Model-Based Multi-objective Optimization: Taxonomy, Multi-Point Proposal, Toolbox and Benchmark

  • Daniel Horn
  • Tobias Wagner
  • Dirk Biermann
  • Claus Weihs
  • Bernd Bischl
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9018)

Abstract

Within the last 10 years, many model-based multi-objective optimization algorithms have been proposed. In this paper, a taxonomy of these algorithms is derived. It is shown which contributions were made to which phase of the MBMO process. A special attention is given to the proposal of a set of points for parallel evaluation within a batch. Proposals for four different MBMO algorithms are presented and compared to their sequential variants within a comprehensive benchmark. In particular for the classic ParEGO algorithm, significant improvements are obtained. The implementations of all algorithm variants are organized according to the taxonomy and are shared in the open-source R package mlrMBO.

Keywords

Expected improvement Hypervolume Kriging Performance indicator Surrogate model 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Daniel Horn
    • 1
  • Tobias Wagner
    • 2
  • Dirk Biermann
    • 2
  • Claus Weihs
    • 1
  • Bernd Bischl
    • 1
  1. 1.Chair of Computational StatisticsTechnische Universität DortmundDortmundGermany
  2. 2.Institute of Machining Technology (ISF)Technische Universität DortmundDortmundGermany

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