Michael Kokkolaras

Constrained stochastic blackbox optimization using a progressive barrier and probabilistic estimates

  • Kwassi Joseph Dzahini
  • Michael Kokkolaras
  • Sébastien Le Digabel
    • Categories Constrained Nonlinear Optimization, Nonlinear Optimization, Stochastic Approaches Tags , , ,

    This work introduces the StoMADS-PB algorithm for constrained stochastic blackbox optimization, which is an extension of the mesh adaptive direct-search (MADS) method originally developed for deterministic blackbox optimization under general constraints. The values of the objective and constraint functions are provided by a noisy blackbox, i.e., they can only be computed with random noise whose … Read more

    Two decades of blackbox optimization applications

  • Stéphane Alarie
  • Charles Audet
  • Aïmen E. Gheribi
  • Michael Kokkolaras
  • Sébastien Le Digabel
    • Categories Applications - Science and Engineering, Chemical Engineering, Mechanical Engineering Tags , , ,

    This work reviews blackbox optimization applications over the last twenty years, addressed using direct search optimization methods. Emphasis is placed on the Mesh Adaptive Direct Search (MADS) derivative-free optimization algorithm. The core of the document describes applications in three specific fields: Energy, materials science, and computational engineering design. Other applications in science and engineering as … Read more

    Stochastic mesh adaptive direct search for blackbox optimization using probabilistic estimates

  • Charles Audet
  • Kwassi Joseph Dzahini
  • Michael Kokkolaras
  • Sébastien Le Digabel
    • Categories Nonlinear Optimization, Other Topics, Unconstrained Optimization Tags , , , ,

    We present a stochastic extension of the mesh adaptive direct search (MADS) algorithm originally developed for deterministic blackbox optimization. The algorithm, called StoMADS, considers the unconstrained optimization of an objective function f whose values can be computed only through a blackbox corrupted by some random noise following an unknown distribution. The proposed method is based … Read more

    Locally weighted regression models for surrogate-assisted design optimization

  • Charles Audet
  • Michael Kokkolaras
  • Sébastien Le Digabel
  • Bastien Talgorn
    • Categories Mechanical Engineering, Nonlinear Optimization, Nonsmooth Optimization Tags , , , ,

    Locally weighted regression combines the advantages of polynomial regression and kernel smoothing. We present three ideas for appropriate and effective use of LOcally WEighted Scatterplot Smoothing (LOWESS) models for surrogate optimization. First, a method is proposed to reduce the computational cost of LOWESS models. Second, a local scaling coefficient is introduced to adapt LOWESS models … Read more

    Order-based error for managing ensembles of surrogates in derivative-free optimization

  • Charles Audet
  • Michael Kokkolaras
  • Sébastien Le Digabel
  • Bastien Talgorn
    • Categories Constrained Nonlinear Optimization, Nonlinear Optimization, Nonsmooth Optimization Tags , , ,

    We investigate surrogate-assisted strategies for derivative-free optimization using the mesh adaptive direct search (MADS) blackbox optimization algorithm. In particular, we build an ensemble of surrogate models to be used within the search step of MADS, and examine different methods for selecting the best model for a given problem at hand. To do so, we introduce … Read more

    Problem Formulations for Simulation-based Design Optimization using Statistical Surrogates and Direct Search

  • Michael Kokkolaras
  • Sébastien Le Digabel
  • Bastien Talgorn
    • Categories Applications - Science and Engineering, Constrained Nonlinear Optimization, Nonsmooth Optimization

    Typical challenges of simulation-based design optimization include unavailable gradients and unreliable approximations thereof, expensive function evaluations, numerical noise, multiple local optima and the failure of the analysis to return a value to the optimizer. One possible remedy to alleviate these issues is to use surrogate models in lieu of the computational models or simulations and … Read more

    Mixed variable optimization of the number and composition of heat intercepts in a thermal insulation system

  • Charles Audet
  • John E. Dennis, Jr.
  • Michael Kokkolaras
    • Categories (Mixed) Integer Nonlinear Programming, Bound-constrained Optimization, Mechanical Engineering Tags , , , , ,

    In the literature, thermal insulation systems with a fixed number of heat intercepts have been optimized with respect to intercept locations and temperatures. The number of intercepts and the types of insulators that surround them were chosen by parametric studies. This was because the optimization methods used could not treat such categorical variables. Discrete optimization … Read more