Multiple Surrogate-Model-Based Optimization Method Using the Multimodal Expected Improvement Criterion for Expensive Problems

Surrogate-Assisted Artificial Immune Systems for Expensive Optimization Problems

A Stopping Criterion for Surrogate Based Optimization using EGO

In Surrogate-based optimization, each optimization cycle consists of fitting a surrogate to a number of simulations at a set of design points, and performing optimization based on the surrogate to obtain one or more new design points. Algorithms like Efficient Global Optimization (EGO) [1] use uncertainty estimates available with the Kriging surrogate to guide the selection of new point(s). The...

Evolutionary Optimization of Computationally Expensive Problems via Surrogate Modeling

We present a parallel evolutionary optimization algorithm that leverages surrogate models for solving computationally expensive design problems with general constraints, on a limited computational budget. The essential backbone of our framework is an evolutionary algorithm coupled with a feasible sequential quadratic programming solver in the spirit of Lamarckian learning.We employ a trust-regi...

A multi-fidelity surrogate-model-assisted evolutionary algorithm for computationally expensive optimization problems

Integrating data-driven surrogate models and simulation models of di erent accuracies (or delities) in a single algorithm to address computationally expensive global optimization problems has recently attracted considerable attention. However, handling discrepancies between simulation models with multiple delities in global optimization is a major challenge. To address it, the two major contrib...

APPLICATION OF KRIGING METHOD IN SURROGATE MANAGEMENT FRAMEWORK FOR OPTIMIZATION PROBLEMS

In this paper, Kriging has been chosen as the method for surrogate construction. The basic idea behind Kriging is to use a weighted linear combination of known function values to predict a function value at a place where it is not known. Kriging attempts to determine the best combination of weights in order to minimize the error in the estimated function value. Because the actual function value...