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Self-adaptive Cluster-Based Differential Evolution with an External Archive for Dynamic Optimization Problems

  • Udit Halder
  • Dipankar Maity
  • Preetam Dasgupta
  • Swagatam Das
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7076)

Abstract

In this paper we propose a self adaptive cluster based Differential Evolution (DE) algorithm to solve the Dynamic Optimization Problems (DOPs). We have enhanced the classical DE to perform better in dynamic environments by a powerful clustering technique. During evolution, the information gained by the particles of different clusters is exchanged by a self adaptive strategy. The information exchange is done by re-clustering, and the cluster number is updated adaptively throughout the optimization process. To detect the environment change a test particle is used. Moreover, to adapt the population in new environment an External Archive is also used. The performance of SACDEEA is evaluated on GDBG benchmark problems and compared with other existing algorithms.

Keywords

Differential Evolution Dynamic Optimization Problem (DOP) Self adaptive clustering 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Udit Halder
    • 1
  • Dipankar Maity
    • 1
  • Preetam Dasgupta
    • 1
  • Swagatam Das
    • 2
  1. 1.Dept. of Electronics & Tele-comm. EngineeringJadavpur UniversityKolkataIndia
  2. 2.Electronics and Comm. Sciences UnitIndian Statistical InstituteKolkataIndia

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