One of the major challenges in optical lens design is to ascertain the lens system with the highest image quality. The image quality of the lens system, which is a measure of the performance of the lens, is a function of aberrations. This function is highly nonlinear and leads to the presence of multiple local minima in the design (optimization) landscape. Evolutionary algorithms, specifically Genetic Algorithm, are receiving attention in this field as an efficient global optimization techniques for multi variables and nonlinear objective functions. However, to the best of our knowledge, studies are as yet unavailable to provide an analysis on the performance of GA and the influence of its tuning parameters on the optimization of these systems. Our research has been conducted to supply such information and to provide a guideline on using GA, in GA-aided optical lens designs. The performance of GA has been investigated in a general group of three-lens systems. It is shown that GA is an efficient optimization technique in this field, while applying the suitable tuning parameters of GA is crucial. It has been realized that Gaussian Mutation (Scale of 0.5), combined with Heuristic Crossover, with a Crossover Fraction of 0.6, was the option which yielded good (i.e. the challengeable practically expected) results. However, any variation of these parameters may prevent the system from ever reaching an optimal configuration.
|2021 IEEE Congress on Evolutionary Computation, CEC 2021 - Proceedings
|2021 IEEE Congress on Evolutionary Computation (CEC)
|Virtual at Kraków
|28/06/21 → 1/07/21
- Optical lens design
- Genetic Algorithm (GA)
- GA Tuning Parameters