Reliability Analysis via an Optimal Covariance Matrix Adaptation Evolution Strategy: Emphasis on Applications in Civil Engineering

Kaveh, A.; Javadi, Seyed Mohammad; Moghanni, R. Mahdipour

doi:10.3311/ppci.15793

Cited by 6 publications

(2 citation statements)

References 24 publications

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“…Therefore, in this study, instead of complex metaheuristic algorithms, a simple robust meta-heuristic algorithm, the covariance matrix adaptation evolution strategy (CMAES), is introduced to build hybrid machine learning methods [37]. CMAES was successfully applied to solve many engineering problems [38][39][40][41]. However, CMAES was so far never used to predict streamflow.…”

Section: Introductionmentioning

confidence: 99%

Covariance Matrix Adaptation Evolution Strategy for Improving Machine Learning Approaches in Streamflow Prediction

et al. 2022

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Precise streamflow estimation plays a key role in optimal water resource use, reservoirs operations, and designing and planning future hydropower projects. Machine learning models were successfully utilized to estimate streamflow in recent years In this study, a new approach, covariance matrix adaptation evolution strategy (CMAES), was utilized to improve the accuracy of seven machine learning models, namely extreme learning machine (ELM), elastic net (EN), Gaussian processes regression (GPR), support vector regression (SVR), least square SVR (LSSVR), extreme gradient boosting (XGB), and radial basis function neural network (RBFNN), in predicting streamflow. The CMAES was used for proper tuning of control parameters of these selected machine learning models. Seven input combinations were decided to estimate streamflow based on previous lagged temperature and streamflow data values. For numerical prediction accuracy comparison of these machine learning models, six statistical indexes are used, i.e., relative root mean squared error (RRMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), Nash–Sutcliffe efficiency (NSE), and the Kling–Gupta efficiency agreement index (KGE). In contrast, this study uses scatter plots, radar charts, and Taylor diagrams for graphically predicted accuracy comparison. Results show that SVR provided more accurate results than the other methods, especially for the temperature input cases. In contrast, in some streamflow input cases, the LSSVR and GPR were better than the SVR. The SVR tuned by CMAES with temperature and streamflow inputs produced the least RRMSE (0.266), MAE (263.44), and MAPE (12.44) in streamflow estimation. The EN method was found to be the worst model in streamflow prediction. Uncertainty analysis also endorsed the superiority of the SVR over other machine learning methods by having low uncertainty values. Overall, the SVR model based on either temperature or streamflow as inputs, tuned by CMAES, is highly recommended for streamflow estimation.

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Section: Introductionmentioning

confidence: 99%

Covariance Matrix Adaptation Evolution Strategy for Improving Machine Learning Approaches in Streamflow Prediction

et al. 2022

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“…[ 10 ] In recent decades, several researches have been done in the different areas of the structural optimization field in order to design efficient structures. [ 11–19 ]…”

Section: Introductionmentioning

confidence: 99%

Optimal design of 3D special steel buckling‐restrained braced structures

Kaveh

Moghanni

Javadi

2021

Structural Design Tall Build

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Summary In the current study, the optimal design of 3D frames equipped with buckling restrained braces is presented. The objective function in the optimization problem is considered as weight minimization subjected to serviceability and strength requirements. Three different metaheuristic algorithms including Particle Swarm Optimization (PSO) algorithm, Teaching Learning Based Optimization (TLBO) algorithm, and Discrete Firefly Algorithm (DFA) are utalized to solve the optimization problem. Response spectrum analysis is considered to examine the performance of the structures during the optimization process under applied loads. Two real‐size steel structures with buckling restrained braces are selected to perform optimal design. The results demonstrate that the TLBO algorithm has better performance in discrete structural optimization.

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Set-Theoretical Metaheuristic Algorithms for Reliability-Based Design Optimization of Truss Structures

Kaveh

Hamedani

2022

Advanced Metaheuristic Algorithms and Their Applications in Structural Optimization

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Reliability Analysis via an Optimal Covariance Matrix Adaptation Evolution Strategy: Emphasis on Applications in Civil Engineering

Cited by 6 publications

References 24 publications

Covariance Matrix Adaptation Evolution Strategy for Improving Machine Learning Approaches in Streamflow Prediction

Covariance Matrix Adaptation Evolution Strategy for Improving Machine Learning Approaches in Streamflow Prediction

Optimal design of 3D special steel buckling‐restrained braced structures

Set-Theoretical Metaheuristic Algorithms for Reliability-Based Design Optimization of Truss Structures

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