A Cuckoo Search-Based Trained Artificial Neural Network for Symmetric Flow Problems

In this work, an artificial neural network based on the Cuckoo search algorithm (CS-ANN) is implemented for squeezing flow problems. Three problems are considered: the squeezing flow, the MHD squeezing flow, and the flow of the third-grade fluid past a moving belt. First, the approximation for the said nonlinear differential equations is explained and the proposed problems are transformed into the L2 norms of minimization problems. Then, a well-known Cuckoo search algorithm is used to minimize the norms of each problem to get the best set of weights for artificial neural networks. The outcome of the proposed method is displayed through graphs. Two cases for each problem are discussed consisting of the solution, error, weights, and fitness function, respectively. The numerical results for the state variables are displayed in Tables. The error analysis in each case proves the accuracy of our implemented technique. The results are validated through graphs by comparing CS-ANN results with the gradient descent method.

Automated summary

In this study, an artificial neural network based on the Cuckoo search algorithm is implemented for solving squeezing flow problems. By transforming the problems into L2 norms of minimization problems, the best set of weights for the neural network is obtained using the Cuckoo search algorithm. The experimental results demonstrate the high accuracy and effectiveness of the proposed method in solving squeezing flow problems.