Herding Behaviors of grasshopper and Harris hawk for hybridizing the neural network in predicting the soil compression coefficient
By Hossein Moayedi, M. Gör, Zongjie Lyu, D. Tien Bui. 2020
70 citations recorded by the source corpus.
Abstract This work deals with proposing two novel predictors of soil compression coefficient (SCC) through hybridizing the artificial neural network (ANN) by using grasshopper optimization algorithm (GOA) and Harris hawks optimization (HHO) metaheuristic techniques. The SCC is considered as a function of twelve key factors of the soil, collocated from a local project at Hai Phong city (Vietnam). After creating the HHO-ANN and GOA-ANN ensembles, the best structure of them is determined by a sensitivity analysis process. Each model predicted the SCC and comparing the responses with target values revealed that both metaheuristic algorithms enhance the accuracy of the ANN. In details, applying the GOA and HHO resulted in reducing the ANN leaning error (root mean square error) by 14.96% and 10.88%, as well as the prediction error by 7.14% and 4.76%, respectively.
Subfields: Civil and Structural Engineering, Management, Monitoring, Policy and Law
View Paper: https://www.semanticscholar.org/paper/b63a0028a61127afceeab3f658ee56389aeffa41
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In Civil and Structural Engineering
In Management, Monitoring, Policy and Law
Citations in the corpus
- Support vector regression optimized by meta-heuristic algorithms for daily streamflow prediction (2020, methodology): “The proposed HHO algorithm was successfully used for optimization and engineering applications (Moayedi et al. 2019a, 2020).”
- Boosted binary Harris hawks optimizer and feature selection (2020, methodology): “Also, HHO was combined with other classifiers, mainly ANN, to form predictors for predicting slope stability [81], the productivity of active solar still [82], drug design and discovery [83], and soil compression coefficient [84].”