Synthesis of thinned concentric circular antenna arrays using teaching-learning-based optimization

Abstract

In this article, the design of thinned concentric circular antenna arrays (CCAAs) of isotropic radiators with optimum side lobe level (SLL) reduction is studied. The newly proposed global evolutionary optimization method; namely, the teaching‐learning‐based optimization (TLBO) is used to determine an optimum set of turned ON elements of thinned CCAAs that provides a radiation pattern with optimum SLL reduction. The TLBO represents a new algorithm for optimization problems in electromagnetics and antennas. It is shown that the TLBO provides results that are somewhat better than those obtained using other evolutionary algorithms, like the firefly algorithm and biogeography based optimization. © 2013 Wiley Periodicals, Inc. Int J RF and Microwave CAE 24:443–450, 2014.

Pattern Synthesis of Linear Antenna Arrays Using Enhanced Flower Pollination Algorithm

“…Antenna arrays design is intricate and nonlinear problem. Hence number of optimization techniques such as genetic algorithm (GA), differential algorithm (DE), particle swarm optimization (PSO), biogeography based optimization (BBO) and many others [2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21] have been used to synthesize these. It is required that antenna arrays radiate in desired directions so that these do no add to electromagnetic pollution.…”

Synthesis of Thinned Concentric Circular Antenna Arrays Using Modified TLBO Algorithm

“…The main advantage of TLBO is less algorithm specified parameters; it requires only common controlling parameters like population size and number of generations. It has been regarded as a new rising star of evolutionary algorithms and successfully applied in many engineering optimization problems [22,23]. In our previous work, a modified version of teaching-learning-based optimization (MTLBO) was proposed for thinning and weighting rectangular antenna arrays [24].…”

Design of non-uniform concentric circular antenna arrays with optimal sidelobe level reduction using biogeography-based optimization

“…In general, the circular array optimization problem is more complicated than the linear array optimization [2–8]. Recently, different well-known evolutionary optimization techniques such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Invasive Weed Optimization (IWO), Differential Evolution (DE), Evolutionary Programming (EP), Firefly Algorithm (FA), Bee Colony Algorithms, and Teaching–Learning-Based Optimization (TLBO), have been used in the synthesis of CCAAs [2–15].…”