Support vector machine firefly algorithm based optimization of lens system

Area of Science:

• Optical Engineering
• Computational Intelligence
• Image Quality Assessment

Background:

• Lens system design is critical for achieving high image quality.
• Optimization is a complex, nonlinear challenge in lens design.
• Soft computing algorithms offer potential solutions for optimization tasks.

Purpose of the Study:

• To develop and evaluate an intelligent soft computing technique for lens system design optimization.
• To utilize Support Vector Machines (SVM) coupled with the Firefly Algorithm (FFA) for this purpose.
• To assess the spot size radius as a key optimization criterion.

Main Methods:

• Implementation of a Support Vector Machines (SVM) and Firefly Algorithm (FFA) hybrid model.
• Utilizing spot diagrams to evaluate optical performance and define optimization criteria.
• Comparative analysis against Support Vector Regression (SVR), Artificial Neural Networks, and Generic Programming.

Main Results:

• The proposed SVM-FFA model achieved higher accuracy in lens system design optimization.
• Simulation results confirmed the effectiveness of the SVM-FFA approach.
• The SVM-FFA model outperformed SVR, ANNs, and GP methods.

Conclusions:

• The SVM-FFA model is an efficient soft computing technique for lens system design optimization.
• This approach offers improved accuracy and performance over existing methodologies.
• Intelligent optimization techniques are valuable for enhancing optical system design.