A novel multiple surrogate multi-objective decision making optimization algorithm and its application in induction heating
Abstract
We improved iTDEA, an existing preference-based multi-objective evolutionary algorithm by introducing a multiple surrogates approach. It means that, for each potential offspring, a surrogate-assisted evolutionary search is conducted in its neighbourhood using the best local surrogate among Kriging, Artificial Neural Networks (ANN) and Radial Basis Function (RBF). This makes the algorithm suitable for time-consuming objective function evaluations, as is often the case in induction heating numerical simulations. We called MSAiTDEA the new algorithm.
Details
- Organisationseinheit(en)
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Institut für Elektroprozesstechnik
- Typ
- Sonstige Publikation
- Publikationsdatum
- 05.2019
- Publikationsstatus
- Veröffentlicht
- Elektronische Version(en)
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https://doi.org/10.13140/RG.2.2.29105.22889 (Zugang:
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