Literature Review of Genetic Algorithm in Power System
Diksha Shende, Sayali Morey, S. R. Gawande · International Journal For Science Technology And Engineering · 2016
The utilization of genetic algorithm (GA) in tackling engineering problems has been a major issue arousing the curiosity of researcher and practitioner system and engineering research, operation research and management sciences in last few years. The various improvement occurs in it year by year, and researches has been done over genetic algorithm to improve its limitation and to process well. In view of this, this paper present a state-of-the-art survey of application of GA technique in engineering with focus on system power optimization using GA in last few years to understand what changes has been done till now and its improvement of various papers that are searched. The scope of the paper is centered between the years 2000-2016. The vast areas of application of GA optimization technique in tackling problem that cannot be handled using the conventional methods and stochastic search are the focal areas of keen interest for consideration in this paper. GA is type of evolutionary algorithm (EA) that is found useful in so many engineering applications which includes numerical and combinatorial optimization problem, filter design as in the field of signal processing, designing of communication networks, semiconductor layout and spacecraft and so on. It is founded on the bases of natural biological evaluation process which is used to mimic nature in searching for optimal solution of a specific problem. In the description of GA, the definition of chromosome and fitness functions is of paramount importance. Chromosomes are abstract representation of candidate solution. The fitness function is used in quantifying the desirability of the solution, which is closely correlated with the objective of the algorithm or optimization process. The fitness level is used in evaluating candidate solution, that is, the values being generated characterize the solutions. In GA, the most promising search space areas are being explored through the utilization of probabilistic rule, hence minimizing the risks of convergence to local minima. This is achieved by simultaneously considering many points in the search space and favoring the mating of the fitter individuals. GA is a robust search algorithm that enables the quick location of high quality solution areas in a complex and large Search space. Among the numerous advantages of GA is its Capability of considering individual population with each population representing a solution to the problem which gives its edge over other search algorithm. The fundamental principal of GA includes selection, reproduction, population solution, encoding and decoding, fitness function evaluation and convergence. This paper presents a concise detailed survey of applications of GA technique in engineering with focus on system power optimization. Finally, conclusions are presented.