Linear Antenna Array Design With Use of Genetic, Memetic and Tabu Search Optimization Algorithms

Abstract

Antenna array design techniques are focused on two main classes: uniformly spaced antenna arrays and the non-uniform spacing case. These include techniques based on mathematical programming, such as constrained programming and non-linear programming. More recently, meta-heuristics approaches have been successful at designing antenna arrays [5]. In this work, this paper presents efficient methods of genetic algorithm (GA), memetic algorithm (MA) and tabu search algorithm (TSA) for the synthesis of linear antenna design. We present three examples of antenna array design to compare the efficiency of the algorithms through simple design to complex design. The GA, TSA and MA has been used to optimize the spacings between the elements of the linear array to produce a radiation pattern with minimum SLL and null placement control.

Uplink Carrier-to-Interference Improvement in a Cellular Telecommunication System When a Six-Beam Switched Parasitic Array Is Implemented

“…The use of genetic algorithms for antenna design purposes and electronic beam steering is a well-known method introduced in the last decade [20][21][22][23][24][25]. For the design of the antenna used in this paper, roulette wheel selection, simple crossover with p crossover = 0.8 and binary mutation with p mutation = 0.04 were employed.…”

Genetic Algorithm Optimization of High-Efficiency Wide-Band Multimodal Square Horns for Discrete Lenses

“…Genetic algorithms are stochastic search techniques that guide a population of solutions towards an optimum using the principles of evolution and natural genetics [19]. In recent years, genetic algorithms have become a popular optimization tool for many areas of research, including electromagnetics [19][20][21][22][23][24][25][26][27]. Both a proprietary GA-based optimizer [20][21][22][23][24] and the internal Matlab GA toolbox will be used here.…”

Deployment of the meta heuristic Colonial Competitive Algorithm in synthesis of unequally spaced linear antenna array

“…This method is used for thinning a large linear array of uniformly excited isotropic antennas to provide the SLL equal to or below a fixed level while the percentage of thinning is always kept equal to or above a given value. Some randomized algorithms were used to synthesize the unequally-spaced arrays such as Tabu search [7] and Particle Swarm [8]. In a more recent work, the authors used Pseudo Least Mean Square (LMS) and Fourier methods to minimize the SLL.…”