We study evolutionary methods for the optimization of optical systems

The development of optical systems usually involves an optimization stage in which a set of parameters leading to maximum efficiency is sought. The complexity of these optimization problems increases exponentially with the number of parameters to be determined. We have developed evolutionary methods (genetic algorithms, PSO) to solve these optimization problems more efficiently.

Recherches du groupe de recherche de Alexandre Mayer

The general idea behind a genetic algorithm is to work with a population of individuals representing possible solutions to the problem under consideration. The best individuals are selected. Their parameters are subjected to crossover and mutation to determine new individuals for the next generation. This strategy is repeated from generation to generation until the population converges towards the global optimum of the problem.

A multi-objective genetic algorithm has been developed for the optimization of systems for which several objectives must be achieved. The genetic algorithm can be easily coupled with any external software used for modeling the system under consideration. It will run massively in parallel on CECI's supercomputers and on Tier-1, using the computing resources of the PTCI technology platform.

New algorithms are currently being developed. Among them, particle swarm optimization (PSO) is inspired by the dynamics of bird flocks. It is also used to determine the global optimum of problems.

Applications

Optimization of light-emitting diodes (LEDs), thermal solar panels, photovoltaic panels, metamaterial superabsorbers.

Representative publications

Projects

  • PhotoNVoltaics, Nanophotonics for ultra-thin crystalline silicon photovoltaics, EU Project H2020 FP7-Energy, 2012-2015
  • Different collaborations on the optimization of optical systems are currently in progress.

Promoter(PI): Alexandre MAYER

Alexandre Mayer, a member of the LPS, is also affiliated with the NISM (Pôle de recherche HPC-MM).

Thesis topics (Pr. Alexandre Mayer)

  • Evolving optimization methods for numerical physics problems
  • Transposing techniques from physics to Machine Learning
  • Applications of artificial intelligence techniques in physics