DEMO: Differential Evolution for Multiobjective Optimization

  • Tea Robič
  • Bogdan Filipič
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3410)


Differential Evolution (DE) is a simple but powerful evolutionary optimization algorithm with many successful applications. In this paper we propose Differential Evolution for Multiobjective Optimization (DEMO) – a new approach to multiobjective optimization based on DE. DEMO combines the advantages of DE with the mechanisms of Pareto-based ranking and crowding distance sorting, used by state-of-the-art evolutionary algorithms for multiobjective optimization. DEMO is implemented in three variants that achieve competitive results on five ZDT test problems.


Pareto Front Multiobjective Optimization Crossover Probability Nondominated Solution Nondominated Sorting 
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-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Tea Robič
    • 1
  • Bogdan Filipič
    • 1
  1. 1.Department of Intelligent SystemsJožef Stefan InstituteLjubljanaSlovenia

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