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 solut...
详细信息
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.
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 ...
详细信息
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.
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 ...
详细信息
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
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 difficu...
详细信息
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.
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 veh...
详细信息
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 optimizationalgorithm 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 optimizationalgorithm are successfully applied to a large-scale network of Chicago. The impac
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 ...
详细信息
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.
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...
详细信息
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.
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 checki...
详细信息
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.
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 (BO...
详细信息
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.
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 learn...
详细信息
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.
暂无评论