A Survey of Decomposition Methods for Multi-objective Optimization

  • Alejandro Santiago
  • Héctor Joaquín Fraire Huacuja
  • Bernabé Dorronsoro
  • Johnatan E. Pecero
  • Claudia Gómez Santillan
  • Juan Javier González  Barbosa
  • José Carlos Soto Monterrubio
Part of the Studies in Computational Intelligence book series (SCI, volume 547)


The multi-objective optimization methods are traditionally based on Pareto dominance or relaxed forms of dominance in order to achieve a representation of the Pareto front. However, the performance of traditional optimization methods decreases for those problems with more than three objectives to optimize. The decomposition of a multi-objective problem is an approach that transforms a multi-objective problem into many single-objective optimization problems, avoiding the need of any dominance form. This chapter provides a short review of the general framework, current research trends and future research topics on decomposition methods.


Weight Vector Differential Evolution Pareto Front Vector Generator Pareto Optimal 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.



B. Dorronsoro acknowledges the support by the National Research Fund, Luxembourg (AFR contract no. 4017742). A. Santiago would like to thank CONACyT Mexico, for the support no. 360199.


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Alejandro Santiago
    • 1
  • Héctor Joaquín Fraire Huacuja
    • 1
  • Bernabé Dorronsoro
    • 2
  • Johnatan E. Pecero
    • 3
  • Claudia Gómez Santillan
    • 1
  • Juan Javier González  Barbosa
    • 1
  • José Carlos Soto Monterrubio
    • 1
  1. 1.Instituto Tecnológico de Ciudad MaderoCiudad MaderoMexico
  2. 2.University of LilleLilleFrance
  3. 3.University of LuxembourgLuxembourgLuxembourg

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