A parallel surrogate model assisted evolutionary algorithm for electromagnetic design optimization

Akinsolu, M. O., Liu, B. , Grout, V., Lazaridis, P. I. and Mognaschi, M. E. (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. (doi: 10.1109/TETCI.2018.2864747)

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: Articles
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Liu, Professor Bo
• Authors: Akinsolu, M. O., Liu, B., Grout, V., Lazaridis, P. I., and Mognaschi, M. E.
• College/School: College of Science and Engineering > School of Engineering
• Journal Name: IEEE Transactions on Emerging Topics in Computational Intelligence
• Publisher: IEEE
• ISSN: 2471-285X
• Published Online: 25 March 2019
• Copyright Holders: Copyright © 2019 IEEE
• First Published: First published in IEEE Transactions on Emerging Topics in Computational Intelligence 3(2): 93-105
• Publisher Policy: Reproduced in accordance with the publisher copyright policy