Parallel Fractal-based Chaotic optimization: Application to the optimization of deep neural networks for energy management
Principal investigators
- Rachid Ellaia, Ecole Mohammadia d’Ingénieurs, Rabat – Morroco
- El-Ghazali Talbi, research team Inria BONUS / Centre Inria Lille – Nord Europe
Abstract
Many scientific and industrial disciplines are more and more concerned by big optimisation problems (BOPs). BOPs are characterised by a huge number of mixed decision variables and/or many expensive objective functions. Bridging the gap between computational intelligence, high performance computing and big optimisation is an important challenge for the next decade in solving complex problems in science and industry. The goal of this associated team project is to come up with breakthrough in nature-inspired algorithms jointly based on any-scale fractal decomposition and chaotic approaches for BOPs. Those algorithms are massively parallel and can be efficiently designed and implemented on heterogeneous exascale supercomputers including millions of CPU/GPU (Graphics Processing Units) cores. The convergence between chaos, fractals and massively parallel computing will represent a novel computing paradigm for solving complex problems. From the application and validation point of view, we target the automatic design of deep neural networks, applied to the prediction of the electrical enerygy consumption and production.
Key words: Fractal-based decomposition, Exascale optimization, Chaotic optimization, Automated design of deep neural networks, Electrical energy management.
Website: in progress
