Solution of singular optimal control problems using the improved differential evolution algorithm

The Differential Evolution algorithm, like other evolutionary techniques, presents as main disadvantage the high number of objective function evaluations as compared with classical methods. To overcome this disadvantage, this work proposes a new strategy for the dynamic updating of the population size to reduce the number of objective function evaluations. This strategy is based on the definition of convergence rate to evaluate the homogeneity of the population in the evolutionary process. The methodology is applied to the solution of singular optimal control problems in chemical and mechanical engineering. The results demonstrated that the methodology proposed represents a promising alternative as compared with other competing strategies.