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Littérature scientifique sur le sujet « BACTERIA FORAGING »

Auteur : Grafiati

Publié le 11 septembre 2023

Mis à jour le 25 juillet 2025

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Articles de revues sur le sujet "BACTERIA FORAGING"

Passino, Kevin M. "Bacterial Foraging Optimization." International Journal of Swarm Intelligence Research 1, no. 1 (2010): 1–16. http://dx.doi.org/10.4018/jsir.2010010101.

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The bacterial foraging optimization (BFO) algorithm mimics how bacteria forage over a landscape of nutrients to perform parallel nongradient optimization. In this article, the author provides a tutorial on BFO, including an overview of the biology of bacterial foraging and the pseudo-code that models this process. The algorithms features are briefly compared to those in genetic algorithms, other bio-inspired methods, and nongradient optimization. The applications and future directions of BFO are also presented.

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Panda, Rutuparna, and Manoj Kumar Naik. "A Crossover Bacterial Foraging Optimization Algorithm." Applied Computational Intelligence and Soft Computing 2012 (2012): 1–7. http://dx.doi.org/10.1155/2012/907853.

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This paper presents a modified bacterial foraging optimization algorithm called crossover bacterial foraging optimization algorithm, which inherits the crossover technique of genetic algorithm. This can be used for improvising the evaluation of optimal objective function values. The idea of using crossover mechanism is to search nearby locations by offspring (50 percent of bacteria), because they are randomly produced at different locations. In the traditional bacterial foraging optimization algorithm, search starts from the same locations (50 percent of bacteria are replicated) which is not d

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Kanagasabai, Lenin. "Diminution of factual power loss by enhanced bacterial foraging optimization algorithm." International Journal of Applied Power Engineering 9, no. 3 (2022): 245~249. https://doi.org/10.5281/zenodo.7353285.

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This paper presents an enhanced bacterial foraging optimization (EBFO) algorithm for solving the optimal reactive power problem. Bacterial foraging optimization is based on foraging behaviour of Escherichia coli bacteria which present in the human intestine. Bacteria have inclination to congregate the nutrient-rich areas by an action called as Chemo taxis. The bacterial foraging process consists of four chronological methods i.e. chemo taxis, swarming and reproduction and elimination-dispersal. In this work rotation angle adaptively and incessantly modernized, which augment the diversity of th

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Wei, Zhong-hua, Xia Zhao, Ke-wen Wang, and Yan Xiong. "Bus Dispatching Interval Optimization Based on Adaptive Bacteria Foraging Algorithm." Mathematical Problems in Engineering 2012 (2012): 1–10. http://dx.doi.org/10.1155/2012/389086.

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The improved bacterial foraging algorithm was applied in this paper to schedule the bus departing interval. Optimal interval can decrease the total operation cost and passengers’ mean waiting time. The principles of colony sensing, chemotactic action, and improved foraging strategy made this algorithm adaptive. Based on adaptive bacteria foraging algorithm (ABFA), a model on one bus line in Hohhot city in China was established and simulated. Two other algorithms, original bacteria foraging algorithm (BFA) and genetic algorithm (GA), were also used in this model to decide which one could greatl

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Lenin, Kanagasabai. "Diminution of factual power loss by enhanced bacterial foraging optimization algorithm." International Journal of Applied Power Engineering (IJAPE) 9, no. 3 (2020): 245. http://dx.doi.org/10.11591/ijape.v9.i3.pp245-249.

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<div data-canvas-width="126.37004132231402">This paper presents an enhanced bacterial foraging optimization (EBFO) algorithm for solving the optimal reactive power problem. Bacterial foraging optimization is based on foraging behaviour of Escherichia coli bacteria which present in the human intestine. Bacteria have inclination to congregate the nutrient-rich areas by an action called as Chemo taxis. The bacterial foraging process consists of four chronological methods i.e. chemo taxis, swarming and reproduction and elimination-dispersal. In this work rotation angle a

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Yan, Xiaohui, Yunlong Zhu, Hao Zhang, Hanning Chen, and Ben Niu. "An Adaptive Bacterial Foraging Optimization Algorithm with Lifecycle and Social Learning." Discrete Dynamics in Nature and Society 2012 (2012): 1–20. http://dx.doi.org/10.1155/2012/409478.

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Bacterial Foraging Algorithm (BFO) is a recently proposed swarm intelligence algorithm inspired by the foraging and chemotactic phenomenon of bacteria. However, its optimization ability is not so good compared with other classic algorithms as it has several shortages. This paper presents an improved BFO Algorithm. In the new algorithm, a lifecycle model of bacteria is founded. The bacteria could split, die, or migrate dynamically in the foraging processes, and population size varies as the algorithm runs. Social learning is also introduced so that the bacteria will tumble towards better direct

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Shen, Hai, and Mo Zhang. "Bacterial Foraging Optimization Algorithm with Quorum Sensing Mechanism." Applied Mechanics and Materials 556-562 (May 2014): 3844–48. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.3844.

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Quorum sensing is widely distributed in bacteria and make bacteria are similar to complex adaptive systems, with intelligent features such as emerging and non-linear, the ultimate expression of the adaptive to changes in the environment. Based on the phenomenon of bacterial quorum sensing and Bacterial Foraging Optimization Algorithm, some new optimization algorithms have been proposed. In this paper, it presents research situations, such as environment-dependent quorum sensing mechanism, quorum sensing mechanism with quantum behavior, cell-to-cell communication, multi-colony communication, de

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Yawata, Yutaka, Francesco Carrara, Filippo Menolascina, and Roman Stocker. "Constrained optimal foraging by marine bacterioplankton on particulate organic matter." Proceedings of the National Academy of Sciences 117, no. 41 (2020): 25571–79. http://dx.doi.org/10.1073/pnas.2012443117.

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Optimal foraging theory provides a framework to understand how organisms balance the benefits of harvesting resources within a patch with the sum of the metabolic, predation, and missed opportunity costs of foraging. Here, we show that, after accounting for the limited environmental information available to microorganisms, optimal foraging theory and, in particular, patch use theory also applies to the behavior of marine bacteria in particle seascapes. Combining modeling and experiments, we find that the marine bacteriumVibrio ordaliioptimizes nutrient uptake by rapidly switching between attac

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Nasir, Ahmad N. K., M. O. Tokhi, and N. Maniha Abd Ghani. "Novel Adaptive Bacteria Foraging Algorithms for Global Optimization." Applied Computational Intelligence and Soft Computing 2014 (2014): 1–7. http://dx.doi.org/10.1155/2014/494271.

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This paper presents improved versions of bacterial foraging algorithm (BFA). The chemotaxis feature of bacteria through random motion is an effective strategy for exploring the optimum point in a search area. The selection of small step size value in the bacteria motion leads to high accuracy in the solution but it offers slow convergence. On the contrary, defining a large step size in the motion provides faster convergence but the bacteria will be unable to locate the optimum point hence reducing the fitness accuracy. In order to overcome such problems, novel linear and nonlinear mathematical

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AMIT, D.PUROHIT, and S. T. KHANDARE PROF. "COLOR IMAGE SEGMENTATION TECHNIQUE USING COOPERATIVE BACTERIAL FORAGING ALGORITHM." JournalNX - A Multidisciplinary Peer Reviewed Journal 3, no. 5 (2017): 56–59. https://doi.org/10.5281/zenodo.1446386.

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 Image segmentation is a crucial and challenging problem in image processing and often a basic step for high level analysis. The intent of image segmentation is to divide an image into different classes based on features, such as color, intensity or histogram, where each pixel in the image should go to one class and only one class. According to the thresholds the segmented results whether or not consistent to the image is also an issue should be considered.Here wepropose a new method for color image segmentation using multilevel thresholding. This paper proposes multilevel thresholding fo

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Thèses sur le sujet "BACTERIA FORAGING"

Vetter, Yves-Alain. "Bacterial foraging with cell-free enzymes /." Thesis, Connect to this title online; UW restricted, 1998. http://hdl.handle.net/1773/11033.

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Ladevèze, Simon. "Functional and structural insights into Glycoside Hydrolase family 130 enzymes : implications in carbohydrate foraging by human gut bacteria." Thesis, Toulouse, INSA, 2015. http://www.theses.fr/2015ISAT0010/document.

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Les relations entre bactéries intestinales, aliments et hôte jouent un rôle crucial dans lemaintien de la santé humaine. La caractérisation fonctionnelle d’Uhgb_MP, une enzyme dela famille 130 des glycoside hydrolases découverte par métagénomique fonctionnelle, arévélé une nouvelle fonction de dégradation par phosphorolyse des polysaccharides de laparoi végétale et des glycanes de l'hôte tapissant l'épithélium intestinal. Les déterminantsmoléculaires de la spécificité d’Uhgb_MP vis-à-vis des mannosides ont été identifiés grâce àla résolution de sa structure cristallographique, sous forme apo e

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Harso, Wahyu [Verfasser], Eckhard [Gutachter] George, Christof [Gutachter] Engels, and Klaus [Gutachter] Dittert. "The mycorrhizal plant root system : foraging activities and interaction with soil bacteria in heterogeneous soil environments / Wahyu Harso. Gutachter: Eckhard George ; Christof Engels ; Klaus Dittert." Berlin : Lebenswissenschaftliche Fakultät, 2016. http://d-nb.info/1112193022/34.

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Tang, W. J. "Optimisation algorithms inspired from modelling of bacterial foraging patterns and their applications." Thesis, University of Liverpool, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.490623.

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Research in biologically-inspired optimisation has been fl<;lurishing over the past decades. This approach adopts a bott0!ll-up viewpoint to understand and mimic certain features of a biological system. It has been proved useful in developing nondeterministic algorithms, such as Evolutionary Algorithms (EAs) and Swarm Intelligence (SI). Bacteria, as the simplest creature in nature, are of particular interest in recent studies. In the past thousands of millions of years, bacteria have exhibited a self-organising behaviour to cope with the natural selection. For example, bacteria have developed

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Nasir, Ahmad. "Bacterial foraging and spiral dynamics based metaheuristic algorithms for global optimisation with engineering applications." Thesis, University of Sheffield, 2014. http://etheses.whiterose.ac.uk/7068/.

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Supriyono, Heru. "Novel bacterial foraging optimisation algorithms with application to modelling and control of flexible manipulator systems." Thesis, University of Sheffield, 2012. http://etheses.whiterose.ac.uk/2122/.

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Biologically-inspired soft-computing algorithms, which were developed by mimicking evolution and foraging techniques of animals in nature, have attracted significant attention of researchers. The works are including the development of the algorithm itself, its modification and its application in broad areas. This thesis presents works on biologically-inspired algorithm based on bacterial foraging algorithm (BFA) and its performance evaluation in modelling and control of dynamic systems. The main aim of the research is to develop new modifications of BFA and its combination with other soft comp

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TIWARI, RAM MUKUND. "FUZZY EDGE DETECTION OF BLURRED IMAGE USING BACTERIA FORAGING." Thesis, 2012. http://dspace.dtu.ac.in:8080/jspui/handle/repository/14020.

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This paper proposes an approach to edge detection of blurred color images. The edge detector involves two phases –Deblurring of color image using wavelet and edge detection using bacteria foraging. Here deblurring is performed without estimating the imge blur. The deblurring algorithm performs deblurring in the spectrum domain. In edge detection process, we find out the edge pixels on the basis of intensity difference value of pixel in their 8-neighbourhood. First step is Chemotaxis step in which we calculate the eight directional nutrients in the form of intensity difference and find out the

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KUMAR, AJAY. "EDGE DETECTION USING BACTERIA FORAGING & FUZZY SIMILARITY MEASURE." Thesis, 2012. http://dspace.dtu.ac.in:8080/jspui/handle/repository/14024.

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Edges characterize boundaries and edge detection is one of the most difficult tasks in image processing hence it is a problem of fundamental importance in image processing. Edges in images are areas with strong intensity contrasts and a jump in intensity from one pixel to the next can create major variation in the picture quality. Edge detection of an image significantly reduces the amount of data and filters out useless information, while preserving the important structural properties in an image.In the proposed method, the bacteria foraging is used along with contemporary fuzzy logic which i

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Das, Saikishan, and K. Prasanna. "Multiple robot co-ordination using particle swarm optimisation and bacteria foraging algorithm." Thesis, 2010. http://ethesis.nitrkl.ac.in/1886/1/B.Tech_Project_Thesis_Saikishan_Das(10603062).pdf.

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The use of multiple robots to accomplish a task is certainly preferable over the use of specialised individual robots. A major problem with individual specialized robots is the idle-time, which can be reduced by the use of multiple general robots, therefore making the process economical. In case of infrequent tasks, unlike the ones like assembly line, the use of dedicated robots is not cost-effective. In such cases, multiple robots become essential. This work involves path-planning and co-ordination between multiple mobile agents in a static-obstacle environment. Multiple small robots (swarms)

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Lee, Kuo-Wei, and 李國維. "Improved Bacterial Foraging Optimization." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/22851452298832117486.

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碩士 大同大學 資訊經營學系(所) 101 This paper proposes an improved approach involving bacterial foraging optimization algorithm (BFOA) behavior. The new algorithm is called improved bacterial foraging optimization (IBFO). BFOA is a new swarm intelligence technique. Three main BFOA operation are chemotaxis, reproduction and elimination-dispersal, which are applied to global and local random searches. This powerful and effective algorithm has been used to solve various real-world optimization problem. However , BFOA has several shortages: many parameters needed to be set ; tumble angles are gener

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Livres sur le sujet "BACTERIA FORAGING"

Stephenson, Steven. Secretive Slime Moulds. CSIRO Publishing, 2021. http://dx.doi.org/10.1071/9781486314140.

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Neither plants, nor animals, nor fungi, the myxomycetes are a surprisingly diverse and fascinating group of organisms. They spend the majority of their life out of sight as single-celled amoeboid individuals in leaf litter, soil or decaying wood, foraging for bacteria and other simple life forms. However, when conditions are right, two individual cells come together to give rise to a much larger, creeping structure called a plasmodium, which produces the even more complex and often beautiful fruiting bodies. Indeed, the fruiting bodies of myxomycetes are often miniature works of art! The

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Chapitres de livres sur le sujet "BACTERIA FORAGING"

Gazi, Veysel, and Kevin M. Passino. "Bacteria Foraging Optimization." In Swarm Stability and Optimization. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-18041-5_11.

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Peh, Sally Chen Woon, and Jer Lang Hong. "Bacteria Foraging Optimization for Drug Design." In Computational Science and Its Applications -- ICCSA 2016. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-42111-7_25.

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Liang, Dongying, Weikun Zheng, and Yueping Li. "Bacteria Foraging Based Agent Feature Selection Algorithm." In Communications in Computer and Information Science. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-18129-0_89.

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Mahapatra, Gautam, Soumya Banerjee, and Ponnuthurai Nagaratnam Suganthan. "Bilevel Optimization Using Bacteria Foraging Optimization Algorithm." In Swarm, Evolutionary, and Memetic Computing. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-20294-5_31.

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Senapati, Ranjan K., P. Bhavya Reddy, K. Thirumalesh, and E. Vamshi Krishna. "Robust Video Watermarking Using Bacteria Foraging Optimization." In Advances in Engineering Research. Atlantis Press International BV, 2023. http://dx.doi.org/10.2991/978-94-6463-252-1_88.

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Selva Rani, B., and Ch Aswani Kumar. "A Comprehensive Review on Bacteria Foraging Optimization Technique." In Multi-objective Swarm Intelligence. Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-46309-3_1.

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Agrawal, Rajesh, Prashant Sahai Saxena, Vijay Singh Rathore, and Saurabh Maheshwari. "Segmentation of Handwritten Text Using Bacteria Foraging Optimization." In Smart Innovation, Systems and Technologies. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-0077-0_48.

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Mahapatra, Gautam, Soumya Banerjee, and Ranjan Chattaraj. "Bi-Level Optimization Using Improved Bacteria Foraging Optimization Algorithm." In Soft Computing Applications. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-52190-5_19.

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Shreyas, J., Chethana S. Reddy, P. K. Udayaprasad, Dhramendra Chouhan, and S. M. Dilip Kumar. "Bacteria Foraging Optimization-Based Geographical Routing Scheme in IoT." In Algorithms for Intelligent Systems. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-4604-8_32.

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Tripathy, M., S. Mishra, L. L. Lai, and Q. P. Zhang. "Transmission Loss Reduction Based on FACTS and Bacteria Foraging Algorithm." In Parallel Problem Solving from Nature - PPSN IX. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11844297_23.

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Actes de conférences sur le sujet "BACTERIA FORAGING"

Kumar, Dev, Sandeep Kumar Sunori, and Shilpa Jain. "Bacterial Foraging Optimization of Electric Vehicle Charging Systems." In 2024 International Conference on Sustainable Communication Networks and Application (ICSCNA). IEEE, 2024. https://doi.org/10.1109/icscna63714.2024.10863835.

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Munoz, Mario A., Saman K. Halgamuge, Wilfredo Alfonso, and Eduardo F. Caicedo. "Simplifying the Bacteria Foraging Optimization Algorithm." In 2010 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2010. http://dx.doi.org/10.1109/cec.2010.5586025.

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Acharya, D. P., G. Panda, S. Mishra, and Y. V. S. Lakshmi. "Bacteria Foraging Based Independent Component Analysis." In International Conference on Computational Intelligence and Multimedia Applications (ICCIMA 2007). IEEE, 2007. http://dx.doi.org/10.1109/iccima.2007.126.

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Hui Liu, Hong Chen, and Li Kong. "Bacteria foraging optimization-based extremum seeking control." In 2010 IEEE International Conference on Intelligent Computing and Intelligent Systems (ICIS 2010). IEEE, 2010. http://dx.doi.org/10.1109/icicisys.2010.5658855.

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Elaydi, Hatem A., and Ramzi J. Al Ghamri. "Designing Adaptive Control Based on Bacteria Foraging Optimization." In 2017 Palestinian International Conference on Information and Communication Technology (PICICT). IEEE, 2017. http://dx.doi.org/10.1109/picict.2017.16.

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Hazra, J., and A. K. Sinha. "Environmental Constrained Economic Dispatch using Bacteria Foraging Optimization." In 2008 Joint International Conference on Power System Technology and IEEE Power India Conference (POWERCON). IEEE, 2008. http://dx.doi.org/10.1109/icpst.2008.4745330.

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Pitchaimanickam, B., and S. Radhakrishnan. "Bacteria Foraging Algorithm based clustering in Wireless Sensor Networks." In 2013 Fifth International Conference on Advanced Computing (ICoAC). IEEE, 2013. http://dx.doi.org/10.1109/icoac.2013.6921949.

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Lei, Xiujuan, Shuang Wu, Liang Ge, and Aidong Zhang. "Clustering PPI Data Based on Bacteria Foraging Optimization Algorithm." In 2011 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2011. http://dx.doi.org/10.1109/bibm.2011.18.

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Yadav, Daksha, Mayank Vatsa, Richa Singh, and Massimo Tistarelli. "Bacteria Foraging Fusion for Face Recognition across Age Progression." In 2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE, 2013. http://dx.doi.org/10.1109/cvprw.2013.33.

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Ram, Gopi, P. Chakravorty, Durbadal Mandal, Rajib Kar, Sakti Prasad Ghoshal, and S. Banerjee. "Radiation pattern synthesis of TMCAA using bacteria foraging optimization." In 2015 IEEE International WIE Conference on Electrical and Computer Engineering (WIECON-ECE). IEEE, 2015. http://dx.doi.org/10.1109/wiecon-ece.2015.7443948.

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