Potential of support vector regression for optimization of lens system

Altameem, Torki; Nikolić, Vlastimir; Shamshirband, Shahaboddin; Petković, Dalibor; Javidnia, Hossein; Kiah, Miss Laiha Mat; Gani, Abdullah

doi:10.1016/j.cad.2014.10.003

Cited by 12 publications

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“…Metaheuristics is a class of powerful stochastic algorithms, which have been proved over the years as efficient and fast problem solvers [23][24][25]. Computational intelligence (CI) belongs to this class of metaheuristic search techniques [26][27][28]. CI is an emerging derivative of artificial intelligence and has recently gained attention from varied fields of science, technology, and management since it is an established metaheuristic optimization technique [29].…”

Section: Introductionmentioning

confidence: 99%

Support vector machine firefly algorithm based optimization of lens system

et al. 2014

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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, nonlinear 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 machines (SVMs) coupled with the firefly algorithm (FFA) are implemented. The performance of the proposed estimators is confirmed with the simulation results. The result of the proposed SVM-FFA model has been compared with support vector regression (SVR), artificial neural networks, and generic programming methods. The results show that the SVM-FFA model performs more accurately than the other methodologies. Therefore, SVM-FFA can be used as an efficient soft computing technique in the optimization of lens system designs.

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