Open AccessNew hybrid genetic particle swarm optimization algorithm to design multi-zone binary filter
Author(s) -
Jie Lin,
Hongyang Zhao,
Yuan Ma,
Jiubin Tan,
Peng Jin
Publication year - 2016
Publication title -
optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.394
H-Index - 271
ISSN - 1094-4087
DOI - 10.1364/oe.24.010748
Subject(s) - algorithm , filter (signal processing) , benchmark (surveying) , particle swarm optimization , focus (optics) , binary number , genetic algorithm , meta optimization , computer science , multi swarm optimization , metaheuristic , convergence (economics) , mathematical optimization , optics , mathematics , physics , computer vision , arithmetic , geodesy , economic growth , economics , geography
The binary phase filters have been used to achieve an optical needle with small lateral size. Designing a binary phase filter is still a scientific challenge in such fields. In this paper, a hybrid genetic particle swarm optimization (HGPSO) algorithm is proposed to design the binary phase filter. The HGPSO algorithm includes self-adaptive parameters, recombination and mutation operations that originated from the genetic algorithm. Based on the benchmark test, the HGPSO algorithm has achieved global optimization and fast convergence. In an easy-to-perform optimizing procedure, the iteration number of HGPSO is decreased to about a quarter of the original particle swarm optimization process. A multi-zone binary phase filter is designed by using the HGPSO. The long depth of focus and high resolution are achieved simultaneously, where the depth of focus and focal spot transverse size are 6.05λ and 0.41λ, respectively. Therefore, the proposed HGPSO can be applied to the optimization of filter with multiple parameters.