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Robust multi-objective optimisation in electromagnetic design

Robust multi-objective optimisation in electromagnetic design
Robust multi-objective optimisation in electromagnetic design
In electromagnetic design, optimisation often involves evaluating the finite element method (FEM) – repetitive evaluation of the objective function may require hours or days of computation, making the use of standard direct search methods (e.g. genetic algorithm and particle swarm) impractical. Surrogate modelling techniques are helpful tools in these scenarios. Indeed, their applications can be found in many aspects of engineering design in which a computationally expensive model is involved.

Kriging, one of the most widely used surrogate modelling techniques, has become an increasingly active research subject in recent decades. This thesis focuses on four interesting research topics in surrogate-based optimisation: infill sampling efficiency, robust optimisation, and the memory problem encountered in large datasets and multi-objective optimisation. This thesis briefly provides relevant background information and introduces a number of independent novel approaches for each topic, with the aim of increasing efficiency of optimisation process and ability to handle larger datasets.
University of Southampton
Li, Yinjiang
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Li, Yinjiang
035e8693-c6e6-4a91-8e10-9232fd0c3112
Sykulski, Jan
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Rotaru, Mihai
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Li, Yinjiang (2017) Robust multi-objective optimisation in electromagnetic design. University of Southampton, Doctoral Thesis, 157pp.

Record type: Thesis (Doctoral)

Abstract

In electromagnetic design, optimisation often involves evaluating the finite element method (FEM) – repetitive evaluation of the objective function may require hours or days of computation, making the use of standard direct search methods (e.g. genetic algorithm and particle swarm) impractical. Surrogate modelling techniques are helpful tools in these scenarios. Indeed, their applications can be found in many aspects of engineering design in which a computationally expensive model is involved.

Kriging, one of the most widely used surrogate modelling techniques, has become an increasingly active research subject in recent decades. This thesis focuses on four interesting research topics in surrogate-based optimisation: infill sampling efficiency, robust optimisation, and the memory problem encountered in large datasets and multi-objective optimisation. This thesis briefly provides relevant background information and introduces a number of independent novel approaches for each topic, with the aim of increasing efficiency of optimisation process and ability to handle larger datasets.

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Published date: August 2017

Identifiers

Local EPrints ID: 415498
URI: http://eprints.soton.ac.uk/id/eprint/415498
PURE UUID: 1940a896-13cf-4ced-a6e5-21741f4071d7
ORCID for Jan Sykulski: ORCID iD orcid.org/0000-0001-6392-126X

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Date deposited: 13 Nov 2017 17:30
Last modified: 30 Jan 2020 01:24

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