Using statistical analysis to tune an evolutionary algorithm for dynamic optimization with progressive step reduction

We describe the use of an evolutionary algorithm (EA) to solve dynamic control optimization problems in engineering. In this class of problems, a set of control variables must be manipulated over time to optimize the outcome, which is obtained by solving a set of differential equations for the state variables. A problem-specific technique, progressive step reduction (PSR), is shown to considerably improve the efficiency of the algorithm for this application. Factorial experimentation and rigorous statistical analysis are used to determine the effects of PSR and tune the parameters of the algorithm.