A Parallel Surrogate Model Assisted Evolutionary Algorithm for Electromagnetic Design Optimization
Akinsolu, Mobayode O., Liu, Bo, Grout, Vic, Lazaridis, P.I, Mognaschi, Maria Evelina and Di Barba, Paolo (2019) A Parallel Surrogate Model Assisted Evolutionary Algorithm for Electromagnetic Design Optimization. IEEE Transactions on Emerging Topics in Computational Intelligence, 3 (2). pp. 93-105. ISSN 2471-285X
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GURO_397_FINAL VERSION Parallel surrogate_optimisation.pdf - Accepted Version Download (4MB) | Preview |
Abstract
Optimization efficiency is a major challenge for electromagnetic (EM) device, circuit, and machine design. Although both surrogate model-assisted evolutionary algorithms (SAEAs) and parallel computing are playing important roles in addressing this challenge, there is little research that investigates their integration to benefit from both techniques. In this paper, a new method, called parallel SAEA for electromagnetic design (PSAED), is proposed. A state-of-the-art SAEA framework, surrogate model-aware evolutionary search, is used as the foundation of PSAED. Considering the landscape characteristics of EM design problems, three differential evolution mutation operators are selected and organized in a particular way. A new SAEA framework is then proposed to make use of the selected mutation operators in a parallel computing environment. PSAED is tested by a micromirror and a dielectric resonator antenna as well as four mathematical benchmark problems of various complexity. Comparisons with state-of-the-art methods verify the advantages of PSAED in terms of efficiency and optimization capacity.
| Item Type: | Article |
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| Keywords: | Electromagnetic design optimization, electromagnetic design, surrogate-model-assisted evolutionary algorithm (SAEA), computationally expensive optimization, Gaussian process, differential evolution. |
| Divisions: | Applied Science, Computing and Engineering |
| Depositing User: | Hayley Dennis |
| Date Deposited: | 24 Apr 2019 13:11 |
| Last Modified: | 06 Nov 2019 14:52 |
| URI: | https://wrexham.repository.guildhe.ac.uk/id/eprint/17404 |
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