Open Access. Powered by Scholars. Published by Universities.®
Physical Sciences and Mathematics Commons™
Open Access. Powered by Scholars. Published by Universities.®
Articles 1 - 2 of 2
Full-Text Articles in Physical Sciences and Mathematics
A Neural Network: Family Competition Genetic Algorithm And Its Application In Electromagnetic Optimization, Chien Hsun Chen, P. Y. Chen, H. Weng
A Neural Network: Family Competition Genetic Algorithm And Its Application In Electromagnetic Optimization, Chien Hsun Chen, P. Y. Chen, H. Weng
Chien Hsun Chen
This study proposes a neural network-family competition genetic algorithm (NN-FCGA) for solving the electromagnetic (EM) optimization and other general-purpose optimization problems. The NN-FCGA is a hybrid evolutionary-based algorithm, combining the good approximation performance of neural network (NN) and the robust and effective optimum search ability of the family competition genetic algorithms (FCGA) to accelerate the optimization process. In this study, the NN-FCGA is used to extract a set of optimal design parameters for two representative design examples: the multiple section low-pass filter and the polygonal electromagnetic absorber. Our results demonstrate that the optimal electromagnetic properties given by the NN-FCGA are …
Synthesis Design Of Artificial Magnetic Metamaterials Using A Genetic Algorithm, Chien Hsun Chen, P. Y. Chen, H. Wang, J. H. Tsai, W. X. Ni
Synthesis Design Of Artificial Magnetic Metamaterials Using A Genetic Algorithm, Chien Hsun Chen, P. Y. Chen, H. Wang, J. H. Tsai, W. X. Ni
Chien Hsun Chen
In this article, we present a genetic algorithm (GA) as one branch of artificial intelligence (AI) for the optimization-design of the artificial magnetic metamaterial whose structure is automatically generated by computer through the filling element methodology. A representative design example, metamaterials with permeability of negative unity, is investigated and the optimized structures found by the GA are presented. It is also demonstrated that our approach is effective for the synthesis of functional magnetic and electric metamaterials with optimal structures. This GA-based optimization-design technique shows great versatility and applicability in the design of functional metamaterials.