Hybrid Grey Wolf Optimization Algorithm–Based Support Vector Machine for Groutability Prediction of Fractured Rock Mass

Deng, Shihan; Wang, Xiaoling; Zhu, Yushan; Lv, Fei; Wang, Jiajun

doi:10.1061/(asce)cp.1943-5487.0000814

Cited by 39 publications

(18 citation statements)

References 30 publications

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“…Hybrid models typically combine prediction algorithms with the data decomposition techniques and give full play to the advantages of each component to generate more accurate predictions. As an extension of SVMs, SVR was proposed by Drucker et al [22] for regression analysis possessing superior capability in solving multidimensional, nonlinear problems with small samples [6], and has been proved to be effective in solving various forecasting problems [23,24]. Therefore, SVR-based models, which combine the SVR model with data decomposition techniques, are extensively used in time series prediction [25,26].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Hoang et al [5] combined SVR with differential flower pollination (DFP) for the prediction of groutability. Deng et al [6] combined a support vector machine (SVM) with the hybrid grey wolf optimization algorithm (HGWO) to model groutability of fracture grouting. The back propagation neural network (BPNN) model and artificial neural network (ANN) model were established, respectively, to predict cement take by Yang [7] and Zhang et al [8].…”

Section: Introductionmentioning

confidence: 99%

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Grouting Power Prediction Using a Hybrid Model Based on Support Vector Regression Optimized by an Improved Jaya Algorithm

et al. 2020

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Grouting power is a vital parameter that can be used as an indicator for simultaneously controlling grouting pressure and injection rate. Accurate grouting power prediction contributes to the real-time optimization of the grouting process, guaranteeing grouting safety and quality. However, the strong nonlinearity of the grouting power time series makes the forecasting task challenging. Hence, this paper proposes a novel hybrid model for accurate grouting power forecasting. First, empirical wavelet transform (EWT) is employed to decompose the original grouting series into several subseries and one residual adaptively. Second, partial autocorrelation function (PACF) is applied to identify the optimal input variables objectively. Then, support vector regression (SVR) is adopted to obtain prediction outcomes of each subseries, while an improved Jaya (IJaya) algorithm by coupling chaos theory and Lévy flights to improve the algorithm’s accuracy performance is proposed to optimize the SVR hyperparameters. Finally, the prediction results of decomposed subseries are superimposed to produce the final results. A consolidation grouting project is taken as a case study and the computation results with the RMSE = 0.2672 MPa·L/min, MAE = 0.2165 MPa·L/min, MAPE = 3.85% and EC = 0.9815 demonstrate that the proposed model exhibits superior forecasting ability and can provide a viable reference for grouting construction.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Grouting Power Prediction Using a Hybrid Model Based on Support Vector Regression Optimized by an Improved Jaya Algorithm

et al. 2020

Self Cite

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“…Then, a score function S(A) and an accuracy function H(A) can be defined by Equations (17) and (18), respectively [69]:…”

Section: Interval-valued Intuitionistic Fuzzy Sets (Ivifs)mentioning

confidence: 99%

“…Firstly, the distance between the decision-making of the expert and the reference point is solved by Equation (16) and the distance matrix is formed. Secondly, the score and accuracy values of the collective interval-valued intuitionistic fuzzy sets are obtained by Equations (17) and (18), respectively, and the scoring function matrix is established.…”

Section: Project Overviewmentioning

confidence: 99%

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A Comprehensive Evaluation Model for Curtain Grouting Efficiency Assessment Based on Prospect Theory and Interval-Valued Intuitionistic Fuzzy Sets Extended by Improved D Numbers

et al. 2019

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Grouting-efficiency evaluation is a key element in grouting-construction control. However, most existing grouting-efficiency evaluation models do not consider the hesitation and bounded rationality of experts and have difficulty in handling the problem of incomplete decision-making information generated by experts. Furthermore, the diversity of the evaluation indicators used can be further improved. This study conducts a comprehensive evaluation model to address these problems. An objective and reasonable fuzzy evaluation method is demonstrated through the integration of interval-valued intuitionistic fuzzy sets, prospect theory, and improved D numbers. The secondary permeability index is introduced to establish a more scientific evaluation indicator system. The proposed model is implemented in evaluating the curtain-grouting efficiency of a hydropower station, and its consistency, representativeness, and superiority are validated and analyzed.

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Gaussian bare‐bones gradient‐based optimization: Towards mitigating the performance concerns

Qiao

Shan

Jiang

et al. 2021

Int J of Intelligent Sys

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Gradient‐based optimizer (GBO) is a metaphor‐free mathematic‐based algorithm proposed in recent years. Encouraged by the gradient‐based Newton's method, this algorithm combines with population‐based evolutionary methods. The disadvantage of the traditional GBO algorithm is that the global search ability of the algorithm is too strong, and the local search ability is too weak; accordingly, it is difficult to obtain the global optimal solution efficiently. Therefore, a new improved GBO algorithm (GOMGBO) is developed to mitigate such performance concerns by introducing a Gaussian bare‐bones mechanism, an opposition‐based learning mechanism, and a moth spiral mechanism enhanced GBO algorithm. The proposed GOMGBO has been compared against many famous methods and improved variants on 30 benchmark functions. The experimental results show that GOMGBO has apparent advantages in convergence speed and precision. In addition, this paper analyzes the balance and diversity of the GOMGBO algorithm and compares GOMGBO with other algorithms on several engineering problems. The experimental results show that the GOMGBO algorithm is also better than the competitive algorithm in engineering problems. This study uses the GOMGBO algorithm to optimize kernel extreme learning machine (KELM), and a new GOMGBO‐KELM model is proposed. The model is used to deal with four clinical disease diagnosis problems. Compared with GBO‐KELM, back propagation neural network algorithm, and other models, comparative experiments show that GOMGBO‐KELM has high performance in dealing with practical cases. We invite the community to investigate further our method for solving problems more efficiently with reasonable speed and efficiency. Readers of this study can refer to https://aliasgharheidari.com for any guidance about the proposed GOMGBO method.

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Hybrid Grey Wolf Optimization Algorithm–Based Support Vector Machine for Groutability Prediction of Fractured Rock Mass

Cited by 39 publications

References 30 publications

Grouting Power Prediction Using a Hybrid Model Based on Support Vector Regression Optimized by an Improved Jaya Algorithm

Grouting Power Prediction Using a Hybrid Model Based on Support Vector Regression Optimized by an Improved Jaya Algorithm

A Comprehensive Evaluation Model for Curtain Grouting Efficiency Assessment Based on Prospect Theory and Interval-Valued Intuitionistic Fuzzy Sets Extended by Improved D Numbers

Gaussian bare‐bones gradient‐based optimization: Towards mitigating the performance concerns

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