A Parallel Surrogate Model Assisted Evolutionary Algorithm for Electromagnetic Design Optimization

Mobayode Akinsolu, Bo Liu, Vic Grout, Pavlos Lazaridis, Maria Evelina Mognaschi, Paolo Di Barba

Research output: Contribution to journalArticlepeer-review

70 Citations (Scopus)


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.
Original languageEnglish
Pages (from-to)93-105
Number of pages13
JournalIEEE Transactions on Emerging Topics in Computational Intelligence
Issue number2
Early online date25 Mar 2019
Publication statusPublished - Apr 2019


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