Abstract
We describe initial results obtained when applying different multi-objective evolutionary algorithms (MOEAs) to direct topology optimization (DTO) scenarios that are relevant in the field of electrical machine design. Our analysis is particularly concerned with investigating if the use of discrete or real-value encodings combined with a preference for a particular population initialization strategy can have a severe impact on the performance of MOEAs applied for DTO.
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Acknowledgments
This work was supported by the K-Project “Advanced Engineering Design Automation” (AEDA) that is financed under the COMET (COMpetence centers for Excellent Technologies) funding scheme of the Austrian Research Promotion Agency.
This work was partially conducted within LCM GmbH as a part of the COMET K2 program of the Austrian government. The COMET K2 projects at LCM are kindly supported by the Austrian and Upper Austrian governments and the participating scientific partners. The authors thank all involved partners for their support.
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Zăvoianu, AC., Bramerdorfer, G., Lughofer, E., Saminger-Platz, S. (2018). Multi-objective Topology Optimization of Electrical Machine Designs Using Evolutionary Algorithms with Discrete and Real Encodings. In: Moreno-Díaz, R., Pichler, F., Quesada-Arencibia, A. (eds) Computer Aided Systems Theory – EUROCAST 2017. EUROCAST 2017. Lecture Notes in Computer Science(), vol 10671. Springer, Cham. https://doi.org/10.1007/978-3-319-74718-7_40
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DOI: https://doi.org/10.1007/978-3-319-74718-7_40
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