Krill herd algorithm-based neural network in structural seismic reliability evaluation

ABSTRACT

In this research work, the relative displacement of the stories has been determined by means of a feedforward Artificial Neural Network (ANN) model, which employs one of the novel methods for the optimization of the artificial neural network weights, namely the krill herd algorithm. For the purpose of this work, the area, elasticity, and load parameters were the input parameters and the relative displacement of the stories was the output parameter. To assess the precision of the feedforward (FF) model optimized using the Krill Herd Optimization (FF-KH) algorithm, comparison of results has been performed relative to the results obtained by the linear regression model, the Genetic Algorithm (GA), and the back propagation neural network model. The comparison of results has been carried out in the training and test phases. It has been revealed that the artificial neural network optimized with the krill herd algorithm supersedes the afore-mentioned models in potential, flexibility, and precision.

KEYWORDS:

• Artificial intelligence techniques
• artificial krill herd algorithm
• artificial neural networks
• krill herd
• optimization
• regression models
• seismic reliability assessment of structures

Conflict of interest

The authors confirm that this article content has no conflict of interest.

Acknowledgments

The authors would like to thank Dr. Panayiotis Roussis, Assistant Professor at the Department of Civil and Environmental Engineering of the University of Cyprus, for his valuable comments and discussions. Moreover, we gratefully acknowledge the anonymous reviewers for their insightful comments and suggestions.