Fine-tuning of neural computing using whale optimization algorithm for predicting compressive strength of concrete

Bui, Dieu Tien; Abdullahi, Mu’azu Mohammed; Ghareh, Soheil; Moayedi, Hossein; Nguyen, Hoang

doi:10.1007/s00366-019-00850-w

Cited by 61 publications

(12 citation statements)

References 27 publications

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“…In fact, this value indicates the number of the individuals in the society (e.g., the number of ant lions in the ALO technique). Each network was tested by nine different population sizes (i.e., 10, 25, 50, 75, 100, 200, 300, 400, and 500) [46]. The RMSE was defined as the objective function to measure the error of the performance at the end of each iteration.…”

Section: Alo Bbo and Goa Conventional Methods For Optimizing The Nnmentioning

confidence: 99%

Application of Three Metaheuristic Techniques in Simulation of Concrete Slump

et al. 2019

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Slump is a workability-related characteristic of concrete mixture. This paper investigates the efficiency of a novel optimizer, namely ant lion optimization (ALO), for fine-tuning of a neural network (NN) in the field of concrete slump prediction. Two well-known optimization techniques, biogeography-based optimization (BBO) and grasshopper optimization algorithm (GOA), are also considered as benchmark models to be compared with ALO. Considering seven slump effective factors, namely cement, slag, water, fly ash, superplasticizer (SP), fine aggregate (FA), and coarse aggregate (CA), the mentioned algorithms are synthesized with a neural network to determine the best-fitted neural parameters. The most appropriate complexity of each ensemble is also found by a population-based sensitivity analysis. The findings revealed that the proposed ALO-NN model acquires a good approximation of concrete slump, regarding the calculated root mean square error (RMSE = 3.7788) and mean absolute error (MAE = 3.0286). It also outperformed both BBO-NN (RMSE = 4.1859 and MAE = 3.3465) and GOA-NN (RMSE = 4.9553 and MAE = 3.8576) ensembles.

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Section: Alo Bbo and Goa Conventional Methods For Optimizing The Nnmentioning

confidence: 99%

Application of Three Metaheuristic Techniques in Simulation of Concrete Slump

et al. 2019

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“…Therefore, this algorithm shows very good optimization performance in solving many numerical optimization and engineering problems [Mirjalili et al, 2016;Bui et al, 2021]. By making improvements based on this algorithm in this paper, an Improved Whale Optimization Algorithm (IWOA) was proposed and applied to the inversion research of the Rayleigh wave dispersion curve.…”

Section: Surface Wave Inversion Research Of Iwoamentioning

confidence: 99%

Research on the Rayleigh Surface Wave Inversion Method Based on the Improved Whale Optimization Algorithm

Ren,

Yao,

Feng

et al. 2024

Annals of Geophysics

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Surface wave exploration is widely used in fields such as near‑surface exploration and engineeringsurveys because of its advantages of high resolution near the surface, non-destructive testing, convenient construction, and high-cost performance. Dispersion curve inversion is a key step isurface wave exploration, and the inversion accuracy of subsurface elastic parameters is heavily dependent on the inversion method. Similar to other geophysical inversion problems, dispersion curve inversion has inherent defects such as multiple parameters and multiple extreme values. Therefore, the use of linear methods has certain uncertainties and instability. From the perspective of global optimization nonlinearity, this paper introduces the whale optimization algorithm (WOA) and improves it. The three population update mechanisms of the WOA are independent of each other, so the global exploration and local development processes in the optimization phase can be run and controlled separately. In addition, WOA does not require the artificial setting of various control parameter values, which improves the efficiency of the algorithm and reduces the difficulty of application. However, the WOA application still has slightly lower convergence precision and result accuracy in dispersion curve inversion, so this paper also proposes an improved whale optimization algorithm (IWOA) based on WOA. IWOA optimizes the initialization of the population and adds adaptive weight coefficients to enrich the population information and improve the convergence ability and local search ability of the algorithm. To test the applicability and noise immunity of IWOA for dispersion curve inversion, the noise-free and noise-contaminated dispersion curves of three theoretical models were inverted with IWOA. IWOA was also applied to the study of multi-mode model dispersion curve inversion. At the same time, the particle swarm optimization (PSO) algorithm and WOA were also tested in the same inversion test to compare the performances of the PSO, WOA, and IWOA. The results of the above various experimental analyses show that IWOA has good applicability and noise immunity in the dispersion curve inversion of the theoretical model. The multi-mode dispersion curve inversion results show that IWOA is not only suitable for the inversion of multi‑mode data but also can significantly improve the accuracy of the inversion results. Compared with PSO and WOA, IWOA has a more stable convergence process and higher convergence accuracy. Finally, the measured data from the Arnarbæli area in Iceland (fundamental-mode surface wave data) and the Wyoming area in the United States (multi-mode surface wave data) were inverted to test the practicality of IWOA in inverting measured data. Analysis of measured data shows that IWOA is very suitable for the inversion of dispersion curves and can effectively quantitatively explain and solve practical engineering problems.

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“…In other words, in this phase, the network predicts the shear strength for unseen soil conditions. The quality of testing results indicates the generalization capability of the model [48][49][50].…”

Section: Data Division and Preprocessingmentioning

confidence: 99%

Shuffled Frog Leaping Algorithm and Wind-Driven Optimization Technique Modified with Multilayer Perceptron

2020

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The prediction aptitude of an artificial neural network (ANN) is improved by incorporating two novel metaheuristic techniques, namely, the shuffled frog leaping algorithm (SFLA) and wind-driven optimization (WDO), for the purpose of soil shear strength (simply called shear strength) simulation. Soil information of the Trung Luong national expressway project (Vietnam) including depth of the sample (m), percentage of sand, percentage of silt, percentage of clay, percentage of moisture content, wet density (kg/m3), liquid limit (%), plastic limit (%), plastic index (%), liquidity index, and the shear strength (kPa) was collocated through a field survey. After constructing the hybrid ensembles of SFLA–ANN and WDO–ANN, both models were optimized in terms of complexity using a population-based trial-and error-scheme. The learning quality of the ANN was compared with both improved versions to examine the effect of the used metaheuristic techniques. In this phase, the training error dropped by 14.25% and 28.25% by applying the SFLA and WDO, respectively. This reflects a significant improvement in pattern recognition ability of the ANN. The results of the testing data revealed 25.57% and 39.25% decreases in generalization (i.e., testing) error. Moreover, the correlation between the measured and predicted shear strengths (i.e., the coefficient of determination) rose from 0.82 to 0.89 and 0.92, which indicates the efficiency of both SFLA and WDO metaheuristic techniques in optimizing the ANN.

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Fine-tuning of neural computing using whale optimization algorithm for predicting compressive strength of concrete

Cited by 61 publications

References 27 publications

Application of Three Metaheuristic Techniques in Simulation of Concrete Slump

Application of Three Metaheuristic Techniques in Simulation of Concrete Slump

Research on the Rayleigh Surface Wave Inversion Method Based on the Improved Whale Optimization Algorithm

Shuffled Frog Leaping Algorithm and Wind-Driven Optimization Technique Modified with Multilayer Perceptron

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