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Numerical and experimental generated data during project https://doi.org/10.1109/ACCESS.2024.3407978
Description
The dataset was generated using a procedure for a fast globalized optimization of passive microwave components. It combines a machine learning procedure, specifically, an iterative construction and refinement of fast surrogates (with infill criterion being a minimization of the predictor-yielded objective improvement) with a response feature technology, where the metamodel targets suitably appointed characteristic points of the circuit outputs. Identification of the infill points is executed using a particle swarm optimization algorithm. Numerical experiments carried out using two microstrip circuits demonstrate the capability for a global search of the proposed algorithm, and its superior performance over direct nature-inspired-based optimization and surrogate-assisted search at the level of complete circuit characteristics.
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
- DOI:
- DOI ID 10.34808/8hka-f425 open in new tab
- Funding:
- Verified by:
- Gdańsk University of Technology
Keywords
- EM-driven design
- global optimization
- microwave engineering
- nature-inspired algorithms
- response features
References
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