On the hardness of offline multiobjective optimization - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Journal Articles Evolutionary Computation Year : 2007

On the hardness of offline multiobjective optimization

Olivier Teytaud


It is empirically established that multiobjective evolutionary algorithms do not scale well with the number of conflicting objectives. We here show that the convergence rate of all comparison-based multiobjective algorithms, for the Hausdorff distance, is not much better than the convergence rate of the random search, unless the number of objectives is very moderate, in a framework in which the stronger assumptions are (i) that the objectives are conflicting (ii) that lower bounding the computational cost by the number of comparisons is a good model. Our conclusions are (i) the relevance of the number of conflicting objectives (ii) the relevance of criteria based on comparisons with random-search for multi-objective optimization (iii) the very-hardness of more than 3- objectives optimization (iv) some hints about cross-over operators.
Fichier principal
Vignette du fichier
pareto2.pdf (228.74 Ko) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

inria-00173239 , version 1 (19-09-2007)


  • HAL Id : inria-00173239 , version 1


Olivier Teytaud. On the hardness of offline multiobjective optimization. Evolutionary Computation, 2007. ⟨inria-00173239⟩
194 View
431 Download


Gmail Facebook Twitter LinkedIn More