Chaotic differential evolution algorithm based on simplex method for large-scale industrial processes of fuzzy model

Abstract

In the large-scale industrial processes, there are more slow perturbations. So the mathematical model of an actual system is difficult to be accurate. When optimizing large-scale industrial processes, the mathematical model and the actual system does not match, that is model-reality difference. In order to deal with this problem, the structure of decomposition and coordination is used in this paper. The whole large-scale industrial processes can be decomposed into several sub processes that are interactive, and the simplex method including open-loop simplex method and the simplex method with global feedback is the coordinate strategy. The main idea of the differential evolution algorithm with simple method is that find out the best individual in every generation with the standard differential evolution algorithm method, then around the best individual do local search for some times and compared the former best one with the ones from the local search, if the latter is better, substitute the best one with the latter. A classical example of large-scale industrial processes is applied and the simulation results show the validity of the method.