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Multi-objective Topology Optimization of Electrical Machine Designs Using Evolutionary Algorithms with Discrete and Real Encodings

  • Alexandru-Ciprian Zăvoianu
  • Gerd Bramerdorfer
  • Edwin Lughofer
  • Susanne Saminger-Platz
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10671)

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.

Keywords

Evolutionary algorithms Multi-objective optimization Discrete encoding Real encoding Topology optimization Electrical machine design 

Notes

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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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Alexandru-Ciprian Zăvoianu
    • 1
  • Gerd Bramerdorfer
    • 2
    • 3
  • Edwin Lughofer
    • 1
  • Susanne Saminger-Platz
    • 1
  1. 1.Department of Knowledge-Based Mathematical SystemsJohannes Kepler University LinzLinzAustria
  2. 2.Institute for Electrical Drives and Power ElectronicsJohannes Kepler University LinzLinzAustria
  3. 3.Linz Center of Mechatronics, LCMLinzAustria

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