Optimization of an Offset Reflector Antenna Using Genetic Algorithms (original) (raw)

Genetic algorithm design of antenna arrays

1996 IEEE Aerospace Applications Conference. Proceedings

Genetic algorithms are capable of optimizing the performance of antenna arrays. They model biological evolution to find the parameters that optimize the output of a function. In this paper, the genetic algorithms find the quantized phase weights that opljmize the sidelobe levels of an array over its scailning region. TABLE OF CONTENTS 1. INTRoDUCrION 2. MODELING ANTENNA ARRAYS 4. RESULTS

A Simple Real-Coded Compact Genetic Algorithm and its Application to Antenna Optimization

2007

Based on Harik's compact genetic algorithm, in this paper a real-coded type of compact genetic algorithm, RCGA, based on probability distribution function of each gene is developed. The algorithm is applied to design a new small-size tapered monopole ultra-wideband antenna as a practical utilization. The antenna is a modified PTMA which is able to improve the bandwidth using eleven degrees of freedom. The final optimized antenna design works from 3.1 to 11.7 GHz and has only 48% of the size compared to previous work.

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

An introduction to genetic algorithms for electromagnetics

IEEE Antennas and Propagation Magazine, 1995

This article is a tutorial on using genetic algorithms to optimize antenna and scattering patterns. Genetic algorithms are "global" numerical-optimization methods, patterned after the natural processes of genetic recombination and evolution. The algorithms encode each parameter into binary sequences, called a gene, and a set of genes is a chromosome. These chromosomes undergo natural selection, mating, and mutation, to arrive at the final optimal solution. After providing a detailed explanation of how a genetic algorithm works, and a listing of a M i Z A B code, the article presents three examples. These examples demonstrate how to optimize antenna patterns and backscattering radar-cross-section patterns. Finally, additional details about algorithm design are given.

Genetic algorithms in the design and optimization of antenna array patterns

IEEE Transactions on Antennas and Propagation, 1999

In this paper, we propose an optimization method based on real-coded genetic algorithm (GA) with elitist strategy for thinning a large linear array of uniformly excited isotropic antennas to yield the maximum relative sidelobe level (SLL) equal to or below a fixed level. The percentage of thinning is always kept equal to or above a fixed value. Two examples have been proposed and solved with different objectives and with different value of percentage of thinning that will produce nearly the same sidelobe level. Directivities of the thinned arrays are found out and simulation results of different problems are also compared with published results to illustrate the effectiveness of the proposed method.

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.

Application of genetic algorithms for optimization of a FSS reflectarray antenna

… Theory, 2004. 10th …, 2004

This paper presents an engineering application of optimization method for energy conservation in condenser water loop operation in HVAC (Heating, Ventilation and Air Conditioning) systems. Based on the analysis of component model characteristics and interactions within the cooling tower and between towers and chillers, an objective function, which minimizes the operating costs through optimal set point changes of controlled variables along with changes of outdoor environment and indoor cooling load demands, is proposed. A modified genetic algorithm is employed to optimize the condenser water loop in HVAC systems. The approach has the capability of solving the combinatorial optimization problem with both discrete variables and continuous variables. Compared with the conventional method, this method has potential to substantially reduce the operating costs and is suitable to deal with both single chiller and multi-chiller central plants.

Genetical swarm optimization: an evolutionary algorithm for antenna design

AUTOMATIKA- …, 2006

In this paper a new effective optimization algorithm called Genetical Swarm Optimization (GSO) is presented. This is an hybrid algorithm developed in order to combine in the most effective way the properties of two of the most popular evolutionary optimization approaches now in use for the optimization of electromagnetic structures, the Particle Swarm Optimization (PSO) and Genetic Algorithms (GA). This algorithm is essentially, as PSO and GA, a population-based heuristic search technique, which can be used to solve combinatorial optimization problems, modeled on the concepts of natural selection and evolution (GA) but also based on cultural and social rules derived from the analysis of the swarm intelligence and from the interaction among particles (PSO). Preliminary analyses are here presented with respect to the other optimization techniques dealing with a classical optimization problem. The optimized design of a printed reflectarray antenna is finally reported with numerical results.

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.