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Description
The dataset was generated using a procedure for low-cost and reliable multiobjective optimization (MOO) of microwave passive circuits. The technique capitalizes on the attributes of surrogate models, specifically artificial neural networks (ANNs), and multiresolution electromagnetic (EM) analysis. We integrate the search process into a machine learning (ML) framework, where each iteration produces multiple infill points selected from the present representation of the Pareto set. This collection is formed by optimizing the ANN metamodel using a multiobjective evolutionary algorithm (MOEA). The procedure concludes upon convergence, defined as a significant similarity between the sets of nondominated solutions acquired through consecutive iterations.
Dataset file
hexmd5(md5(part1)+md5(part2)+...)-{parts_count} where a single part of the file is 512 MB in size.Example script for calculation:
https://github.com/antespi/s3md5
File details
- License:
-
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CC BYAttribution
Details
- Year of publication:
- 2025
- Verification date:
- 2025-03-17
- Dataset language:
- English
- Fields of science:
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- automation, electronics, electrical engineering and space technologies (Engineering and Technology)
- DOI:
- DOI ID 10.34808/xbjz-3s26 open in new tab
- Funding:
- Verified by:
- Gdańsk University of Technology
Keywords
- Computer-aided design
- machine learning (ML)
- microwave engineering
- multicriterial optimization (MOO)
- neural networks
References
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