Potential of support vector regression for optimization of lens system

Lens system design is an important factor in image quality. The main aspect of the lens system design methodology is the optimization procedure. Since optimization is a complex, non-linear task, soft computing optimization algorithms can be used. There are many tools that can be employed to measure optical performance, but the spot diagram is the most useful. The spot diagram gives an indication of the image of a point object. In this paper, the spot size radius is considered an optimization criterion. Intelligent soft computing scheme Support Vector Regression (SVR) is implemented. In this study, the polynomial and radial basis functions (RBF) are applied as the SVR kernel function to estimate the optimal lens system parameters. The performance of the proposed estimators is confirmed with the simulation results. The SVR results are then compared with other soft computing techniques. According to the results, a greater improvement in estimation accuracy can be achieved through the SVR with polynomial basis function compared to other soft computing methodologies. The SVR coefficient of determination R2 with the polynomial function was 0.9975 and with the radial basis function the R2 was 0.964. The new optimizationmethods benefit from the soft computing capabilities of global optimization andmultiobjective optimization rather than choosing a starting point by trial and error and combining multiple criteria into a single criterion in conventional lens design techniques. © 2014 Elsevier Ltd. All rights reserved.