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A comparison of various optimization algorithms on a multilevel problem

A comparison of various optimization algorithms on a multilevel problem
A comparison of various optimization algorithms on a multilevel problem
In many problems in science and engineering, there are often a number of computational models that can be used to simulate the problem at hand. Models of physical systems can differ according to computational cost, accuracy and precision. This paper presents the concept of multilevel optimization, where different models of the problem are used in combination. This initial study compares several strategies for combining fast evaluations of limited accuracy with a few accurate calculations. It also attempts to show how different optimizers work under these different combination strategies. A specially designed test function is used to carry out these comparisons. Of the proposed strategies and optimisers, a sequential mixing strategy applied to a genetic algorithm with clustering gives the best results. This paper highlights the need to develop specialized optimization algorithms for this kind of problem.
optimization, multilevel problems, genetic algorithms
0952-1976
639-654
El-Beltagy, M.A
820b78f8-f595-4508-bb65-9ac44db711db
Keane, A.J.
26d7fa33-5415-4910-89d8-fb3620413def
El-Beltagy, M.A
820b78f8-f595-4508-bb65-9ac44db711db
Keane, A.J.
26d7fa33-5415-4910-89d8-fb3620413def

El-Beltagy, M.A and Keane, A.J. (1999) A comparison of various optimization algorithms on a multilevel problem. Engineering Applications of Artificial Intelligence, 12 (5), 639-654. (doi:10.1016/S0952-1976(99)00033-0).

Record type: Article

Abstract

In many problems in science and engineering, there are often a number of computational models that can be used to simulate the problem at hand. Models of physical systems can differ according to computational cost, accuracy and precision. This paper presents the concept of multilevel optimization, where different models of the problem are used in combination. This initial study compares several strategies for combining fast evaluations of limited accuracy with a few accurate calculations. It also attempts to show how different optimizers work under these different combination strategies. A specially designed test function is used to carry out these comparisons. Of the proposed strategies and optimisers, a sequential mixing strategy applied to a genetic algorithm with clustering gives the best results. This paper highlights the need to develop specialized optimization algorithms for this kind of problem.

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Published date: 1999
Keywords: optimization, multilevel problems, genetic algorithms

Identifiers

Local EPrints ID: 23618
URI: http://eprints.soton.ac.uk/id/eprint/23618
ISSN: 0952-1976
PURE UUID: 55c4ae3f-b766-411f-a26c-0d58a09ffbbb

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Date deposited: 01 Feb 2007
Last modified: 30 Jul 2019 17:48

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