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Compact Bacterial Foraging Optimization

  • Giovanni Iacca
  • Ferrante Neri
  • Ernesto Mininno
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7269)

Abstract

Compact algorithms are Estimation of Distribution Algorithms which mimic the behavior of population-based algorithms by means of a probabilistic representation of the population of candidate solutions. Compared to an actual population, a probabilistic model requires a much smaller memory, which allows algorithms with limited memory footprint. This feature is extremely important in some engineering applications, e.g. robotics and real-time control systems. This paper proposes a compact implementation of Bacterial Foraging Optimization (cBFO). cBFO employs the same chemotaxis scheme of population-based BFO, but without storing a swarm of bacteria. Numerical results, carried out on a broad set of test problems with different dimensionalities, show that cBFO, despite its minimal hardware requirements, is competitive with other memory saving algorithms and clearly outperforms its population-based counterpart.

Keywords

Bacterial Forage Optimization Compact Algorithm Bacterial Forage Bacterial Forage Optimization Algorithm Chemotactic Step 
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 2012

Authors and Affiliations

  • Giovanni Iacca
    • 1
  • Ferrante Neri
    • 1
  • Ernesto Mininno
    • 1
  1. 1.Department of Mathematical Information TechnologyUniversity of JyväskyläFinland

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