Classical and Evolutionary Algorithms in the Optimization of Optical Systems

Vasiljević, Darko

doi:10.1007/978-1-4615-1051-2

Cited by 22 publications

(18 citation statements)

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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%

“…Furthermore, the studies that use of glass directly as a discrete variable [16][17][18]23,26,32,33] do not do so efficiently, despite reporting excellent results. The dimensionality of the glass selection problem is huge, even for a reasonably simple optical system, as pointed out by Tesar [34].…”

Section: Evolutionary Optimization Methods and Their Use In Optical Dmentioning

confidence: 99%

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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%

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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“…Each design modified by these operators is re-optimized with traditional local search algorithms. Since 1996, several papers have been published on the use of bit string GAs [19,20], real-valued GA [21][22][23] or ES [22,24] for the optimization of a fixed number of real-valued parameters. Results presented in [21] are apparently very good, with the successful automatic design of large-scale lens systems composed of more than ten parts with real-valued GA, and experiments on the use of Pareto optimal selection strategy for multiobjective optimization [25].…”

Section: Lens System Optimizationmentioning

confidence: 99%

Human-competitive lens system design with evolution strategies

Gagné

Beaulieu

Parizeau

et al. 2008

Applied Soft Computing

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Lens system design provides ideal problems for evolutionary algorithms: a complex non-linear optimization task, often with intricate physical constraints, for which there is no analytical solutions. This paper demonstrates, through the use of two evolution strategies, namely non-isotropic SA-ES and CMA-ES, as well as multiobjective NSGA-II optimization, the human competitiveness of an approach where an evolutionary algorithm is hybridized with a local search algorithm to solve both a classic benchmark problem, and a real-world problem.

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“…These kinds of optimization algorithms are attractive options to deal with the problem due to the fact that the search space in optical design is typically very complex, and it includes: several local minimums, high-dimensionality, strong epistasis, non-linearities, and non-continuous variables (i.e. optical glasses) [1][2][3][4].…”

Section: Introductionmentioning

confidence: 99%

“…Several different kinds of evolutionary algorithms with different variations have been reported with good results in the optical design problem, as: Genetic Algorithms (GA) [3][4][5][6][7][8][9]. Evolutionary Strategy (ES) [1][2][3] and Genetic Programming (GP) [9][10][11].…”

Section: Introductionmentioning

confidence: 99%