Gaussian surrogate models for expensive interval multi-objective optimization problem

In this paper data mining (Gaussian process regression modeling) and intelligent evolutionary algorithm (GA, NSGA-II) are combined to solve the expensive interval multi-objective optimization problem with unknown optimization functions. Firstly, Gaussian process (GP) is used to model the objective functions and constraint functions represented by the midpoint and uncertainty. Because relevance and accuracy are two essential factors of interval function models, A kind of double steps screening strategy based on multiple attribute decision making (MADM) is proposed and it is embedded into the genetic algorithm to identify the parameters of the GP model. In the first step, inferior solutions in candidate solutions are excluded according to relevance. In the second step, the rest of inferior solutions beyond population quantity are excluded according to accuracy. And the proportion of inferior solutions excluded in the two steps is decided by the weight coefficient of two factors. Then, the built GP models for optimization objects are used as surrogate models in the NSGA-II optimization algorithm, so that Pareto front can be found. © 2016, Editorial Department of Control Theory & Applications South China University of Technology. All right reserved.