Wire antennas optimized using genetic algorithm (original) (raw)

Genetic algorithm-based Ku-band microstrip patch antennas optimization to avoid jamming attacks

Revista Brasileira de Aplicações de Vácuo

This paper reflects on the design and optimization of patch antennas. Two different methods were conducted. First, optimization was carried out over the whole antenna extension. Second, half antenna was optimized, and the final design was obtaining by reflection. The optimization process was conducted using genetic algorithm, return loss was obtained with full wave finite-differences time-domain, and the initial configuration (design) was obtained with line transmission and cavite method. All methods implemented in-house software. The antennas were designed to operate in Ku band with the center frequency at 16 GHz. Antenna with return greater than 22 dB and bandwidth between 2-5 GHz were obtained. The effectiveness of the proposed designs was confirmed through proper simulation results. Such optimization would aim to make a communication system more robust to electronic warfare attacks.

Optimization of the performance of patch antennas using genetic algorithms

Journal of the National Science Foundation of Sri Lanka, 2013

Patch antenna is a widely used antenna type in many applications. These antennas are low-profile, cheap, conformable to planar and non planar surfaces, simple to fabricate using printed circuit technology and compatible with monolithic microwave integrated circuit (MMIC) designs. However, their narrow bandwidth and low efficiency are the major drawbacks. In this study, genetic algorithm optimization (GAO) method was used to design the shape of the patch, feed position, thickness of the dielectric substrate and the substrate material simultaneously in order to optimize both bandwidth and gain. It was found that thin broadband fragmented single probe feed patch antennas with-10 dB impedance bandwidths up to almost 2:1 can be easily designed using GAO. The antennas were simulated using high frequency structure simulator (HFSS) and the results were validated using measurements.

DEVELOPING AN ACTUAL VERY HIGH FREQUENCY ANTENNA USING GENETIC ALGORITHMS

Antenna for the 88-108MHz Very high frequency (VHF) broadcast audio frequency-modulation (FM) band. The antenna is intended tofit in the flat area inside the head-band of an over ear hearing-protector headset. The space for the antenna is limited by an existing head-band design, where the unused internal area is the space studied in this thesis. A genetic algorithm is described for the multiple objective optimization of the antenna matching and radiation pattern optimization. The results of multiple genetic algorithm evaluations are described, and possible further improvements outlined. Progress is made on the development of the antenna. The antenna radiation pattern is evolved in desirable way, but a difficulty in solving the antenna matching problem is identified. Research for resolving the antenna matching problem is described in this paper

Design of Dual Band Patch Antennas for Cellular Communications by Genetic Algorithm Optimization

International Journal of Engineering & Technology, 2012

Designing multiband antennas with low volume becomes of practical interest for mobile telecommunications. This paper presents the designs of five small dual band patch antennas for GSM1800 (1710-1880MHz) and Bluetooth (2400-2483.5MHz) applications using a genetic algorithm combined with MoM (Method of Moments). A substrate with dielectric constant 3.2 and height 8mm is used for the first two dual band designs. The height is reduced thanks to the optimization process to 6mm in the third design by inserting a shorting pin to the fragmented patch antenna. The height is further reduced to 4mm in the by inserting two shorting pins. In the final design with three shorting pins, the height is only 3mm. The patch dimensions are similar to that of the conventional rectangular patch for the center frequency of the lowest frequency band but with the advantage of having dual-band operation at the desired bands. Genetic algorithm optimization is used to optimize the patch geometry, feed position and shorting positions. HFSS is used to carry out simulations. The antenna thickness is reduced from Design of Dual Band Patch Antennas 27 8mm to 3mm by incorporating shorting pins which position is optimized by the genetic algorithm.

Wire-antenna geometry design with multiobjective genetic algorithms

International Conference on Evolutionary Computation, 2002

Two different multiobjective genetic algorithms, built using the GENOCOP III system are employed, for the design of wire antenna geometries. Designs are examined using a priori and a posteriori decision criteria. The relative advantages of each of these criteria and their applicability to the problem domain are examined

Microstrip antenna optimization using genetic algorithms

2010

The design of a micro-strip patch antenna is proposed by optimizing its resonant frequency, Bandwidth of operation and Radiation resistance using Genetic Algorithm (GA). GA is based on the mechanics of natural genetics and natural selection and good at taking larger, potentially huge, search spaces and navigating them looking for optimal combinations of solutions which we might not find in life time .Optimizing radiation resistance operational bandwidth as high as 25.52 % and return loss -47.5dB is obtained without any complexity of design. The antenna can be used for various applications in fields of mobile communication, satellite communication, RFID, GPS, Radar communication etc.

Teaching Genetic Algorithms and Parameters Setup on Antenna Design

INTERNATIONAL JOURNAL OF ENGINEERING RESEARCH & TECHNOLOGY (IJERT), 2022

Evolutionary algorithms are widely being used on any engineering field where optimizations are required over complex problems. Genetic Algorithm (GA) is one of the most popular algorithms over the evolutionary ones. GA are being taught on graduated and post-graduated university courses. Despite this wide spread of the GA usage and variety of applicable field, a correct setup of GA parameters is still not uniquely defined. In this material, the influence of GA parameters setup such as number of populations on each generation, elitism coefficient, mutation function used to generate and push genetic modifications over the population, parent selection functions and reproduction functions choice parameter influence are also analyzed. All parameters are being analyzed on a simple Yagi-Uda constrained antenna problem as a simple test case where linear constraints and genetic coding of variables are of immediate understanding to the reader. At the end, some discussions on the available functions used on GA and different parameter setup and their influence on the GA results are drown. The material concludes with conclusions and bibliographic references designed to help the reader expand his knowledge on optimization algorithms and in particular in the correct use of GA functions on constrained optimization problems.

Multi-Objective Optimization of Wire Antennas: Genetic Algorithms Versus Particle Swarm Optimization

Radioengineering

The paper is aimed to the multi-objective optimization of wire multi-band antennas. Antennas are numerically modeled using time-domain integral-equation method. That way, the designed antennas can be characterized in a wide band of frequencies within a single run of the analysis. Antennas are optimized to reach the prescribed matching, to exhibit the omni-directional constant gain and to have the satisfactory polarization purity. Results of the design are experimentally verified. The multi-objective cost function is minimized by the genetic algorithm and by the particle swarm optimization. Results of the optimization by both the multi-objective methods are in detail compared. The combination of the time domain analysis and global optimization methods for the broadband antenna design and the detailed comparison of the multi-objective particle swarm optimization with the multi-objective genetic algorithm are the original contributions of the paper.