Genetic Algorithm is a search heuristic that mimics the process of evaluation. Genetic Algorithms can be applied to process controllers for their optimization using natural operators. This paper discusses the concept and design procedure of Genetic Algorithm as an optimization tool. Further, this paper explores the well established methodologies of the literature to realize the workability and applicability of genetic algorithms for process control applications. Genetic Algorithms are applied to direct torque control of induction motor drive, speed control of gas turbine, speed control of DC servo motor for the optimization of control parameters in this work. The simulations were carried out in simulink package of MATLAB. The simulation results show better optimization of hybrid genetic algorithm controllers than fuzzy standalone and conventional controllers.

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Genetic Algorithms: Concepts, Design for Optimization of Process Controllers

In this article, two particular methods for the synthesis of the complex radiation pattern of a linear and a planar array, based on genetic algorithms (GAs), are discussed. First, we present a combination of Schelkunoff's method and GAs for linear arrays with arbitrary radiation patterns; then, we consider the synthesis of planar arrays with rectangular cells. The results show the flexibility of GAs to solve complex problems related to antenna synthesis, subject to many restrictions which are difficult to treat by classical optimization techniques. There is good agreement between the desired and calculated radiation patterns.

Synthesis of antenna arrays using genetic algorithms

Linear and circular arrays are optimized using the particle swarm optimization (PSO) method. Also, arrays of isotropic and cylindrical dipole elements are considered. The parameters of isotropic arrays are elements excitation amplitude, excitation phase and locations, while for dipole array the optimized parameters are elements excitation amplitude, excitation phase, location, and length. PSO is a high-performance stochastic evolutionary algorithm used to solve N-dimensional problems. The method of PSO is used to determine a set of parameters of antenna elements that provide the goal radiation pattern. The efiectiveness of PSO for the design of antenna arrays is shown by means of numerical results. Comparison with other methods is made whenever possible. The results reveal that design of antenna arrays using the PSO method provides considerable enhancements compared with the uniform array and the synthesis obtained from other optimization techniques.

/pdf/linear-and-circular-array-optimization-a-study-using-59epvbaudv.pdf

Linear and Circular Array Optimization: a Study Using Particle Swarm Intelligence

An optimization method, such as the steepest gradient methods, could not easily obtain globally optimum solutions for devising antenna design parameters that allow the antenna to simultaneously improve multiple performances such as gain, sidelobe level, and input impedance. The genetic algorithm (GA) is suitable for empirically solving optimization problems and is effective in designing an antenna. In particular, this method can solve the multiobjective optimization problem using various Pareto-optimal solutions in an extremely efficient manner. In this paper, the Pareto GA, by which various Pareto-optimal solutions for each objective function (performance) can be obtained in a single trial of a numerical simulation and which enables the selection of parameters in accordance with the design requirement, is applied to the multiobjective optimization design of the Yagi-Uda antenna. The effectiveness of the Pareto GA was demonstrated by comparing the performances obtained by the Pareto GA with those of the previously reported values, which were obtained by the conventional GA, and with the values of the design benchmark reference.

Multiobjective optimization design of Yagi-Uda antenna

A parallel genetic algorithm (GA) optimization tool has been developed for the synthesis of arbitrarily shaped beam coverage using planar 2D phased-array antennas. Typically, the synthesis of a contoured beam footprint using a planar 2D array is difficult because of the inherently large number of degrees of freedom involved (in general, the amplitude and phase of each element must be determined). We make use of a parallel GA tool in this study to compensate for this aspect of the design problem. The algorithm essentially compares a desired pattern envelope with that of trial arrays, and quantifies the effectiveness or desirability of each test case via a fitness function. The GA uses this information to rank and select subsequent arrays over a given number of generations via the conventional stochastic operators, i.e., selection, crossover, and mutation. Each fitness evaluation of a trial pattern is done on a node of the aerospace fellowship cluster supercomputer, which increases the speed of the algorithm linearly with the number of nodes. Because of the continuous nature of the parameters for this optimization problem, a real parameter encoding scheme is employed for the GA chromosome in order to avoid the quantization errors associated with a binary representation. A benchmark 10 times 10 (100) element array is employed, and various results of optimized coverage patterns are shown herein to illustrate the effectiveness and validity of the technique.

Parallel Genetic-Algorithm Optimization of Shaped Beam Coverage Areas Using Planar 2-D Phased Arrays

A method of using genetic algorithms to optimize the element spacing and lengths of Yagi-Uda antennas is presented. A method of moments code, NEC2, performs the task of evaluating each of the antenna designs generated by the genetic algorithm (GA) during the optimization process. To illustrate the capabilities of the method, the length and spacing of several Yagi-Uda antennas are optimized for various performance characteristics. The results are compared to published results from other optimization techniques and to well-designed equally spaced arrays.

Design of Yagi-Uda antennas using genetic algorithms

Discusses a way to optimize both the topology and the numerical parameters of an antenna design. The approach relies on an "antenna language" to define how antennas are constructed, and a genetic algorithm to create new designs using this language. The grammatical rules of a language can be very vague or very specific, depending on the purpose of the designer. With a vague grammar, genetic algorithms search a very large design space, and can occasionally find unexpected solutions to a design problem. Other times, they completely fail to find a reasonable solution because of the vastness of the search space. In this case, including knowledge about the problem into the grammar narrows the search to a region expected to yield good results. This yields more conventional design solutions that usually perform reasonably well. In an example, two languages were used to design a linear antenna array. The general language allowed a wide variety of designs, while the Yagi-log language confined the search to topologies known to perform well. The performance of the antennas produced by both languages was superior to that of a conventional log-periodic design. Further, the Yagi-log design was more fit than the unconventional design from the general language, illustrating the benefits of including knowledge in the grammar.

E.A. Jones

Papers

Design of Yagi-Uda antennas using genetic algorithms

Genetic design of linear antenna arrays