Abstract:

In order to deal with the nonlinear optimization problems of three-dimentional (3D) gaze tracking technology efficiently and make the system satisfy the requirements of real-time, accuracy and stability, this paper presents a hybrid algorithm, known as differential evolution-shuffled frog leaping-Nelder-Mead (DE-SFL-NM), which is based on the differential evolution algorithm and takes full advantages of the shuffled frog leaping algorithm and the NelderMead simple method. The convergence of DE-SFL-NM is discussed by exploring infinite products property, which arrives at the conclusion of probabilistic convergence. Numerical experiments are designed for verifying the effectiveness and advancement of the proposed algorithm in terms of convergence rate, precision and robustness on 10 benchmark functions including single mode and multi-modal. Meanwhile, the center of corneal curvature of 3D gaze tracking system can be accurately estimated by using DE-SFL-NM.