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DMS and MultiGLODS: Black-Box Optimization Benchmarking of Two Direct Search Methods on the bbob-biobj Test Suite

Dimo Brockhoff 1 Baptiste Plaquevent-Jourdain 2 Anne Auger 1 Nikolaus Hansen 1 
1 RANDOPT - Randomized Optimisation
CMAP - Centre de Mathématiques Appliquées - Ecole Polytechnique, Inria Saclay - Ile de France
Abstract : Direct Multisearch (DMS) and MultiGLODS are two derivative-free solvers for approximating the entire set of Pareto-optimal solutions of a multiobjective (blackbox) problem. They both follow the search/poll step approach of direct search methods, employ Pareto dominance to avoid aggregating objectives, and have theoretical limit guarantees. Although the original publications already compare the two algorithms empirically with a variety of multiobjective solvers, an analysis on their scaling behavior with dimension was missing. Here, we run the publicly available implementations on the bbob-biobj test suite of the COCO platform and by investigating their performances in more detail, observe (i) a small defect in the default initialization of DMS, (ii) for both algorithms a decrease in relative performance to other algorithms of the original studies (even matching the performance of random search for MultiGLODS in higher dimension), and (iii) consequently, an under-performance to previously untested stochastic solvers from the evolutionary computation field, especially when the dimension is higher.
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Submitted on : Monday, July 12, 2021 - 3:52:56 PM
Last modification on : Thursday, May 12, 2022 - 3:50:08 AM
Long-term archiving on: : Wednesday, October 13, 2021 - 7:22:14 PM


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Dimo Brockhoff, Baptiste Plaquevent-Jourdain, Anne Auger, Nikolaus Hansen. DMS and MultiGLODS: Black-Box Optimization Benchmarking of Two Direct Search Methods on the bbob-biobj Test Suite. GECCO '21 - Genetic and Evolutionary Computation Conference Companion, Jul 2021, Lille / Virtual, France. pp.8, ⟨10.1145/3449726.3463207⟩. ⟨hal-03284476⟩



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