Integrated neuro-evolution-based computing solver for dynamics of nonlinear corneal shape model numerically

In this study, bio-inspired computational techniques have been exploited to get the numerical solution of a nonlinear two-point boundary value problem arising in the modelling of the corneal shape. The computational process of modelling and optimization makes enormously straightforward to obtain accurate approximate solutions of the corneal shape models through artificial neural networks, pattern search (PS), genetic algorithms (GAs), simulated annealing (SA), active-set technique (AST), interior-point technique, sequential quadratic programming and their hybrid forms based on GA-AST, PS-AST and SA-AST. Numerical results show that the designed solvers provide a reasonable precision and efficiency with minimal computational cost. The efficacy of the proposed computing strategies is also investigated through a descriptive statistical analysis by means of histogram illustrations, probability plots and one-way analysis of variance.

Automated summary

Bio-inspired computational techniques were used in this study to solve a nonlinear two-point boundary value problem related to modeling corneal shape. Various computational methods were employed to obtain accurate approximate solutions with minimal cost, showing reasonable precision and efficiency. The efficacy of the proposed computing strategies was also validated through statistical analysis methods.