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Stability analysis of the reproduction operator in bacterial foraging optimization

Authors:

Sambarta DasguptaAuthors Info & Claims

Theoretical Computer Science, Volume 411, Issue 21

Pages 2127 - 2139

https://doi.org/10.1016/j.tcs.2010.03.005

Published: 01 May 2010 Publication History

Abstract

In his seminal paper published in 2002, Passino pointed out how individual and groups of bacteria forage for nutrients and how to model it as a distributed optimization process, which he named the Bacterial Foraging Optimization Algorithm (BFOA). One of the major operators of BFOA is the reproduction phenomenon of virtual bacteria, each of which models one trial solution of the optimization problem. During reproduction, the least healthy bacteria (with a lower accumulated value of the objective function in one chemotactic lifetime) die and the other healthier bacteria each split into two, which then starts exploring the search place from the same location. The phenomenon has a direct analogy with the selection mechanism of classical evolutionary algorithms. This paper attempts to model reproduction as a dynamics and then analyses the stability of the reproductive system very near to an equilibrium point, which in this case is an isolated optimum. It also finds conditions under which a stable reproduction event can take place, to direct a worse bacterium towards a better one. Our analysis reveals that a stable reproduction event contributes to the quick convergence of the bacterial population near optima.

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cover image Theoretical Computer Science

Theoretical Computer Science Volume 411, Issue 21

May, 2010

82 pages

ISSN:0304-3975

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Copyright © Elsevier B.V. © 2010.

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Elsevier Science Publishers Ltd.

United Kingdom

Publication History

Published: 01 May 2010

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Cited By

Sajwan AYadav A(2022)A study of exploratory and stability analysis of artificial electric field algorithmApplied Intelligence10.1007/s10489-021-02865-752:9(10805-10828)Online publication date: 1-Jul-2022
https://dl.acm.org/doi/10.1007/s10489-021-02865-7
(2017)An object classification method based on the improved bacterial foraging optimisation algorithmInternational Journal of Wireless and Mobile Computing10.1504/IJWMC.2017.08417512:2(166-173)Online publication date: 1-Jan-2017
https://dl.acm.org/doi/10.1504/IJWMC.2017.084175
Zhao WWang L(2016)An effective bacterial foraging optimizer for global optimizationInformation Sciences: an International Journal10.1016/j.ins.2015.10.001329:C(719-735)Online publication date: 1-Feb-2016
https://dl.acm.org/doi/10.1016/j.ins.2015.10.001
Mahmoodabadi MTaherkhorsandi MMaafi RCastillo-Villar K(2015)A novel multi-objective optimisation algorithmInternational Journal of Intelligent Engineering Informatics10.1504/IJIEI.2015.0730883:4(369-386)Online publication date: 1-Nov-2015
https://dl.acm.org/doi/10.1504/IJIEI.2015.073088
Meng GZhao QHu Y(2015)The variable HSS iteration based on the bacterial foraging optimisation algorithmInternational Journal of Computing Science and Mathematics10.1504/IJCSM.2015.0729696:5(471-479)Online publication date: 1-Nov-2015
https://dl.acm.org/doi/10.1504/IJCSM.2015.072969
Daas MChikhi SBatouche M(2015)Bacterial foraging optimization with double role of reproduction and step adaptationProceedings of the International Conference on Intelligent Information Processing, Security and Advanced Communication10.1145/2816839.2816868(1-5)Online publication date: 23-Nov-2015
https://dl.acm.org/doi/10.1145/2816839.2816868
Yıldız YAltun O(2015)Hybrid achievement oriented computational chemotaxis in bacterial foraging optimizationSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-015-1687-419:12(3647-3663)Online publication date: 1-Dec-2015
https://dl.acm.org/doi/10.1007/s00500-015-1687-4
Rajinikanth VLatha K(2012)Controller parameter optimization for nonlinear systems using enhanced bacteria foraging algorithmApplied Computational Intelligence and Soft Computing10.1155/2012/2142642012(22-22)Online publication date: 1-Jan-2012
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Latha KRajinikanth V(2012)2DOF PID controller tuning for unstable systems using bacterial foraging algorithmProceedings of the Third international conference on Swarm, Evolutionary, and Memetic Computing10.1007/978-3-642-35380-2_61(519-527)Online publication date: 20-Dec-2012
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