Optimization Online


DMulti-MADS: Mesh adaptive direct multisearch for blackbox multiobjective optimization

Jean Bigeon(jean.bigeon***at***grenoble-inp.fr)
Sébastien Le Digabel(sebastien.le.digabel***at***gerad.ca)
Ludovic Salomon(ludovic.salomon***at***gerad.ca)

Abstract: The context of this research is multiobjective optimization where conflicting objectives are present. In this work, these objectives are only available as the outputs of a blackbox for which no derivative information is available. This work proposes a new extension of the mesh adaptive direct search (MADS) algorithm to constrained multiobjective derivative-free optimization. This method does not aggregate objectives and keeps a list of non dominated points which converges to a (local) Pareto set as long as the algorithm unfolds. As in the single-objective optimization MADS algorithm, this method is built around a search step and a poll step. Under classical direct search assumptions, it is proved that the so-called DMulti-MADS algorithm generates multiple subsequences of iterates which converge to a set of local Pareto stationary points. Finally, computational experiments suggest that this approach is competitive compared to the state-of-the-art algorithms for multiobjective blackbox optimization.

Keywords: Multiobjective optimization, derivative-free optimization, blackbox optimization, mesh adaptive direct search, Clarke analysis.

Category 1: Nonlinear Optimization

Category 2: Nonlinear Optimization (Other )

Category 3: Other Topics (Multi-Criteria Optimization )


Download: [PDF]

Entry Submitted: 04/16/2020
Entry Accepted: 04/16/2020
Entry Last Modified: 04/16/2020

Modify/Update this entry

  Visitors Authors More about us Links
  Subscribe, Unsubscribe
Digest Archive
Search, Browse the Repository


Coordinator's Board
Classification Scheme
Give us feedback
Optimization Journals, Sites, Societies
Mathematical Optimization Society