The University of Southampton
University of Southampton Institutional Repository

Efficient search for trade-offs by adaptive range multi-objective genetic algorithms

Efficient search for trade-offs by adaptive range multi-objective genetic algorithms
Efficient search for trade-offs by adaptive range multi-objective genetic algorithms
Trade-offs is one of important elements for engineering design problems characterized by multiple conflicting objectives that needs to be simultaneously improved. Further, in many problems such as aerodynamic design, due to computational reasons, only a limited number of evaluations can be allowed for industrial use. This paper proposes new efficient Multi-Objective Evolutionary Algorithms (MOEAs), Adaptive Range Multi Objective Genetic Algorithms (ARMOGAs), to identify trade-offs among objectives using a small number of function evaluations. The search performance of ARMOGAs is examined by using four different multi-objective analytical test problems. ARMOGAs are also compared with another MOEA. Although the number of evaluations is limited, ARMOGAs showed good performance. In addition, Sequential Quadratic Programming and Dynamic Hill Climber methods are applied to obtain trade-offs for the same problems. These gradient-based methods had some difficulties in identifying trade-offs.
1542-9423
44-64
Sasaki, Daisuke
1d400b29-02c8-42f9-8bbc-47cdc12ec5fa
Obayashi, Shigeru
f5569406-1354-4642-82c3-82bf73c6594e
Sasaki, Daisuke
1d400b29-02c8-42f9-8bbc-47cdc12ec5fa
Obayashi, Shigeru
f5569406-1354-4642-82c3-82bf73c6594e

Sasaki, Daisuke and Obayashi, Shigeru (2005) Efficient search for trade-offs by adaptive range multi-objective genetic algorithms. Journal of Aerospace Computing, Information, and Communication, 2 (1), 44-64.

Record type: Article

Abstract

Trade-offs is one of important elements for engineering design problems characterized by multiple conflicting objectives that needs to be simultaneously improved. Further, in many problems such as aerodynamic design, due to computational reasons, only a limited number of evaluations can be allowed for industrial use. This paper proposes new efficient Multi-Objective Evolutionary Algorithms (MOEAs), Adaptive Range Multi Objective Genetic Algorithms (ARMOGAs), to identify trade-offs among objectives using a small number of function evaluations. The search performance of ARMOGAs is examined by using four different multi-objective analytical test problems. ARMOGAs are also compared with another MOEA. Although the number of evaluations is limited, ARMOGAs showed good performance. In addition, Sequential Quadratic Programming and Dynamic Hill Climber methods are applied to obtain trade-offs for the same problems. These gradient-based methods had some difficulties in identifying trade-offs.

Text
Sasa_05.pdf - Accepted Manuscript
Download (2MB)

More information

Published date: 2005

Identifiers

Local EPrints ID: 23302
URI: http://eprints.soton.ac.uk/id/eprint/23302
ISSN: 1542-9423
PURE UUID: 1c1efcc3-07bd-4b81-94d1-8143f0938e60

Catalogue record

Date deposited: 14 Mar 2006
Last modified: 15 Mar 2024 06:46

Export record

Contributors

Author: Daisuke Sasaki
Author: Shigeru Obayashi

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.

×