Constrained optimization involving expensive function evaluations: A sequential approach

Constrained optimization involving expensive function evaluations: A sequential approach

This paper presents a new sequential method for constrained non-linear optimization problems. The principal characteristics of these problems are very time consuming function evaluations and the absence of derivative information. Such problems are common in design optimization, where time consuming function evaluations are carried out by simulation tools (e.g., FEM, CFD). Classical optimization...

Constrained Evolutionary Optimization - the penalty function approach

Estimating the Parameters in Photovoltaic Modules: A Constrained Optimization Approach

This paper presents a novel identification technique for estimation of unknown parameters in photovoltaic (PV) systems. A single diode model is considered for the PV system, which consists of five unknown parameters. Using information of standard test condition (STC), three unknown parameters are written as functions of the other two parameters in a reduced model. An objective function and ...

Expensive Function Optimization with Stochastic Binary Outcomes

Real world systems often have parameterized controllers which can be tuned to improve performance. Bayesian optimization methods provide for efficient optimization of these controllers, so as to reduce the number of required experiments on the expensive physical system. In this paper we address Bayesian optimization in the setting where performance is only observed through a stochastic binary o...

An Efficient Adaptive Sequential Methodology for Expensive Response Surface Optimization

The preset response surface methodology (RSM) designs are commonly used in a wide range of process and design optimization applications. Although they offer ease of implementation and good performance, they are not sufficiently adaptive to reduce the required number of experiments and thus are not cost effective for applications with high cost of experimentation. We propose an efficient adaptiv...