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.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.References
Armitage J (1999) Bacterial tactic responses. Adv Microbiol Phys 41:229–290
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 349:630–633
Budrene E, Berg H (1995) Dynamics of formation of symmetrical patterns by chemotactic bacteria. Nature 376:49–53
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):1348–1359
Dasgupta S, Das S, Abraham A, Biswas A (2009) Adaptive computational chemotaxis in bacterial foraging optimization: an analysis. IEEE Trans Evol Comput 13(4):919–941
Dasgupta S, Das S et al (2010) Automatic circle detection on digital images with an adaptive bacterial foraging algorithm. Soft Comput 14(11):1151–1164
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, London
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):3918–3937
Liu Y, Passino KM (2002) Biomimicry of social foraging bacteria for distributed optimization: models, principles, and emergent behaviors. J Optim Theory Appl 115(3):603–628
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):10097–10104
Mishra S (2005) A hybrid least square-fuzzy bacterial foraging strategy for harmonic estimation. IEEE Trans Evol Comput 9(1):61–73
Mishra S, Bhende CN (2007) Bacterial foraging technique-based optimized active power filter for load compensation. IEEE Trans Power Deliv 22(1):457–465
Passino KM (2002) Biomimicry of bacterial foraging for distributed optimization and control. IEEE Control Syst Mag 22(3):52–67
Ratnaweera A, Halgamuge KS (2004) Self organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients. IEEE Trans Evol Comput 8(3):240–254
Sathya PD, Kayalvizhi R (2011) Optimal segmentation of brain MRI based on adaptive bacterial foraging algorithm. Neurocomputing 74(14–15):2299–2313
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):2992–3002
Thomsen R (2003) Flexible ligand docking using evolutionary algorithms: investigating the effects of variation operators and local search hybrids. BioSystems 72(1–2):57–73
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 68:2181–2189
Yao X, Liu Y, Lin G (1999) Evolutionary programming made faster. IEEE Trans Evol Comput 3(2):82–102
Acknowledgments
The authors are highly appreciative for the assistance extended by Sambarta Dasgupta in providing the source code of ABFO.
Author information
Authors and Affiliations
Corresponding author
Additional information
Communicated by A.-A. Tantar.
Rights and permissions
About this article
Cite this article
Xu, X., Chen, Hl. Adaptive computational chemotaxis based on field in bacterial foraging optimization. Soft Comput 18, 797–807 (2014). https://doi.org/10.1007/s00500-013-1089-4
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00500-013-1089-4