Global and local optimization for optical systems

Cheng, Xuemin; Wang, Yongtian; Hao, Qun; Isshiki, Masaki

doi:10.1016/j.ijleo.2005.06.007

Cited by 11 publications

(2 citation statements)

References 2 publications

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“…Therefore a decrease in the light intensity due to distance between fireflies will lead to variations of intensity and thereby lessen the attractiveness among them. Equation (13) can be used to represent the light intensity with varying distance:…”

Section: Svm Parameter Selection Using Firefly Optimization Algorithmmentioning

confidence: 99%

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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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Section: Svm Parameter Selection Using Firefly Optimization Algorithmmentioning

confidence: 99%

“…The goal of the optimization process is to find the minimum merit function in a multidimensional variable space [13][14][15][16]. In each step of the optimization procedure, the search direction in terms of optimization variables has to be found [17][18][19].…”

Section: Introductionmentioning

confidence: 99%