Open AccessOptimization of metamaterial based weighted real-coded genetic algorithm
Author(s) -
Hongwei Chen,
Hui Ma,
Jieqiu Zhang,
Zhiyuan Zhang,
Zhao Xu,
Jiafu Wang,
Qu Shao-Bo
Publication year - 2014
Publication title -
wuli xuebao
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.199
H-Index - 47
ISSN - 1000-3290
DOI - 10.7498/aps.63.087804
Subject(s) - computer science , algorithm , metamaterial , coding (social sciences) , computation , genetic algorithm , binary number , meta optimization , convergence (economics) , selection (genetic algorithm) , optimization problem , artificial intelligence , mathematics , physics , machine learning , optics , statistics , arithmetic , economics , economic growth
A weighted real-coded genetic algorithm is proposed in this paper, in allusion to the characteristics of optimizing designs of metamaterials, which have many parameters and parameters of different powers in metamaterial structure. The algorithm utilizing allele or double gene to change the power of one gene, develops the idea of weighted encoded binary coding. Compared with common real-coded based genetic algorithm, the weighted real-coded genetic algorithm adds artificial selection in it, it can not only accelerate the speed of convergence, but also improve the solution quality, especially for the design of large-scale and long computation time. In this paper, the algorithm is verified by optimizing a metamaterial absorber.