Theoretical Aspects of Evolutionary Multiobjective Optimization---A Review - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Reports (Research Report) Year : 2009

Theoretical Aspects of Evolutionary Multiobjective Optimization---A Review

Abstract

Optimization problems in practice often involve the simultaneous optimization of 2 or more conflicting objectives. Evolutionary multiobjective optimization (EMO) techniques are well suited for tackling those multiobjective optimization problems because they are able to generate a set of solutions that represent the inherent trade-offs between the objectives. In the beginning, multiobjective evolutionary algorithms have been seen as single-objective algorithms where only the selection scheme needed to be tailored towards multiobjective optimization. In the meantime, EMO has become an independent research field with its specific research questions---and its own theoretical foundations. Several important theoretical studies on EMO have been conducted in recent years which opened up a better understanding of the underlying principles and resulted in the proposition of better algorithms in practice. Besides a brief introduction about the basic principles of EMO, the main goal of this report is to give a general overview of theoretical studies published in the field of EMO and to present some of the theoretical results in more detail. Due to space limitations, we only focus on three main aspects of previous and current research here: (i) performance assessment with quality indicators, (ii) hypervolume-based search, and (iii) rigorous runtime analyses and convergence properties of multiobjective evolutionary algorithms.
Fichier principal
Vignette du fichier
RR-7030.pdf (524.66 Ko) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

inria-00414333 , version 1 (09-09-2009)

Identifiers

  • HAL Id : inria-00414333 , version 1

Cite

Dimo Brockhoff. Theoretical Aspects of Evolutionary Multiobjective Optimization---A Review. [Research Report] RR-7030, INRIA. 2009. ⟨inria-00414333⟩
257 View
365 Download

Share

Gmail Facebook X LinkedIn More