Multiple Surrogate Assisted Multiobjective Optimization using Improved Pre-selection

In multiobjective engineering design, evaluation of a single design (solution) often requires running one or more computationally expensive simulation models. Surrogate assisted optimization (SAO) approaches have long been used for solving such problems, in which approximations/surrogates are used in lieu of computationally expensive simulations during the course of search. Existing SAO approaches use a variety of surrogate models and model management strategies, and the best choice is still a matter under investigation. Our current proposal is an attempt to exploit the best features of several strategies, and in particular compares two possible versions of pre-selection in multiobjective optimization. The proposed algorithm is based on the non-dominated sorting genetic algorithm (NSGA-II) but, instead of evaluating the potential offspring solutions directly, a surrogate assisted evolutionary search is conducted in the neighborhood of every offspring solution using the best local surrogate model (among Kriging, Radial basis function (RBF), Polynomial response surface method (RSM) of order 1 and 2 and Multilayer perceptrons (MLP)). Out of the combined set of candidate solutions generated using the above step, the most promising offspring solutions are pre-selected, and we examine and compare two versions of pre-selection, one ignoring the parents and one taking the parents into account. The performance of the proposed approach is studied using a number of well known numerical benchmarks and engineering design optimization problems.