Abstract
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.
Highlights
Evolutionary algorithms (EAs) are population-based meta heuristic optimization algorithms that use biology-inspired mechanisms and survival of the fittest theory in order to refine a set of solution iteratively
Genetic algorithms (GAs) are computer based search techniques patterned after the genetic mechanisms of biological organisms that have adapted and flourished in changing highly competitive environment
Selection is an important function in genetic algorithms (GAs), based on an evaluation criterion that returns a measurement of worth for any chromosome in the context of the problem
Summary
Evolutionary algorithms (EAs) are population-based meta heuristic optimization algorithms that use biology-inspired mechanisms and survival of the fittest theory in order to refine a set of solution iteratively. Last decade has witnessed many exciting advances in the use of genetic algorithms (GAs) to solve optimization problems in process control systems. Genetic algorithms (GAs) are the solution for optimization of hard problems quickly, reliably and accurately. Genetic algorithms (GAs) may contain a chromosome, a gene, set of population, fitness, fitness function, breeding, mutation and selection. Genetic algorithms (GAs) begin with a set of solutions represented by chromosomes, called population. The basic genetic algorithm (GAs) is outlined as below: Step I [Start] Generate random population of chromosomes, that is, suitable solutions for the problem. Encoding techniques in genetic algorithms (GAs) are problem specific, which transforms the problem solution into chromosomes. Various encoding techniques used in genetic algorithms (GAs) are binary encoding, permutation encoding, value encoding and tree encoding
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