to 9.18 mV, 0.0831 mV, and 6.708 mV, respectively. Furthermore, the influence of different sampling intervals and training set ratios on prediction results is analyzed using actual grid operation data, leading to a dataset that balances efficiency and accuracy. The proposed BO-based method achieves more precise voltage abnormity prediction than the existing methods.

关键词： Informer Energy storage power station Voltage abnormity prediction bayesian optimization algorithm

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Prediction of shear strength of RC deep beams using XGBoost regression with bayesian optimization

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EUROPEAN JOURNAL OF ENVIRONMENTAL AND CIVIL ENGINEERING 2023年第14期27卷 4046-4066页

作者： Truong, Gia Toai Choi, Kyoung-Kyu Nguyen, Tri-Hai Kim, Chang-Soo Seoul Natl Univ Sci & Technol Sch Architecture Seoul South Korea Soongsil Univ Sch Architecture Seoul South Korea Seoul Natl Univ Sci & Technol Dept Comp Sci & Engn Seoul South Korea Seoul Natl Univ Sci & Technol Sch Architecture Seoul 01811 South Korea

For the shear strength of deep beams, most of current design codes adopt the strut-and-tie model. However, since the shear resistance mechanism of deep beams is quite complicated and shear failure could be catastrophic with no prior warning, more reliable and robust shear strength prediction models are required. To this end, in the present study, using the extreme gradient boosting (XGBoost) and bayesian optimization (BO) algorithms, a BO-XGBoost hybrid model was developed and its hyperparameters were optimized based on a database of 320 test specimens. The prediction accuracy of the proposed BO-XGBoost hybrid model was compared with G-XGBoost (with hyperparameters tuned by grid search), R-XGBoost (with hyperparameters tuned by random search), D-XGBoost (with default hyperparameters), and existing design equations, and the comparison showed that the proposed model can predict most accurately the shear strength of deep beams without any significant and evident bias. The parametric study using the proposed model revealed that the effective depth, beam width, shear span-to-depth ratio, tension reinforcement ratio, and concrete compressive strength are critical to the shear strength of deep beams. For ease and convenience of the utilization of the proposed model, a stand-alone application program was also developed.

关键词： RC deep beam shear strength machine learning bayesian optimization algorithm extreme gradient boosting graphical user interface

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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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Oceanic 3-D Thermohaline Field Reconstruction With Multidimensional Features Using SABNN

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IEEE JOURNAL OF OCEANIC ENGINEERING 2025年第2期50卷 1273-1289页

作者： Li, Juan Bai, Yajie Cui, Xuerong Li, Lei Jiang, Bin Li, Shibao Yang, Jungang China Univ Petr East China Coll Comp Sci & Technol Qingdao 266580 Peoples R China China Univ Petr East China Coll Oceanog & Space Informat Qingdao 266580 Peoples R China Minist Nat Resources Inst Oceanog 1 Qingdao 266061 Peoples R China

Aiming at the problems of missing data and outliers in ocean observations and incomplete characterization of thermohaline related features, a 3-D thermohaline reconstruction model of the ocean based on multisource data are proposed. Multisource data from remote sensing and Current and Pressure recording Inverse Echo Sounders were used to analyze the projection relationship between 12-D features, such as sea surface temperature, bidirectional propagation time, and seafloor current velocity, and the distribution of ocean temperature and salinity at different depths (10-1000 m). A bayesian optimization algorithmic framework is used to evaluate and gradually remove uncertainty from currently known data during the iterative process by extracting network parameters from the approximate probability distribution. More informed decision making improves the stability of the iterative process and reconstruction. In addition, a self-attention mechanism is introduced to dynamically focus on the dependencies between features of different dimensions by calculating the correlation matrix between features at arbitrary locations, enabling the model to more comprehensively characterize the thermohaline distribution and its changes. A Self-attentive bayesian neural network (SABNN) model is established through empirical regression. The reconstructed model is validated using observational data from the Gulf of Mexico, and the experimental results show that the SABNN model has a significant improvement in temperature and salinity reconstruction accuracy compared with other network models or methods, with the RMSE and R-2 improved by more than 29.68%, 21.14% and 31.01%, 37.33%, respectively.

关键词： Oceans Ocean temperature Remote sensing Data models Three-dimensional displays Sea surface Sea measurements Salinity (geophysical) Mathematical models Time measurement 3-D thermohaline field bayesian optimization algorithm current and pressure recording inverse echo sounders (CPIES) multisource data satellite remote sensing self-attention mechanism

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