GENETIC ALGORITHMS IN ELECTRICAL ENGINEERING

Abstract

In view of the exponential increase in electricity demand, driven by technological development and rapid urbanization, heuristic optimization techniques have emerged as crucial tools in electrical engineering to promote efficiency and sustainability. This study explores Genetic Algorithms (GAs), a methodology inspired by Darwinian evolution, highlighting their versatility in solving complex optimization problems involving nonlinearities, multiple constraints, and stochastic uncertainties. Through an in-depth theoretical analysis, the conceptual structure of GAs is discussed, including their essential operators such as selection, crossover, and mutation, as well as potential applications in areas such as power system planning, control of electrical devices, parameter estimation, and distribution network optimization. The specialized literature shows that GAs outperform conventional approaches, such as deterministic methods, in multivariable and high-dimensional scenarios, fostering efficient, adaptable, and robust solutions that contribute to reducing operational costs and environmental impacts. Furthermore, inherent limitations, such as computational time and parameter sensitivity, are addressed, suggesting hybridizations with other metaheuristics for improvement.