Top

Soft Computing

Published in:

01-04-2014 | Methodologies and Application

Adaptive computational chemotaxis based on field in bacterial foraging optimization

Authors: Xin Xu, Hui-ling Chen

Published in: Soft Computing | Issue 4/2014

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

Bacterial foraging optimization (BFO) is predominately used to find solutions for real-world problems. One of the major characteristics of BFO is the chemotactic movement of a virtual bacterium that models a trial solution of the problems. It is pointed out that the chemotaxis employed by classical BFO usually results in sustained oscillation, especially on rough fitness landscapes, when a bacterium cell is close to the optima. In this paper we propose a novel adaptive computational chemotaxis based on the concept of field, in order to accelerate the convergence speed of the group of bacteria near the tolerance. Firstly, a simple scheme is designed for adapting the chemotactic step size of each field. Then, the scheme chooses the fields which perform better to boost further the convergence speed. Empirical simulations over several numerical benchmarks demonstrate that BFO with adaptive chemotactic operators based on field has better convergence behavior, as compared against other meta-heuristic algorithms.

previous article MOIRAE: A computational strategy to extract and represent structural information from experimental protein templates

next article A new approach to similarity and inclusion measures between general type-2 fuzzy sets

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Armitage J (1999) Bacterial tactic responses. Adv Microbiol Phys 41:229–290CrossRef

Biswas A, Dasgupta S, Das S, Abraham A (2007) Synergy of PSO and bacterial foraging optimization: a comparative study on numerical benchmarks. In: Proceeding of second international symposis hybrid artificial intell system (HAIS) advances soft computing servive, vol. 44. Innovations in hybrid intelligent systems, ASC. Springer, Germany, pp 255–263

Blat Y, Eisenbach M (1995) Tar-dependent and-independent pattern formation by salmonella typhimurium. J Bacteriol 177:1683–1691

Budrene E, Berg H (1991) Complex patterns formed by motile cells of Escherichia coli. Nature 349:630–633CrossRef

Budrene E, Berg H (1995) Dynamics of formation of symmetrical patterns by chemotactic bacteria. Nature 376:49–53CrossRef

Chen H-L, Yang B, Wang G, Liu J, Wang S-J, Liu D-Y (2011) A novel bankruptcy prediction model based on an adaptive fuzzy k-nearest neighbor method. Knowl Base Syst 24(8):1348–1359CrossRef

Dasgupta S, Das S, Abraham A, Biswas A (2009) Adaptive computational chemotaxis in bacterial foraging optimization: an analysis. IEEE Trans Evol Comput 13(4):919–941CrossRef

Dasgupta S, Das S et al (2010) Automatic circle detection on digital images with an adaptive bacterial foraging algorithm. Soft Comput 14(11):1151–1164CrossRef

Flury B (1997) A first course in multivariate statistics, vol 28. Springer, New York

Holland JH (1975) Adaptation in natural and artificial systems. University of Michigan Press, Ann Harbor

Hughes BD (1996) Random walks and random environments: random walks, vol 1. Oxford University Press, LondonMATH

Kennedy J, Eberhart R (1995) Particle swarm optimization. Proceeding of IEEE international conference of neural network, In, pp 1942–1948

Kim DH, Cho CH (2005) Bacterial foraging based neural network fuzzy learning. In: Proceeding of 2nd Indian international conference on artificial intelligence (IICAI), pp 2030–2036

Kim DH, Abraham A, Cho JH (2007) A hybrid genetic algorithm and bacterial foraging approach for global optimization. Inf Sci 177(18):3918–3937CrossRef

Liu Y, Passino KM (2002) Biomimicry of social foraging bacteria for distributed optimization: models, principles, and emergent behaviors. J Optim Theory Appl 115(3):603–628CrossRefMATHMathSciNet

Majhi R, Panda G et al (2009) Efficient prediction of stock market indices using adaptive bacterial foraging optimization (ABFO) and BFO based techniques. Expert Syst Appl 36(6):10097–10104CrossRef

Mishra S (2005) A hybrid least square-fuzzy bacterial foraging strategy for harmonic estimation. IEEE Trans Evol Comput 9(1):61–73CrossRef

Mishra S, Bhende CN (2007) Bacterial foraging technique-based optimized active power filter for load compensation. IEEE Trans Power Deliv 22(1):457–465CrossRef

Passino KM (2002) Biomimicry of bacterial foraging for distributed optimization and control. IEEE Control Syst Mag 22(3):52–67CrossRefMathSciNet

Ratnaweera A, Halgamuge KS (2004) Self organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients. IEEE Trans Evol Comput 8(3):240–254CrossRef

Sathya PD, Kayalvizhi R (2011) Optimal segmentation of brain MRI based on adaptive bacterial foraging algorithm. Neurocomputing 74(14–15):2299–2313CrossRef

Stephens D, Krebs J (1986) Foraging theory. Princeton University Press, Princeton

Sun X, Liu Y, Li J, Zhu J, Chen H, Liu X (2012) Feature evaluation and selection with cooperative game theory. Pattern Recognit 45(8):2992–3002CrossRef

Thomsen R (2003) Flexible ligand docking using evolutionary algorithms: investigating the effects of variation operators and local search hybrids. BioSystems 72(1–2):57–73CrossRef

Tripathy M, Mishra S, Lai LL, Zhang QP (2006) Transmission loss reduction based on FACTS and bacteria foraging algorithm. In: Proceeding of parallel problem solving from nature (PPSN), 9–13 Sept 2006, pp 222–231

Woodward D, Tyson R, Myerscough M, Murray J, Budrene E, Berg H (1995) Spatio-temporal patterns generated by Salmonella typhimurium. Biophys J 68:2181–2189CrossRef

Yao X, Liu Y, Lin G (1999) Evolutionary programming made faster. IEEE Trans Evol Comput 3(2):82–102CrossRef

Title: Adaptive computational chemotaxis based on field in bacterial foraging optimization
Authors: Xin Xu
Hui-ling Chen
Publication date: 01-04-2014
Publisher: Springer Berlin Heidelberg
Published in: Soft Computing / Issue 4/2014
Print ISSN: 1432-7643
Electronic ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-013-1089-4

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Wirtschaft"

Springer Professional "Technik"

Other articles of this Issue 4/2014

Preface

Quantum immune clonal coevolutionary algorithm for dynamic multiobjective optimization

MOIRAE: A computational strategy to extract and represent structural information from experimental protein templates

The relationship among soft sets, soft rough sets and topologies

On the influence of different number generators on results of the symbolic regression

Improving a multi-objective differential evolution optimizer using fuzzy adaptation and $$K$$ K -medoids clustering

Premium Partner