The University of Southampton
University of Southampton Institutional Repository

A hybrid one-then-two stage algorithm for computationally expensive electromagnetic design optimization

A hybrid one-then-two stage algorithm for computationally expensive electromagnetic design optimization
A hybrid one-then-two stage algorithm for computationally expensive electromagnetic design optimization
Purpose – The purpose of this paper is to propose a surrogate model-assisted optimization algorithm which effectively searches for the optimum at the earliest opportunity, avoiding the need for a large initial experimental design, which may be wasteful. Design/methodology/approach – The methodologies of two-stage and one-stage selection of points are combined for the first time. After creating a small experimental design, a one-stage Kriging algorithm is used to search for the optimum for a fixed number of iterations. If it fails to locate the optimum, the points it samples are then used in lieu of a traditional experimental design to initialize a two-stage algorithm. Findings – The proposed approach was tested on a mathematical test function. It was found that the optimum could be located, without necessarily constructing an accurate surrogate model first. The algorithm performed well on an electromagnetic design problem, outperforming both a random search and a genetic algorithm, in significantly fewer iterations. The results suggest a new interpretation of surrogate models – merely as tools for constructing a utility function to locate the optimum of an unknown function, as opposed to actual approximations of the unknown function. Research limitations/implications – The research was carried out on unconstrained problems only. The findings have implications for modern experimental designs, as the proposed algorithm can often locate the optimum without necessarily constructing an accurate surrogate model. Originality/value – The two paradigms of one-stage and two-stage selection of points in surrogate-model assisted optimization are combined for the first time. Also, it is believed that this is the first time that the methodology of one-stage optimization has been used in optimal electromagnetic design.
Optimization techniques
0332-1649
236-246
Hawe, G.
8ea51060-d74b-411c-a24f-18015fa9ce8d
Sykulski, J.K.
d6885caf-aaed-4d12-9ef3-46c4c3bbd7fb
Hawe, G.
8ea51060-d74b-411c-a24f-18015fa9ce8d
Sykulski, J.K.
d6885caf-aaed-4d12-9ef3-46c4c3bbd7fb

Hawe, G. and Sykulski, J.K. (2007) A hybrid one-then-two stage algorithm for computationally expensive electromagnetic design optimization. COMPEL: The International Journal for Computation and Mathematics in Electrical and Electronic Engineering, 26 (2), 236-246.

Record type: Article

Abstract

Purpose – The purpose of this paper is to propose a surrogate model-assisted optimization algorithm which effectively searches for the optimum at the earliest opportunity, avoiding the need for a large initial experimental design, which may be wasteful. Design/methodology/approach – The methodologies of two-stage and one-stage selection of points are combined for the first time. After creating a small experimental design, a one-stage Kriging algorithm is used to search for the optimum for a fixed number of iterations. If it fails to locate the optimum, the points it samples are then used in lieu of a traditional experimental design to initialize a two-stage algorithm. Findings – The proposed approach was tested on a mathematical test function. It was found that the optimum could be located, without necessarily constructing an accurate surrogate model first. The algorithm performed well on an electromagnetic design problem, outperforming both a random search and a genetic algorithm, in significantly fewer iterations. The results suggest a new interpretation of surrogate models – merely as tools for constructing a utility function to locate the optimum of an unknown function, as opposed to actual approximations of the unknown function. Research limitations/implications – The research was carried out on unconstrained problems only. The findings have implications for modern experimental designs, as the proposed algorithm can often locate the optimum without necessarily constructing an accurate surrogate model. Originality/value – The two paradigms of one-stage and two-stage selection of points in surrogate-model assisted optimization are combined for the first time. Also, it is believed that this is the first time that the methodology of one-stage optimization has been used in optimal electromagnetic design.

Text
COMPELvol26no2y2007page236.pdf - Other
Download (220kB)

More information

Published date: April 2007
Keywords: Optimization techniques
Organisations: EEE

Identifiers

Local EPrints ID: 263878
URI: http://eprints.soton.ac.uk/id/eprint/263878
ISSN: 0332-1649
PURE UUID: 4c2fb61c-6d2a-4042-a515-b97ef2325d23
ORCID for J.K. Sykulski: ORCID iD orcid.org/0000-0001-6392-126X

Catalogue record

Date deposited: 17 Apr 2007
Last modified: 03 Dec 2019 02:07

Export record

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×