Search of Initial Conditions for Dynamic Systems using Intelligent Optimization Methods

In this contribution we propose the use of intelligent optimization methods in the search of initial conditions for the analysis of dynamic systems. The use of intelligent optimization methods provides a search tool that does not depend on the experience of the researcher in the particular system to analyze. An example of a dynamic system that models an electrical power system is provided. Three intelligent optimization methods are compared: genetic algorithms, multimodal genetic algorithms, and particle swarm optimization. An analysis of precision and error is presented, contrasting the three methods.