Loading...
 

Job Ad Display

Return to the list

PhD: PhD position in multiobjective optimization at Inria in collaboration with industry
Where: Paris, France France
What: PhD position: Stochastic and surrogate-assisted multi-objective optimization

in collaboration with INRIA (Dimo Brockhoff, Nikolaus Hansen), CNRS (Rodolphe Le Riche) and STORENGY (Frédéric Huguet)


Context
Optimization problems with multiple objective functions are pervasive in practice. They are also key problems at Storengy, mainly for gas storage reservoir history matching studies. The objective functions are typically: the bottom well pressure, the gas-water interface position in wells and the water production. The reservoir simulator can be seen as an expensive high-dimensional simulator which internal parameters need to be adapted in order to match simulator output and measured history data. Due to the number of conflicting objective functions, the solution of the problem is not unique and we need to find a set of solutions to explain the uncertainty on parameters and measurements.

Recently, it turned out that a reformulation of 2-objective optimization problems as a single-objective problem, optimizing the quality of the entire solution set evaluated so far, can be solved efficiently by single-objective solvers such as Quasi-Newton BFGS or the well-known Covariance Matrix Adaptation Evolution Strategy (CMA-ES). The usage of the latter resulted in the so-called COMO-CMA-ES. Theoretical results in combination with numerical experiments show that for two objective functions and convex quadratic objective functions, convergence towards the theoretical optimum should occur. However, the fact that objective functions can come from expensive numerical simulations reduces the amount of available function evaluations to a few hundred or thousand evaluations and might not allow a good convergence. In this budget range surrogate based algorithms such as classical trust-region methods or Bayesian optimization approaches are expected to perform well. At the same time, the
combination of CMA-ES with Bayesian approaches can help to investigate and manage the uncertainties present in the problems at Storengy. As importantly, the current COMO-CMA-ES does not allow to exploit highly parallel computations, another important aspect when dealing with expensive objective functions.

Goal
The goal of this thesis project is to extend the available COMO-CMA-ES algorithm towards expensive optimization by using surrogate models in order to save true function evaluations and manage high dimensional problems. A first approach for single-objective optimization problems, which is a portfolio algorithm combining a model-based version of CMA-ES with a classical solver such as SLSQP, has significantly reduced the amount of necessary function evaluations to reach a given target. In this thesis, we would like to follow a similar line for the multiobjective COMO-CMA-ES algorithm. The COMO-CMA-ES builds a single-objective (dynamic) objective function to be optimized to find multiple near-optimal solutions. In this context, the approach for using surrogate models to solve multiobjective problems will be to employ a single surrogate model for the single-objective function, optimized within the COMO-CMA-ES instead of previous approaches that build a surrogate model for each objective function separately. The advantage of a single surrogate is to capture in a unique model the compromise that underlies multi-objective decision problems. It furthermore reduces the internal complexity of the algorithm if only a single surrogate has to be learned. The option of a single surrogate will be compared to the more classical approach with a surrogate per optimization criterion.

Collaboration team

The thesis will be coadvised between the INRIA RandOpt team (Dimo Brockhoff and Nikolaus Hansen), CNRS LIMOS at Mines Saint-Etienne (Rodolphe Le Riche) and the Storengy company (Frédéric Huguet). The PhD project is part of a larger consortium project called CIROQUO ("Consortium Industrie Recherche pour l’Optimisation et la QUantification d’incertitude pour les données Onéreuses") with 6 industrial and 6 academic partners carrying out research in statistical modeling and optimization. This guarantees numerous scientific interactions during the course of the PhD.


Candidate Profile

Probability/statistics/operational research student, with a master degree or equivalent
Good mastery of the foundations of statistical learning and optimization
Ease in scientific programming, with a good knowledge of R, Python
Expected starting date is in autumn 2021 with a possibility of a Master's thesis before.

Who: Dimo Brockhoff, http://www.cmap.polytechnique.fr/~dimo.brockhoff/
When: Until 2021-09-29 18:00
Presented at next GECCO?: yes



Return to the list