A Building-Block Favoring Method for the Topology Optimization of Internal Antenna Design. (2nd August 2015)

• Record Type: Journal Article
• Authors: Chen, Yen-Sheng
• Other Names: Anguera Jaume Academic Editor.
• Is Part Of: International journal of antennas and propagation. Volume 2015(2015)
• Publication Date: 2015-08-02
• Subjects: Electronic apparatus and appliances -- Periodicals; Antennas (Electronics) -- Periodicals; Radio wave propagation -- Periodicals; Antennes (Électronique); Ondes radioélectriques -- Propagation; Antennas (Electronics); Electronic apparatus and appliances; Radio wave propagation; Electronic journals; Periodicals; 621.382405
• DOI: 10.1155/2015/206243
• Languages: English
• ISSNs: 1687-5869

Abstract

Abstract : This paper proposes a new design technique for internal antenna development. The proposed method is based on the framework of topology optimization incorporated with three effective mechanisms favoring the building blocks of associated optimization problems. Conventionally, the topology optimization of antenna structures discretizes a design space into uniform and rectangular pixels. However, the defining length of the resultant building blocks is so large that the problem difficulty arises; furthermore, the order of the building blocks becomes extremely high, so genetic algorithms (GAs) and binary particle swarm optimization (BPSO) are not more efficient than the random search algorithm. In order to form tight linkage groups of building blocks, this paper proposes a novel approach to handle the design details. In particular, a nonuniform discretization is adopted to discretize the design space, and the initialization of GAs is assigned as orthogonal arrays (OAs) instead of a randomized population; moreover, the control map of GAs is constructed by ensuring the schema growth based on the generalized schema theorem. By using the proposed method, two internal antennas are thus successfully developed. The simulated and measured results show that the proposed technique significantly outperforms the conventional topology optimization.