Multi-Factorial Evolutionary Algorithm Based on M2M Decomposition

  • Jiajie Mo
  • Zhun Fan
  • Wenji Li
  • Yi Fang
  • Yugen You
  • Xinye Cai
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10593)

Abstract

This paper proposes a decomposition-based multi-objective multi-factorial evolutionary algorithm (MFEA/D-M2M). The MFEA/D-M2M adopts the M2M approach to decompose multi-objective optimization problems into multiple constrained sub-problems for enhancing the diversity of population and convergence of sub-regions. An machine learning model augmented version is also been implemented, which utilized discriminative models for pre-selecting solutions. Experimental studies on nine multi-factorial optimization (MFO) problem sets are conducted. The experimental results demonstrated that MFEA/D-M2M outperforms the vanilla MFEA on six MFO benchmark problem sets and achieved comparable results on the other three problem sets with partial intersection of global optimal.

Keywords

Multi-factorial optimization M2M decomposition Pre-selection 

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Jiajie Mo
    • 2
  • Zhun Fan
    • 1
    • 2
  • Wenji Li
    • 2
  • Yi Fang
    • 2
  • Yugen You
    • 2
  • Xinye Cai
    • 3
  1. 1.Guangdong Provincial Key Laboratory of Digital Signal and Image ProcessingShantouChina
  2. 2.Department of Electronic EngineeringShantou UniversityShantouChina
  3. 3.College of Computer Science and TechnologyNanjing University of Aeronautics and AstronauticsJiangsuChina

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