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On the performance of the bayesian optimization algorithm with combined scenarios of search algorithms and scoring metrics

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GENETIC PROGRAMMING AND EVOLVABLE MACHINES 2022年第2期23卷 193-223页

作者： Nametala, Ciniro A. L. Faria, Wandry R. Pereira Junior, Benvindo R. Univ Sao Paulo Sao Carlos Sch Engn Sao Paulo Brazil

The bayesian optimization algorithm (BOA) is one of the most prominent Estimation of Distribution algorithms. It can detect the correlation between multiple variables and extract knowledge on regular patterns in solutions. bayesian Networks (BNs) are used in BOA to represent the probability distributions of the best individuals. The BN's construction is challenging since there is a trade-off between acuity and computational cost to generate it. This trade-off is determined by combining a search algorithm (SA) and a scoring metric (SM). The SA is responsible for generating a promising BN and the SM assesses the quality of such networks. Some studies have already analyzed how this relationship affects the learning process of a BN. However, such investigation had not yet been performed to determine the bond linking the selection of SA and SM and the BOA's output quality. Acting on this research gap, a detailed comparative analysis involving two constructive heuristics and four scoring metrics is presented in this work. The classic version of BOA was applied to discrete and continuous optimization problems using binary and floating-point representations. The scenarios were compared through graphical analyses, statistical metrics, and difference detection tests. The results showed that the selection of SA and SM affects the quality of the BOA results since scoring metrics that penalize complex BN models perform better than metrics that do not consider the complexity of the networks. This study contributes to a discussion on this metaheuristic's practical use, assisting users with implementation decisions.

关键词： algorithm design and analysis bayesian Network models bayesian optimization algorithm Metaheuristics Probabilistic model

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A Hybrid Method for Traffic Incident Detection Using Random Forest-Recursive Feature Elimination and Long Short-Term Memory Network With bayesian optimization algorithm

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IEEE ACCESS 2021年 9卷 1219-1232页

作者： Shang, Qiang Feng, Linlin Gao, Song Shandong Univ Technol Sch Transportat & Vehicle Engn Zibo 255000 Peoples R China Shandong Univ Technol Sch Marxism Zibo 255000 Peoples R China

Automatic Incident Detection (AID) is an important part of Intelligent Transportation Systems (ITS). A hybrid AID method using Random Forest-Recursive Feature Elimination (RF-RFE) algorithm and Long-Short Term Memory (LSTM) network optimized by bayesian optimization algorithm (BOA) is proposed in this article. Firstly, a relatively comprehensive set of initial variables is constructed using basic traffic variables and their combinations. Secondly, feature variables are selected from the initial variables using the RF-RFE algorithm. Then, the feature variables are used for training the LSTM network, and the hyper-parameters of the LSTM network are optimized by BOA. In addition, Synthetic Minority Over-Sampling Technique (SMOTE) is employed to solve the problem of imbalance between incident sample size and non-incident sample size. Finally, experiments are conducted using real-world data to test performance of the proposed method and compare with several state-of-the-art AID methods on multiple evaluation criteria. The experimental results illustrate that the proposed method achieved superior performance with respect to almost all the evaluation criteria. It also shows that the proposed method is promising for dealing with the problems of imbalance and small sample size of traffic incident data.

关键词： Detectors Prediction algorithms Volume measurement Support vector machines Roads Machine learning algorithms Feature extraction Traffic incident detection feature selection long short-term memory network bayesian optimization algorithm

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bayesian optimization algorithm based support vector regression analysis for estimation of shear capacity of FRP reinforced concrete members

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APPLIED SOFT COMPUTING 2021年 105卷 107281-107281页

作者： Alam, Md Shah Sultana, N. Hossain, S. M. Zakir Univ Bahrain Dept Civil Engn Coll Engn Zallaq Bahrain Imam Abdulrahman Bin Faisal Univ Dept Comp Sci Coll Comp Sci & Informat Technol Dammam Saudi Arabia Univ Bahrain Dept Chem Engn Coll Engn Zallaq Bahrain

The use of fiber-reinforced polymer (FRP) rebars in lieu of steel rebars has led to some deviations in the shear behavior of concrete members. Several methods have been proposed to forecast the shear capacity of such members. Nonetheless, there are differences in the methods of considering the various parameters affecting shear capacity, and some of them provide widely scattered and conservative results. This paper presents a hybrid of the bayesian optimization algorithm (BOA) and support vector regression (SVR) as a novel modeling tool for the prediction of the shear capacity of FRP-reinforced members with no stirrups. For this purpose, a large dataset of simply supported beams and unidirectional slabs reinforced with FRP were utilized. The model performance was assessed using several statistical performance indicators and compared with the Japan Society of Civil Engineers (JSCE), British Institution of Structural Engineers (BISE), Canadian Standard Association (CSA), and American Concrete Institute (ACI) design codes and guidelines, as well as some other artificial intelligence (AI) models. For development of the model, all the hyperparameters, i.e., kernel function type, epsilon, box constraint, and kernel scale, were optimized using the BOA technique. The k-fold cross validation approach was utilized to avoid overfitting of the model. It was found that the mean, median, standard deviation, minimum, maximum, and interquartile range of the developed hybrid model predictions are very close to the experimental results. The predicted results overlap the experimental data with a coefficient of determination of 95.5%. The plot of relative deviations and residual plots are scattered around the zero reference line with low deviation, which indicates that the model is reliable and valid. The error terms (e.g., mean absolute error, root mean square error) obtained for all specimens were 4.85 and 11.03, which are very low values. The correlation coefficient (R) and fraction

关键词： Shear resistance FRP reinforcement Reinforced concrete bayesian optimization algorithm SVR model

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Hydrological probabilistic forecasting based on deep learning and bayesian optimization algorithm

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HYDROLOGY RESEARCH 2021年第4期52卷 927-943页

作者： Bai, Haijun Li, Guanjun Liu, Changming Li, Bin Zhang, Zhendong Qin, Hui Hunan Wushui Hydropower Dev Co Ltd China Energy Shaoyang Hunan Peoples R China Huazhong Univ Sci & Technol Sch Civil & Hydraul Engn Wuhan Hubei Peoples R China

Obtaining accurate runoff prediction results and quantifying the uncertainty of the forecasting are critical to the planning and management of water resources. However, the strong randomness of runoff makes it difficult to predict. In this study, a hybrid model based on XGBoost (XGB) and Gaussian process regression (GPR) with bayesian optimization algorithm (BOA) is proposed for runoff probabilistic forecasting. XGB is first used to obtain point prediction results, which can guarantee the accuracy of forecast. Then, GPR is constructed to obtain runoff probability prediction results. To make the model show better performance, the hyper-parameters of the model are optimized by BOA. Finally, the proposed hybrid model XGB-GPR-BOA is applied to four runoff prediction cases in the Yangtze River Basin, China and compared with eight state-of-the-art runoff prediction methods from three aspects: point prediction accuracy, interval prediction suitability and probability prediction comprehensive performance. The experimental results show that the proposed model can obtain high-precision point prediction, appropriate prediction interval and reliable probabilistic prediction results on the runoff prediction problems.

关键词： bayesian optimization algorithm Gaussian process regression probabilistic forecasting runoff XGBoost

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A stochastic framework using bayesian optimization algorithm to assess the network-level societal impacts of connected and autonomous vehicles

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TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES 2022年第0期139卷

作者： Fakhrmoosavi, Fatemeh Kamjoo, Ehsan Kavianipour, Mohammadreza Zockaie, Ali Talebpour, Alireza Mittal, Archak Michigan State Univ E Lansing MI 48824 USA Univ Illinois Urbana IL USA Ford Motor Co Dearborn MI 48121 USA

Connected and autonomous vehicle technologies are expected to alter transportation systems by enhancements in mobility, safety, and emission reduction. While many studies have investigated the impacts of connected vehicle (CV) and autonomous vehicle (AV) technologies on traffic congestion and emission at the facility level, little is known about these impacts at large scales. Furthermore, different effective parameters associated with these impacts, such as the extra vehicle miles traveled (VMT) induced by AVs, technology cost of these vehicles, and possible reductions in the value of time of AV users, are highly uncertain. The uncertainty of these parameters and its role in assessing the societal impacts of CVs and AVs are not fully explored in the literature. Therefore, this study aims to develop a stochastic framework and an optimization algorithm to find the optimum market shares of CVs and AVs in a mixed traffic environment, consisting of human-driven vehicles without connectivity (HDVs), CVs, and AVs, minimizing the system cost. Emission, travel time, and technology costs are considered as components of the system cost. Thus, the framework combines a traffic simulation tool that considers a mixed fleet of HDVs, CVs, and AVs with heterogeneous drivers (for HDVs and CVs) distributed spatially over the network, along with an emission estimation model, to measure network-wide travel time and emission costs. Many parameters, such as extra VMT produced by AVs, value of time reduction for AV users, and automation cost, are subject to a considerable degree of stochasticity, which is considered by assuming probabilistic distributions for these parameters. A bayesian optimization algorithm with heteroskedastic non-stationary Gaussian process model is presented to estimate the optimum market shares of CVs and AVs considering these uncertainties. The stochastic framework and the optimization algorithm are successfully applied to a large-scale network of Chicago. The impac

关键词： Network Modeling bayesian optimization algorithm Connected and Autonomous Vehicles Stochastic optimization Traffic Simulation

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A hybrid short-term load forecasting model based on variational mode decomposition and long short-term memory networks considering relevant factors with bayesian optimization algorithm

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APPLIED ENERGY 2019年 237卷 103-116页

作者： He, Feifei Zhou, Jianzhong Feng, Zhong-kai Liu, Guangbiao Yang, Yuqi Huazhong Univ Sci & Technol Sch Hydropower & Informat Engn Wuhan 430074 Hubei Peoples R China Hubei Key Lab Digital Valley Sci & Technol Wuhan 430074 Hubei Peoples R China

Short-term load forecasting plays an essential role in the safe and stable operation of power systems and has always been a vital research issue of energy management. In this research, a hybrid short-load forecasting method with Variational Mode Decomposition (VMD) and Long Short-Term Memory (LSTM) networks considering relevant factors which optimized by the bayesian optimization algorithm (BOA) is studied. This method firstly decomposition with VMD which is a non-recursive signal processing technology that can decompose a signal into a discrete number of modes, then, consider the relevant factors and extend to the sequence according to the coefficient of association. Specifically, for the day type and higher or lower temperature, the nonlinear mapping is used and optimized by the BOA. Finally, the subsequences are predicted by LSTM which is a special Recurrent Neural Network with memory cells and reconstructed. To validate the performance of the proposed method, two categories of contrast methods including individual methods and decomposition-based methods are demonstrated in this study. The individual methods which without decomposition processes including LSTM, Support Vector Regression, Multi-Layered Perceptron Regressor, Linear Regression, and Random Forest Regressor, and the decomposition based methods including Empirical Mode Decomposition-Long Short Term Memory, and Ensemble Empirical Mode Decomposition-Long Short-Term Memory. The simulation results, which developed in four periods of Hubei Province, China, show that the prediction accuracy of the proposed model is significantly improved compared with the contrast methods.

关键词： Short-term load forecasting Variational mode decomposition Long short-term memory network Relevant factors bayesian optimization algorithm

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Optimal steganography with blind detection based on bayesian optimization algorithm

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PATTERN ANALYSIS AND APPLICATIONS 2019年第1期22卷 205-219页

作者： Molaei, Amir Masoud Ebrahimzadeh, Ataollah Babol Noshirvani Univ Technol Fac Elect & Comp Engn Shariati Ave Babol Sar Iran

An optimal steganography method is provided to embed the secret data into the low-order bits of host pixels. The main idea of the proposed method is that before the embedding process, the secret data are mapped to the optimal values using bayesian optimization algorithm (along with introducing a novel mutation operator), in order to reduce the mean square error (MSE) and also maintain the structural similarity between the images before and after embedding (i.e., preserving the visual quality of the embedded-image). Then, the mapped data are embedded into the low-order bits of host pixels using modulus function and a systematic and reversible algorithm. Since the proposed method is able to embed data into more significant bits, it has enhanced the payload, while preserving the visual quality of the image. Extraction of data from the host image is possible without requiring the original image. The simulation results show that the proposed algorithm can lead to a minimum loss in MSE criterion and also a minimal reduction in visual quality of the image in terms of diagnostic criteria of the human eye, whereas there is no limitation on the improvement of payload, in comparison with other methods.

关键词： Optimal steganography bayesian optimization algorithm Optimal mapping vector Mutation

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Deadlock detection in complex software systems specified through graph transformation using bayesian optimization algorithm

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JOURNAL OF SYSTEMS AND SOFTWARE 2017年 131卷 181-200页

作者： Pira, Einollah Rafe, Vahid Nikanjam, Amin Arak Univ Dept Comp Engn Fac Engn Arak *** Iran KN Toosi Univ Technol Fac Comp Engn Tehran *** Iran

While developing concurrent systems, one of the important properties to be checked is deadlock freedom. Model checking is an accurate technique to detect errors, such as deadlocks. However, the problem of model checking in complex software systems is state space explosion in which all reachable states cannot be generated due to exponential memory usage. When a state space is too large to be explored exhaustively, using meta-heuristic and evolutionary approaches seems a proper solution to address this problem. Recently, a few methods using genetic algorithm, particle swarm optimization and similar approaches have been proposed to handle this problem. Even though the results of recent approaches are promising, the accuracy and convergence speed may still be a problem. In this paper, a novel method is proposed using bayesian optimization algorithm (BOA) to detect deadlocks in systems specified formally through graph transformations. BOA is an Estimation of Distribution algorithm in which a bayesian network (as a probabilistic model) is learned from the population and then sampled to generate new solutions. Three different structures are considered for the bayesian network to investigate deadlocks in the benchmark problems. To evaluate the efficiency of the proposed approach, it is implemented in GROOVE, an open source toolset for designing and model checking graph transformation systems. Experimental results show that the proposed approach is faster and more accurate than existing algorithms in discovering deadlock states in the most of case studies with large state spaces. (C) 2017 Elsevier Inc. All rights reserved.

关键词： bayesian network bayesian optimization algorithm Deadlock detection Graph transformation system State space explosion

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Robust optimization using bayesian optimization algorithm: Early detection of non-robust solutions

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APPLIED SOFT COMPUTING 2017年 61卷 1125-1138页

作者： Kaedi, Marjan Ahn, Chang Wook Univ Isfahan Fac Comp Engn Hezar Jerib Ave Esfahan *** Iran GIST Sch Elect Engn & Comp Sci 123 Cheomdangwagi Ro Gwangju 61005 South Korea

Probabilistic robustness evaluation is a promising approach to evolutionary robust optimization;however, high computational time arises. In this paper, we apply this approach to the bayesian optimization algorithm (BOA) with a view to improving its computational time. To this end, we analyze the bayesian networks constructed in BOA in order to extract the patterns of non-robust solutions. In each generation, the solutions that match the extracted patterns are detected and then discarded from the process of evaluation;therefore, the computational time in discovering the robust solutions decreases. The experimental results demonstrate that our proposed method reduces computational time, while increasing the robustness of solutions. (C) 2017 Published by Elsevier B.V.

关键词： Robust optimization bayesian optimization algorithm bayesian networks Probabilistic robustness evaluation

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bayesian optimization algorithm Based on Incremental Model Building 7th

Bayesian Optimization Algorithm Based on Incremental Model B...

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7th International Symposium on Intelligence Computation and Applications (ISICA)

作者： Yao, Jintao Kong, Yuyan Yang, Lei South China Agr Univ Coll Math & Informat Guangzhou 510642 Guangdong Peoples R China Guangzhou Coll Commerce Guangzhou 511363 Guangdong Peoples R China

ISBN: (纸本)9789811003561;9789811003554

In bayesian optimization algorithm (BOA), to accurately build the best bayesian network with respect to most metrics is NP-complete. This paper proposes an improved BOA based on incremental model building, which learns bayesian network structure using PBIL instead of greedy algorithm in BOA. The PBIL is effective to learn better bayesian network. The simulation results also show that the improved BOA has the better performance than BOA.

关键词： bayesian optimization algorithm PBIL Model building Incremental learning

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