Eliminating chromatic aberration in Gauss-type lens design using a novel genetic algorithm

Fang, Yi-Chin; Tsai, Cheng-Mu; Macdonald, John; Pai, Yang-Chieh

doi:10.1364/ao.46.002401

Cited by 29 publications

(15 citation statements)

References 10 publications

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“…Different evolutionary optimization approaches have been used in lens design: genetic algorithms [16][17][18][19][20][21][22][23], evolution strategy [16][17][18][19][20][21][22][23][24][25], genetic programming [21,26,27], and evolutionary programming [28]. All of these studies have reported good results.…”

Section: Evolutionary Optimization Methods and Their Use In Optical Dmentioning

confidence: 99%

Multi-objective approach for the automatic design of optical systems

2016

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An innovative method for the automatic design of optical systems is presented and verified. The proposed method is based on a multi-objective evolutionary memetic optimization algorithm. The multi-objective approach simultaneously, but separately, addresses the image quality, tolerance, and complexity of the system. The memetic technique breaks down the search for optical designs in to three different parts or phases: optical glass selection, exploration, and exploitation. The optical glass selection phase defines the most appropriate set of glasses for the system under design. The glass selection phase limits the available glasses from hundreds to just a few, drastically reducing the design space and significantly increasing the efficiency of the automatic design method. The exploration phase is based on an evolutionary algorithm (EA), more specifically, on a problem-tailored generalized extremal optimization (GEO) algorithm, named Optical GEO (O-GEO). The new EA incorporates many features customized for lens design, such as optical system codification and diversity operators. The trade-off systems found in the exploration phase are refined by a local search, based on the damped least square method in the exploitation phase. As a result, the method returns a set of trade-off solutions, generating a Pareto front. Our method delivers alternative and useful insights for the compromise solutions in a lens design problem. The efficiency of the proposed method is verified through real-world examples, showing excellent results for the tested problems.

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Section: Evolutionary Optimization Methods and Their Use In Optical Dmentioning

confidence: 99%

Multi-objective approach for the automatic design of optical systems

2016

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“…This study proposes combining a neural network (NN) algorithm [11][12][13] with a genetic algorithm (GA) [14,15] to optimize the SA, TCO, and MTF values of contact lenses; this method is called the NN-GA [16][17][18][19]. The purpose of this study was to apply optical design theory and an optimization algorithm method of optimal processing to select contact lens parameters.…”

Section: Introductionmentioning

confidence: 99%

Aspherical lens design using hybrid neural-genetic algorithm of contact lenses

Yen

2015

Appl. Opt.

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The design of complex contact lenses involves numerous uncertain variables. How to help an optical designer to first design the optimal contact lens to reduce discomfort when wearing a pair of glasses is an essential design concern. This study examined the impact of aberrations on contact lenses to optimize a contact lens design for myopic and astigmatic eyes. In general, two aspherical surfaces can be assembled in an optical system to reduce the overall volume size. However, this design reduces the spherical aberration (SA) values at wide contact radii. The proposed optimization algorithm with optical design can be corrected to improve the SA value and, thus, reduce coma aberration (TCO) values and enhance the modulation transfer function (MTF). This means integrating a modified genetic algorithm (GA) with a neural network (NN) to optimize multiple-quality characteristics, namely the SA, TCO, and MTF, of contact lenses. When the proposed optional weight NN-GA is implemented, the weight values of the fitness function can be varied to adjust system performance. The method simplifies the selection of parameters in the optimization of optical systems. Compared with the traditional CODE V built-in optimal scheme, the proposed scheme is more flexible and intuitive to improve SA, TCO, and MTF values by 50.03%, 45.78%, and 24.7%, respectively.

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“…In this research, three design goals are followed: light efficiency, uniformity and volumetric size with a weighting of 1:1:1. Optimization work by the Taguchi method, which is believed to extend optimization following the normal optimization method by damping least square (DLS) [2], significantly improves the system performance and volumetric size. The extended optimized parameter design is able to improve the luminous flux by 4.84%, the illumination uniformity 5.62% and the packing size by 12%.…”

Section: Introductionmentioning

confidence: 99%