Hybridizing Nature-Inspired Algorithms to Derive Accurate Surrogate Thermal Model: Genetic and Particle Swarm Optimization

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{Monier-Vinard:2018:ITherm,

• author = "Eric Monier-Vinard and Olivier Daniel and Valentin Bissuel and Brice Rogie and Minh-Nhat Nguyen and Najib Laraqi and Ismael Aliouat",
• booktitle = "2018 17th IEEE Intersociety Conference on Thermal and Thermomechanical Phenomena in Electronic Systems (ITherm)",
• title = "Hybridizing Nature-Inspired Algorithms to Derive Accurate Surrogate Thermal Model: Genetic and Particle Swarm Optimization",
• year = "2018",
• pages = "368--378",
• 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.",
• keywords = "genetic algorithms, genetic programming",
• DOI = "doi:10.1109/ITHERM.2018.8419587",
• ISSN = "2577-0799",
• month = may,
• 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