Adaptive and parallel capabilities in the Multipoint Approximation Method

INTRODUCTION

In the present work the Multipoint Approximation Method (MAM) has been enhanced with new capabilities that allow to solve large scale design optimization problems more efficiently. The first feature is adaptive building of approximate models during the optimization search. And the second feature is a parallel implementation of MAM. A traditional approach to adaptive building of metamodels is to check several types for their quality on a set of design points and select the best type. The technique presented in this paper is based on the assembly of multiple metamodels into one model using linear regression. The obtained coefficients of the model assembly are not weights of the individual models but regression coefficients determined by the least squares minimization method. The enhancements were implemented within Multipoint Approximation Method (MAM) method related to mid-range approximation framework. The developed technique has been tested on several benchmark problems.