A novel hybrid Differential Evolution-Estimation of Distribution Algorithm for dynamic optimization problem

In many engineering applications, the dynamic optimization problems with Ordinary Differential Equations (ODE) or Differential Algebraic Equations (DAE) constraints are encountered frequently. These types of problems are solved difficultly because of the characteristic of their nonlinear, multidimensional and multimodal. In this paper, a novel hybrid Differential Evolution (DE) and Estimation of Distribution Algorithm (EDA) is proposed for the dynamic optimization problems. A novel hybrid scheme based on DE and EDA (DEEDA) is designed to generate the offspring population. Using the DE-EDA, the population can reach a promising area in which the optimal solution is located speedily. A modified mutation scheme is proposed which can increase the diversity of the population. In addition, the modeling and sampling scheme based on empirical Copula is used to improve the speed of modeling and sampling. Eight optimal control optimization problems and one parameter estimation problem are tested to measure the performance of the algorithm. Experimental results show that the algorithm is feasible and effective.