Search Dynamics on Multimodal Multiobjective Problems

Publikation: Beitrag in FachzeitschriftForschungsartikelBeigetragenBegutachtung

Beitragende

  • Pascal Kerschke - , Westfälische Wilhelms-Universität Münster (Autor:in)
  • Hao Wang - (Autor:in)
  • Mike Preuss - (Autor:in)
  • Christian Grimme - (Autor:in)
  • André H. Deutz - (Autor:in)
  • Heike Trautmann - (Autor:in)
  • Michael T. M. Emmerich - (Autor:in)

Abstract

We continue recent work on the definition of multimodality in multiobjective optimization (MO) and the introduction of a test bed for multimodal MO problems. This goes beyond well-known diversity maintenance approaches but instead focuses on the landscape topology induced by the objective functions. More general multimodal MO problems are considered by allowing ellipsoid contours for single-objective subproblems. An experimental analysis compares two MO algorithms, one that explicitly relies on hypervolume gradient approximation, and one that is based on local search, both on a selection of generated example problems. We do not focus on performance but on the interaction induced by the problems and algorithms, which can be described by means of specific characteristics explicitly designed for the multimodal MO setting. Furthermore, we widen the scope of our analysis by additionally applying visualization techniques in the decision space. This strengthens and extends the foundations for Exploratory Landscape Analysis (ELA) in MO.

Details

OriginalspracheEnglisch
Seiten (von - bis)577–609
FachzeitschriftEvolutionary Computation
Jahrgang27
Ausgabenummer4
PublikationsstatusVeröffentlicht - Dez. 2019
Peer-Review-StatusJa
Extern publiziertJa

Externe IDs

Scopus 85075950939

Schlagworte

Bibliotheksschlagworte