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Pareto-, Aggregation-, and Indicator-Based Methods in Many-Objective Optimization

  • Tobias Wagner
  • Nicola Beume
  • Boris Naujoks
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4403)

Abstract

Research within the area of Evolutionary Multi-objective Optimization (EMO) focused on two- and three-dimensional objective functions, so far. Most algorithms have been developed for and tested on this limited application area. To broaden the insight in the behavior of EMO algorithms (EMOA) in higher dimensional objective spaces, a comprehensive benchmarking is presented, featuring several state-of-the-art EMOA, as well as an aggregative approach and a restart strategy on established scalable test problems with three to six objectives. It is demonstrated why the performance of well-established EMOA (NSGA-II, SPEA2) rapidly degradates with increasing dimension. Newer EMOA like ε-MOEA, MSOPS, IBEA and SMS-EMOA cope very well with high-dimensional objective spaces. Their specific advantages and drawbacks are illustrated, thus giving valuable hints for practitioners which EMOA to choose depending on the optimization scenario. Additionally, a new method for the generation of weight vectors usable in aggregation methods is presented.

Keywords

Weight Vector Pareto Front Objective Space Target Vector Extremal Solution 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Tobias Wagner
    • 1
  • Nicola Beume
    • 2
  • Boris Naujoks
    • 2
  1. 1.Institut für Spanende Fertigung (ISF) 
  2. 2.Chair of Algorithm Engineering, University of Dortmund, 44221 DortmundGermany

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