The Bacterial Foraging Optimization Algorithm is inspired by the group foraging behavior of bacteria such as E.coli and M.xanthus. Specifically, the BFOA is inspired by the chemotaxis behavior of bacteria that will perceive chemical gradients in the environment (such as nutrients) and move toward or away from specific signals.
You can refer to following articles for more info:
Kiran, M., Choudary, S., & Sunita, M. (2013, January). Bacterial Foraging Optimization: Review. In International Journal of Engineering Research and Technology (Vol. 2, No. 7 (July-2013)). ESRSA Publications.
Chen, H., Zhu, Y., & Hu, K. (2011, March). Adaptive bacterial foraging optimization. In Abstract and Applied Analysis (Vol. 2011). Hindawi Publishing Corporation.
S. Das and A. Biswas and S. Dasgupta and A. Abraham, "Bacterial Foraging Optimization Algorithm: Theoretical Foundations, Analysis, and Applications", in Foundations of Computational Intelligence Volume 3: Global Optimization, pages 23–55, Springer, 2009.
S. D. Müller and J. Marchetto and S. Airaghi and P. Koumoutsakos, "Optimization Based on Bacterial Chemotaxis", IEEE Transactions on Evolutionary Computation, 2002
. K. M. Passino, "Biomimicry of bacterial foraging for distributed optimization and control", IEEE Control Systems Magazine, 2002.
K. M. Passino, "Part V: Foraging", in Biomimicry for Optimization, Control, and Automation, Springer, 2005.
K. M. Passino, "Bacterial Foraging Optimization", International Journal of Swarm Intelligence Research, 2010.
Majhi, R., Panda, G., Majhi, B., & Sahoo, G. (2009). Efficient prediction of stock market indices using adaptive bacterial foraging optimization (ABFO) and BFO based techniques. Expert Systems with Applications, 36(6), 10097-10104.
The Bacterial Foraging Optimization Algorithm is inspired by the group foraging behavior of bacteria such as E.coli and M.xanthus. Specifically, the BFOA is inspired by the chemotaxis behavior of bacteria that will perceive chemical gradients in the environment (such as nutrients) and move toward or away from specific signals.
You can refer to following articles for more info:
Kiran, M., Choudary, S., & Sunita, M. (2013, January). Bacterial Foraging Optimization: Review. In International Journal of Engineering Research and Technology (Vol. 2, No. 7 (July-2013)). ESRSA Publications.
Chen, H., Zhu, Y., & Hu, K. (2011, March). Adaptive bacterial foraging optimization. In Abstract and Applied Analysis (Vol. 2011). Hindawi Publishing Corporation.
S. Das and A. Biswas and S. Dasgupta and A. Abraham, "Bacterial Foraging Optimization Algorithm: Theoretical Foundations, Analysis, and Applications", in Foundations of Computational Intelligence Volume 3: Global Optimization, pages 23–55, Springer, 2009.
S. D. Müller and J. Marchetto and S. Airaghi and P. Koumoutsakos, "Optimization Based on Bacterial Chemotaxis", IEEE Transactions on Evolutionary Computation, 2002
. K. M. Passino, "Biomimicry of bacterial foraging for distributed optimization and control", IEEE Control Systems Magazine, 2002.
K. M. Passino, "Part V: Foraging", in Biomimicry for Optimization, Control, and Automation, Springer, 2005.
K. M. Passino, "Bacterial Foraging Optimization", International Journal of Swarm Intelligence Research, 2010.
Majhi, R., Panda, G., Majhi, B., & Sahoo, G. (2009). Efficient prediction of stock market indices using adaptive bacterial foraging optimization (ABFO) and BFO based techniques. Expert Systems with Applications, 36(6), 10097-10104.
Bacterial foraging algorithm (BFA) used the technique of foraging of bacteria. This idea is based on the fact that that natural selection tends to eliminate animals with foraging strategies through methods for locating, handling, and ingesting food, and to favor the propagation of genes of those animals that have successful foraging strategies. They are more likely to apply reproductive success to have an optimal solution. After many generations, poor foraging strategies are either eliminated or shaped into good ones. Such evolutionary principles have led scientists in the field of engineering to hypothesize the activity of foraging as an optimization process. The E. coli bacteria that are present in our intestines have a foraging strategy governed by four processes, namely, chemo taxis, swarming, reproduction, and elimination and dispersal.
For your reference
[1]. K. M. Passino, “Biomimicry of bacterial foraging for distributed optimization and control”, IEEE Control Syst., vol.22, no.3, pp.52–67, 2002.
[2]. B.K. Panigrahi , V. R. Pandi , S. Das and S. Das ,“Multiobjective fuzzy dominance based bacterial foraging algorithm to solve economic emission dispatch problem”, Energy, vol. 35,pp. 4761-4770,2010.
[3]. P.K. Hota, A.K. Barisal and R. chakrabarti “Economic emission load dispatch through fuzzy based bacterial foraging algorithm”, Int. J. Electr. Power Energy Syst., vol. 32, pp. 794-803, 2010.
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