An Optimization Algorithm Employing Multiple Metamodels and Optimizers

Abstract

Modern engineering design optimization often relies on computer simulations to evaluate candidate designs, a setup which results in expensive black-box optimization problems. Such problems introduce unique challenges, which has motivated the application of metamodel-assisted computational intelligence algorithms to solve them. Such algorithms combine a computational intelligence optimizer which employs a population of candidate solutions, with a metamodel which is a computationally cheaper approximation of the expensive computer simulation. However, although a variety of metamodels and optimizers have been proposed, the optimal types to employ are problem dependant. Therefore, a priori prescribing the type of metamodel and optimizer to be used may degrade its effectiveness. Leveraging on this issue, this study proposes a new computational intelligence algorithm which autonomously adapts the type of the metamodel and optimizer during the search by selecting the most suitable types out of a family of candidates at each stage. Performance analysis using a set of test functions demonstrates the effectiveness of the proposed algorithm, and highlights the merit of the proposed adaptation approach.

Reduced Order Modeling & Controller Design for Mass Transfer in a Grain Storage System

“…Here all the components of z * are taken as 1 000. The desired humidity profiles U * corresponding to z * can be obtained using (16). The nodal humidity profiles can be obtained from z(t).…”

“…The approach involved discretization of governing equations with FE method and obtaining a reduced nonlinear dynamic system of much smaller size with almost same dynamic property as the original using POD method. A controller based on optimal control theory [16] is designed for the reduced model so that the output reaches to a desired profile. It has also been shown that the controller acts fairly good for the original model as well.…”

Efficient Use of Partially Converged Simulations in Evolutionary Optimization

“…This measure is then used to select the most suitable surrogate model. Tenne [27] proposes a framework in which the algorithm to construct surrogate models as well as the search algorithm is adapted online during the run.…”

Machine–Learning in Optimization of Expensive Black–Box Functions

“…Common metamodel variants include artificial neural networks, Kriging, polynomials, and radial basis functions (RBFs) (Wortmann et al, 2015;Forrester and Keane, 2008;Queipo et al, 2005;Regis, 2014;Viana et al, 2013;Tenne, 2013). Metamodel-assisted frameworks typically operate by first training a metamodel and then seeking its optimum.…”