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Improving the Responsiveness of NSGA-II in Dynamic Environments Using an Adaptive Mutation Operator – A Case Study

  • Alvaro Gomes
  • C. Henggeler Antunes
  • A. Gomes Martins
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5177)

Abstract

This paper presents a comparative analysis of the results obtained with two different implementations of the NSGA-II genetic algorithm in the framework of load management activities in electric power systems. The multiobjective real-world problem deals with the identification and the selection of suitable control strategies to be applied to groups of electric loads aimed at reducing maximum power demand, maximize profits and minimize user discomfort. It is shown that the algorithm performance is improved when the NSGA-II mutation operator is adaptively changed to incorporate information about the results of the search process and transfer this “knowledge” to the population.

Keywords

Evolutionary Multiobjective Optimization Real-world applications 

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References

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Alvaro Gomes
    • 1
    • 2
  • C. Henggeler Antunes
    • 1
    • 2
  • A. Gomes Martins
    • 1
    • 2
  1. 1.Department of Electrical Engineering and ComputersUniversity of CoimbraPortugal
  2. 2.INESC CoimbraCoimbraPortugal

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