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An Informative Differential Evolution with Self Adaptive Re-clustering Technique

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

Abstract

We propose an informative Differential Evolution (DE) algorithm where the information gained by the individuals of a cluster will be exchanged after a certain number of iterations called refreshing gap. The DE is empowered with a clustering technique to improve its efficiency over multimodal landscapes. During evolution, self-adaptive behaviour helps in re-clustering. With the better explorative power of the proposed algorithm we have used a new local search technique for fine tuning near a suspected optimal position. The performance of the proposed algorithm is evaluated over 25 benchmark functions and compared with existing algorithms.

Keywords

Differential Evolution optimization cluster self-adaptive reclustering 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Dipankar Maity
    • 1
  • Udit Halder
    • 1
  • Preetam Dasgupta
    • 1
  1. 1.Dept. of Electronics and Tele-communication EngineeringJadavpur UniversityKolkataIndia

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