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Efficient Surrogate Model-Assisted Evolutionary Algorithm for Electromagnetic Design Automation with Applications
Akinsolu, Mobayode, O.
Akinsolu, Mobayode, O.
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2019-10
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Abstract
In this thesis, the surrogate model-aware evolutionary search (SMAS) framework is
extended for efficient interactive optimisation of multiple criteria electromagnetic
(EM) designs and/or devices through a novel method called two-stage interactive
efficient EM micro-actuator design optimisation (TIEMO). The first robust analytical
and behavioural study of the SMAS framework is also carried out in this thesis to serve
as a guide for the meticulous selection of multiple differential evolution (DE) mutation
strategies to make SMAS fit for use in parallel computing environments. Based on the
study of SMAS and the self-adaptive use of the selected multiple DE mutation
strategies and reinforcement learning techniques, a novel method, parallel surrogate
model-assisted evolutionary algorithm for EM design (PSAED) is proposed. PSAED
is tested extensively using mathematical benchmark problems and numerical EM
design problems. For all cases, the efficiency improvement of PSAED compared to
state-of-the-art evolutionary algorithms (EAs) is demonstrated by the several times up
to about 20 times speed improvement observed and the high quality of design
solutions. PSAED is then applied to real-world EM design problems as two purposebuilt methods for antenna design and optimisation and high-performance microelectro-mechanical systems (MEMS) design and optimisation in parallel computing
environments, parallel surrogate model-assisted hybrid DE for antenna optimisation
(PSADEA) and adaptive surrogate model-assisted differential evolution for MEMS
optimisation (ASDEMO), respectively. For all the real-world antenna and MEMS
design cases, PSAED methods obtain very satisfactory design solutions using an
affordable optimisation time and comparisons are made with available alternative
methods. Results from the comparisons show that PSAED methods obtain very
satisfactory design solutions in all runs using an affordable optimisation time in each,
whereas the alternative methods fail and/or seldom succeed to obtain feasible or
satisfactory design solutions. PSAED methods also show better robustness and
stability. In the future, PSAED methods will be embedded into commercial
CAD/CEM tools and will be further extended for use in higher-order parallel clusters.
Citation
Akinsolu, M, O. (2019). Efficient surrogate model-assisted evolutionary algorithm for electromagnetic design automation with applications (Doctoral dissertation). University of Chester, United Kingdom & Wrexham Glyndwr University, United Kingdom.
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University of Chester
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Thesis or dissertation
Language
en