A Multiobjective Differential Evolution Based on Decomposition for Multiobjective Optimization with Variable Linkages

  • Hui Li
  • Qingfu Zhang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4193)


Although a number of multiobjective evolutionary algorithms have been proposed over the last two decades, not much effort has been made to deal with variable linkages in multiobjective optimization. Recently, we have suggested a general framework of multiobjective evolutionary algorithms based on decomposition (MOEA/D) [1]. MOEA/D decomposes a MOP into a number of scalar optimization subproblems by a conventional decomposition method. The optimal solution to each of these problems is a Pareto optimal solution to the MOP under consideration. An appropriate decomposition could make these individual Pareto solutions evenly distribute along the Pareto optimal front. MOEA/D aims at solving these scalar optimization subproblems simultaneously. In this paper, we propose, under the framework of MOEA/D, a multiobjective differential evolution based decomposition (MODE/D) for tackling variable linkages. Our experimental results show that MODE/D outperforms several other MOEAs on several test problems with variable linkages.


Pareto Front Pareto Optimal Solution Test Instance Pareto Optimal Front Multiobjective Optimization Problem 


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© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Hui Li
    • 1
  • Qingfu Zhang
    • 1
  1. 1.Department of Computer ScienceUniversity of EssexColchesterUnited Kingdom

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