Approaches for many-objective optimization: analysis and comparison on MNK-landscapes
Résumé
This work analyses the behavior and compares the performance of MOEA/D, IBEA using the binary additive ε and the hypervolume difference indicators, and AεSεH as representative algorithms of
decomposition, indicators, and ε-dominance based approaches for manyobjective optimization. We use small MNK-landscapes to trace the dynamics of the algorithms generating high-resolution approximations of
the Pareto optimal set. Also, we use large MNK-landscapes to analyze
their scalability to larger search spaces.
Domaines
Intelligence artificielle [cs.AI]| Origine | Fichiers produits par l'(les) auteur(s) |
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