Hybridizing Nature-Inspired Algorithms to Derive Accurate Surrogate Thermal Model: Genetic and Particle Swarm Optimization
Created by W.Langdon from
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- @InProceedings{Monier-Vinard:2018:ITherm,
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author = "Eric Monier-Vinard and Olivier Daniel and
Valentin Bissuel and Brice Rogie and Minh-Nhat Nguyen and
Najib Laraqi and Ismael Aliouat",
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booktitle = "2018 17th IEEE Intersociety Conference on Thermal and
Thermomechanical Phenomena in Electronic Systems
(ITherm)",
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title = "Hybridizing Nature-Inspired Algorithms to Derive
Accurate Surrogate Thermal Model: Genetic and Particle
Swarm Optimization",
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year = "2018",
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pages = "368--378",
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abstract = "The need for miniaturization leads IC packaging
technologies toward more and more complex three
dimensional geometries, which should be carefully
addressed when dealing with thermal management. In
order to model these devices in the necessary CFD
simulations, Boundary Conditions Independent (BCI)
Compact Thermal Models (CTM) were developed in the
scope of the DELPHI consortium using Genetic Algorithm
(GA) as the optimization technique. But at each level
of packaging technology breakthrough, the ability to
achieve the right balance between accuracy,
reproducibility and speed of GA procedure shrank. The
paper describes the first results when using a Particle
Swarm Optimization (PSO) in place of genetic
programming. So instead of reproduction, the swarm
method updates the position and speed of each particle
over time. This study presents different PSO variants
found in the literature and implemented on 2 real test
cases. They confirm that the PSO tends to fall easily
in local optimums and even more as the component-model
complexity grows. Trying to combine the benefits of
both algorithms, a parallel GA-PSO hybridization is
discussed in terms of methodology, accuracy, speed and
robustness. Thus the promoted GA-PSO hybridization
succeeds supplying the best solution 8 times more
often, in half the computation time, for a multi-chip
package. The study results confirm the feasibility to
create blackbox models of various components having
low-discrepancy in comparison with the authentic
thermal behaviour.",
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keywords = "genetic algorithms, genetic programming",
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DOI = "doi:10.1109/ITHERM.2018.8419587",
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ISSN = "2577-0799",
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month = may,
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notes = "Also known as \cite{8419587}",
- }
Genetic Programming entries for
Eric Monier-Vinard
Olivier Daniel
Valentin Bissuel
Brice Rogie
Minh-Nhat Nguyen
Najib Laraqi
Ismael Aliouat
Citations