Search

This study proposes efficient progressive polynomial approximate optimization (ePPAO) which is a new function-based sequential approximate optimization algorithm. ePPAO finds an improved design than an initial design in a short amount of time and at low-cost. The proposed method adopts a screening method in every iteration to improve efficiency for large-scale problems. A new move limit strategy as a region management technique was developed based on the concept of the trust region method. To evaluate the proposed method, we solved well-known unconstrained and constrained problems and compared the results with two different algorithms from commercial software similar to the ePPAO concepts.. In addition, the proposed method is applied to an industrial design problem. [ABSTRACT FROM AUTHOR]

This study compares the performance of popular sampling methods for computer experiments using various performance measures to compare them. It is well known that the sample points, in the design space located by a sampling method, determine the quality of the meta-model generated based on expensive computer experiment (or simulation) results obtained at sample (or training) points. Thus, it is very important to locate the sample points using a sampling method suitable for the system of interest to be approximated. However, there is still no clear guideline for selecting an appropriate sampling method for computer experiments. As such, a sampling method, the optimal Latin hypercube design (OLHD), has been popularly used, and quasi-random sequences and the centroidal Voronoi tessellation (CVT) have begun to be noticed recently. Some literature on the CVT asserted that the performance of the CVT was better than that of the LHD, but this assertion seems unfair because those studies only employed space-filling performance measures in favor of the CVT. In this research, we performed the comparison study among the popular sampling methods for computer experiments (CVT, OLHD, and three quasi-random sequences) with employing both space-filling properties and a projective property as performance measures to fairly compare them. We also compared the root mean square error (RMSE) values of Kriging meta-models generated using the five sampling methods to evaluate their prediction performance. From the comparison results, we provided a guideline for selecting appropriate sampling methods for some systems of interest to be approximated. [ABSTRACT FROM AUTHOR]

As the use of meta-models to replace computationally-intensive simulations for estimating real system behaviors increases, there is an increasing need to select appropriate meta-models that well represent real system behaviors. Since in most cases designers do not know the behavior of the real system a priori, however, they often have trouble selecting a suitable meta-model. In order to provide robust prediction performance, ensembles of meta-models have been developed which linearly combines stand-alone meta-models. In this study, we propose a new pointwise ensemble of meta-models whose weights vary according to the prediction point of interest. The suggested method can include all kinds of stand-alone meta-models for ensemble construction, and can interpolate real system response values at training points, even if regression models are included as stand-alone meta-models. To evaluate the effectiveness of the proposed method, its prediction performance is compared with those of existing ensembles of meta-models using well-known mathematical functions. The results show that our pointwise ensemble of meta-models provides more robust and accurate predictions than existing models for a majority of test problems. [ABSTRACT FROM AUTHOR]