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Erschienen in:
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2017 | OriginalPaper | Buchkapitel

Hybrid Improved Bacterial Swarm (HIBS) Optimization Algorithm

verfasst von : K. Shanmugasundaram, A. S. A. Mohamed, N. I. R. Ruhaiyem

Erschienen in: Advances in Visual Informatics

Verlag: Springer International Publishing

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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.

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Metadaten
Titel
Hybrid Improved Bacterial Swarm (HIBS) Optimization Algorithm
verfasst von
K. Shanmugasundaram
A. S. A. Mohamed
N. I. R. Ruhaiyem
Copyright-Jahr
2017
Buch
Advances in Visual Informatics

Print ISBN: 978-3-319-70009-0

Electronic ISBN: 978-3-319-70010-6

Copyright-Jahr: 2017

DOI
https://doi.org/10.1007/978-3-319-70010-6_7