Comparing Decomposition-Based and Automatically Component-Wise Designed Multi-Objective Evolutionary Algorithms

  • Leonardo C. T. Bezerra
  • Manuel López-Ibáñez
  • Thomas Stützle
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9018)

Abstract

A main focus of current research on evolutionary multi-objective optimization (EMO) is the study of the effectiveness of EMO algorithms for problems with many objectives. Among the several techniques that have led to the development of more effective algorithms, decomposition and component-wise design have presented particularly good results. But how do they compare? In this work, we conduct a systematic analysis that compares algorithms produced using the MOEA/D decomposition-based framework and the AutoMOEA component-wise design framework. In particular, we identify a version of MOEA/D that outperforms the best known MOEA/D algorithm for several scenarios and confirms the effectiveness of decomposition on problems with three objectives. However, when we consider problems with five objectives, we show that MOEA/D is unable to outperform SMS-EMOA, being often outperformed by it. Conversely, automatically designed AutoMOEAs display competitive performance on three-objective problems, and the best and most robust performance among all algorithms considered for problems with five objectives.

Keywords

Multi-objective optimization Evolutionary algorithms Decomposition Component-wise design Automatic configuration 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Leonardo C. T. Bezerra
    • 1
  • Manuel López-Ibáñez
    • 1
  • Thomas Stützle
    • 1
  1. 1.IRIDIAUniversité libre de Bruxelles (ULB)BrusselsBelgium

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