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Solving Rotated Multi-objective Optimization Problems Using Differential Evolution

  • Antony W. Iorio
  • Xiaodong Li
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3339)

Abstract

This paper demonstrates that the self-adaptive technique of Differential Evolution (DE) can be simply used for solving a multi-objective optimization problem where parameters are interdependent. The real-coded crossover and mutation rates within the NSGA-II have been replaced with a simple Differential Evolution scheme, and results are reported on a rotated problem which has presented difficulties using existing Multi-objective Genetic Algorithms. The Differential Evolution variant of the NSGA-II has demonstrated rotational invariance and superior performance over the NSGA-II on this problem.

Keywords

Multiobjective Genetic Algorithm Traditional Genetic Algorithm Mass Rapid Transit Multiobjective Optimization Algorithm Automatic Train Operation 
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 2004

Authors and Affiliations

  • Antony W. Iorio
    • 1
  • Xiaodong Li
    • 1
  1. 1.School of Computer Science and Information TechnologyRoyal Melbourne Institute of Technology UniversityMelbourneAustralia

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