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Hybrid Improved Bacterial Swarm (HIBS) Optimization Algorithm

  • K. Shanmugasundaram
  • A. S. A. MohamedEmail author
  • N. I. R. Ruhaiyem
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10645)

Abstract

This paper proposed a hybrid improved bacterial swarm optimization (HIBS) algorithm by combining bacterial foraging optimization algorithm (BFO) with particle swarm optimization (PSO) to improve the performance of the classical BFO algorithm. Adaptive step size is introduced instead of fixed step size by random walk of the Fire Fly Algorithm (FFA) in the tumble move of the bacterium at the chemo-taxis stage of BFO. So that, the slow convergence of the BFO algorithm is mitigated. PSO algorithm is acted as mutation operator to attain the global best. So, the trapping out in the local optima by PSO is being avoided. BFO algorithm is used to attain the local best optimality. The new algorithm is tested on a set of benchmark functions. The proposed hybrid algorithm is compared with the original BFO and PSO algorithm. It has been proved that the proposed algorithm shows the significance than the classical BFO and PSO algorithms.

Keywords

Adaptive step size Bacterial Foraging Optimization Particle Swarm Optimization Fire Fly Algorithm 

Notes

Acknowledgements

The author wish to thank Universiti Sains Malaysia for the support it has extended in the completion of the present research through Short Term University Grant No. 304/PKOMP/6313280.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • K. Shanmugasundaram
    • 1
  • A. S. A. Mohamed
    • 1
    Email author
  • N. I. R. Ruhaiyem
    • 1
  1. 1.School of Computer SciencesUniversiti Sains Malaysia (USM)GelugorMalaysia

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