MultiGLODS: global and local multiobjective optimization using direct search

A. L. Custódio, J. F. A. Madeira

Research output: Contribution to journalArticlepeer-review

14 Citations (Scopus)


The optimization of multimodal functions is a challenging task, in particular when derivatives are not available for use. Recently, in a directional direct search framework, a clever multistart strategy was proposed for global derivative-free optimization of single objective functions. The goal of the current work is to generalize this approach to the computation of global Pareto fronts for multiobjective multimodal derivative-free optimization problems. The proposed algorithm alternates between initializing new searches, using a multistart strategy, and exploring promising subregions, resorting to directional direct search. Components of the objective function are not aggregated and new points are accepted using the concept of Pareto dominance. The initialized searches are not all conducted until the end, merging when they start to be close to each other. The convergence of the method is analyzed under the common assumptions of directional direct search. Numerical experiments show its ability to generate approximations to the different Pareto fronts of a given problem.

Original languageEnglish
Pages (from-to)1-23
Number of pages23
JournalJournal of Global Optimization
Issue number2
Publication statusPublished - 1 Oct 2018


  • Direct search methods
  • Global optimization
  • Multiobjective optimization
  • Multistart strategies
  • Nonsmooth calculus


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