The solution to a biobjective optimization problem is composed of a collection of trade-off solution called the Pareto set. The present work studies the que...

In derivative-free and blackbox optimization, the objective function is often evaluated through the execution of a computer program seen as a blackbox. It ...

This work proposes strategies to handle three types of constraints in the context of blackbox optimization: binary constraints that simply indicate if they a...

This work introduces StoMADS, a stochastic variant of the mesh adaptive direct-search (MADS) algorithm originally developed for deterministic blackbox optim...

In an optimization problem, multiplying an inequality constraint by a positive scalar has no effect on the domain. However, such a transformation might have...

A small polygon is a polygon of unit diameter. The question of finding the largest area of small \(n-\)gons has been answered for some values of \(n\)....

We are interested in blackbox optimization for which the user is aware of monotonic behaviour of some constraints defining the problem. That is, when incr...

The present work is in a context of derivative-free optimization involving direct search algorithms guided by surrogate models of the original problem. The...

This is a two-part work. In Part I, low-cost and representative reduced-fidelity models of two versions of the HYDROTEL hydrological model are constructed, u...

Dealing with computationally-intensive calibration processes is still common in distributed hydrological modelling despite the computing power growth. Comput...

In the recent years, the development of new algorithms for multiobjective optimization has considerably grown. A large number of performance indicators has...

The parallel space decomposition of the Mesh Adaptive Direct Search algorithm (PSD-MADS proposed in 2008) is an asynchronous parallel method for constrained ...

Derivative-free optimization (DFO) is the mathematical study of the optimization algorithms that do not use derivatives. One branch of DFO focuses on model-...

The mesh adaptive direct search (MADS) algorithm is designed for blackbox optimization problems for which the functions defining the objective and the constr...

We investigate surrogate-assisted strategies for global derivative-free optimization using the mesh adaptive direct search MADS blackbox optimization algorit...

Despite the lack of theoretical and practical convergence support, the Nelder-Mead (NM) algorithm is widely used to solve unconstrained optimization proble...

The calibration of hydrological models is here formulated as a Blackbox optimization problem where the only information available to the optimization algorit...

The Runge-Kutta class of iterative methods is designed to approximate solutions of a system of ordinary differential equations (ODE). The second-order cla...

Locally weighted regression combines the advantages of polynomial regression and kernel smoothing. We present three ideas for appropriate and effective use...

The Mesh Adaptive Direct Search algorithm (MADS) is an iterative method for constrained blackbox optimization problems. One of the optional MADS features i...