Literatura académica sobre el tema "BACTERIAL FORAGING"
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Artículos de revistas sobre el tema "BACTERIAL FORAGING"
Passino, Kevin M. "Bacterial Foraging Optimization". International Journal of Swarm Intelligence Research 1, n.º 1 (enero de 2010): 1–16. http://dx.doi.org/10.4018/jsir.2010010101.
Texto completoPanda, Rutuparna y 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.
Texto completoChen, Hanning, Yunlong Zhu y Kunyuan Hu. "Adaptive Bacterial Foraging Optimization". Abstract and Applied Analysis 2011 (2011): 1–27. http://dx.doi.org/10.1155/2011/108269.
Texto completoChen, Hanning, Ben Niu, Lianbo Ma, Weixing Su y Yunlong Zhu. "Bacterial colony foraging optimization". Neurocomputing 137 (agosto de 2014): 268–84. http://dx.doi.org/10.1016/j.neucom.2013.04.054.
Texto completoChen, Hanning, Yunlong Zhu y Kunyuan Hu. "Cooperative Bacterial Foraging Optimization". Discrete Dynamics in Nature and Society 2009 (2009): 1–17. http://dx.doi.org/10.1155/2009/815247.
Texto completoLenin, Kanagasabai. "Diminution of factual power loss by enhanced bacterial foraging optimization algorithm". International Journal of Applied Power Engineering (IJAPE) 9, n.º 3 (1 de diciembre de 2020): 245. http://dx.doi.org/10.11591/ijape.v9.i3.pp245-249.
Texto completoShen, Hai y Mo Zhang. "Bacterial Foraging Optimization Algorithm with Quorum Sensing Mechanism". Applied Mechanics and Materials 556-562 (mayo de 2014): 3844–48. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.3844.
Texto completoCho, Jae-Hoon, Dae-Jong Lee y Myung-Geun Chun. "Parameter Optimization of Extreme Learning Machine Using Bacterial Foraging Algorithm". Journal of Korean Institute of Intelligent Systems 17, n.º 6 (25 de diciembre de 2007): 807–12. http://dx.doi.org/10.5391/jkiis.2007.17.6.807.
Texto completoNiu, Ben, Hong Wang, Jingwen Wang y Lijing Tan. "Multi-objective bacterial foraging optimization". Neurocomputing 116 (septiembre de 2013): 336–45. http://dx.doi.org/10.1016/j.neucom.2012.01.044.
Texto completoWei, Zhong-hua, Xia Zhao, Ke-wen Wang y 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.
Texto completoTesis sobre el tema "BACTERIAL 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.
Texto completoTang, 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.
Texto completoNasir, 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/.
Texto completoSupriyono, 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/.
Texto completoLadevèze, 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.
Texto completoThe interplay between gut bacteria, food and host play a key role in human health. Thefunctional characterization of Uhgb_MP, an enzyme belonging to the family 130 of glycosidehydrolases, discovered by functional metagenomics, revealed novel functions of plant cellwall polysaccharide and host glycan degradation by phosphorolysis. The moleculardeterminants of Uhgb_MP specificity towards mannosides were identified by solving itscrystal structure, in apo form and in complex with its ligands. A new process of high addedvalue mannosylated oligosaccharide synthesis by reverse-phosphorolysis was alsodeveloped. Finally, the functional characterization of the BACOVA_03624 protein fromBacteroides ovatus ATCC 8483, a highly prevalent gut bacterium, revealed that GH130 familyboth contains glycoside phosphorylases and glycoside hydrolases, which are able to degrademannosides and galactosides, and to synthesize them by reverse-phosphorolysis and/ortransglycosylation. All these results, together with the identification of GH130 enzymeinhibitors, open new perspectives for studying, and potentially also for controlling,interactions between host and gut microbes
Harso, Wahyu [Verfasser], Eckhard [Gutachter] George, Christof [Gutachter] Engels y 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.
Texto completoLee, Kuo-Wei y 李國維. "Improved Bacterial Foraging Optimization". Thesis, 2013. http://ndltd.ncl.edu.tw/handle/22851452298832117486.
Texto completo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 generated randomly and a fixed chemotactic step size causing poor convergence. In this paper, we try to improve these shortages of BFOA base on reduce setting parameters. Finally, we compare the performance of IBFO with the classical BFOA, testing them on seven widely-used benchmark functions. The experimental result shows that the IBFO is very competitive and outperforms the BFOA.
Lin, Guan-Yu y 林冠喻. "Bacterial foraging for watermarkings applications". Thesis, 2009. http://ndltd.ncl.edu.tw/handle/66p8px.
Texto completo國立高雄大學
電機工程學系碩士班
97
In recent years, along with the booming application of the Internet, digital files and associated multimedia contents can be easily acquired in our daily lives. With the inherent characteristics of lossless copying and easy spreading, the intellectual property or ownerships of the multimedia contents have become a rising problem. Data hiding and watermarking techniques aiming at protecting copyright-related issues are of considerable interest in academia and industry. In this thesis, we mainly focus on improving the requirements of watermarking applications, including the watermark robustness and the invisibility in the frequency domain. Since the requirements tend to have conflicts, we employ bacterial foraging for training the watermarking algorithm and obtain the optimized solution. With the simulations presented, bacterial foraging provides a systematic way to balance the contributions by the watermarking requirements, and to offer another scope for designing an effective algorithm for watermarking.
Allemneny, Raghuveer. "Bacterial Foraging Based Channel Equalizers". Thesis, 2006. http://ethesis.nitrkl.ac.in/24/1/raghuveer.pdf.
Texto completoCheng, Hsiu-Tzu y 鄭秀姿. "Bacterial Foraging Optimization for Portfolio Optimizations". Thesis, 2012. http://ndltd.ncl.edu.tw/handle/79562681397598793645.
Texto completo大同大學
資訊經營學系(所)
100
Portfolio optimization (PO) is a mixed quadratic and integer programming problem, and an effective solution approach is essential for most investors in order to raise expected returns and reduce investment risks. To solve this problem, various heuristic algorithms, such as genetic algorithms and particle swarm optimization, have been proposed in the past. This paper aims to examine the potential of bacterial foraging optimization algorithms (BFO) for solving the portfolio optimization problem. Bacterial foraging optimization algorithm is a new swarm intelligence technique and has successfully applied to some real world problems. Through three operations, chemotaxis, reproduction, and elimination and dispersal, the proposed BFO algorithm can effectively solve a PO problem with cardinality and bounding constraints. The performance of BFO approach was evaluated by performing computational tests on five benchmark data sets, and the computational results were compared to those obtained with existing heuristic algorithms. Experimental results demonstrate that the proposed algorithm is very competitive in portfolio optimization.
Libros sobre el tema "BACTERIAL FORAGING"
Stephenson, Steven. Secretive Slime Moulds. CSIRO Publishing, 2021. http://dx.doi.org/10.1071/9781486314140.
Texto completoCapítulos de libros sobre el tema "BACTERIAL FORAGING"
Du, Ke-Lin y M. N. S. Swamy. "Bacterial Foraging Algorithm". En Search and Optimization by Metaheuristics, 217–25. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-41192-7_13.
Texto completoBrabazon, Anthony, Michael O’Neill y Seán McGarraghy. "Bacterial Foraging Algorithms". En Natural Computing Algorithms, 187–99. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-43631-8_11.
Texto completoParashar, Sonam, Nand K. Meena, Jin Yang y Neeraj Kanwar. "Bacterial Foraging Optimization". En Swarm Intelligence Algorithms, 31–42. First edition. | Boca Raton : Taylor and Francis, 2020.: CRC Press, 2020. http://dx.doi.org/10.1201/9780429422614-3.
Texto completoIacca, Giovanni, Ferrante Neri y Ernesto Mininno. "Compact Bacterial Foraging Optimization". En Swarm and Evolutionary Computation, 84–92. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-29353-5_10.
Texto completoPattnaik, S. S., K. M. Bakwad, S. Devi, B. K. Panigrahi y Sanjoy Das. "Parallel Bacterial Foraging Optimization". En Adaptation, Learning, and Optimization, 487–502. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-17390-5_21.
Texto completoAgrawal, Vivek, Harish Sharma y Jagdish Chand Bansal. "Bacterial Foraging Optimization: A Survey". En Advances in Intelligent and Soft Computing, 227–42. India: Springer India, 2012. http://dx.doi.org/10.1007/978-81-322-0487-9_23.
Texto completoBrabazon, Anthony y Seán McGarraghy. "Bacterial and Viral Foraging Algorithms". En Natural Computing Series, 267–95. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-59156-8_14.
Texto completoLiu, Wei, Yunlong Zhu, Ben Niu y Hanning Chen. "Optimization Based on Bacterial Colony Foraging". En Communications in Computer and Information Science, 489–94. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-31837-5_71.
Texto completoChen, Hanning, Yunlong Zhu, Kunyuan Hu, Xiaoxian He y Ben Niu. "Cooperative Approaches to Bacterial Foraging Optimization". En Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence, 541–48. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-85984-0_65.
Texto completoKanwar, Neeraj, Nand K. Meena, Jin Yang y Sonam Parashar. "Modified Bacterial Foraging Optimization and Application". En Swarm Intelligence Algorithms, 29–41. First edition. | Boca Raton : Taylor and Francis, 2020.: CRC Press, 2020. http://dx.doi.org/10.1201/9780429422607-3.
Texto completoActas de conferencias sobre el tema "BACTERIAL FORAGING"
Yichuan Shao y Hanning Chen. "Cooperative Bacterial Foraging Optimization". En 2009 International Conference on Future BioMedical Information Engineering (FBIE). IEEE, 2009. http://dx.doi.org/10.1109/fbie.2009.5405806.
Texto completoChen, Yanhai y Weixing Lin. "An improved bacterial foraging optimization". En 2009 IEEE International Conference on Robotics and Biomimetics (ROBIO). IEEE, 2009. http://dx.doi.org/10.1109/robio.2009.5420524.
Texto completoLi, Fei, Yuting Zhang, Jiulong Wu y Haibo Li. "Quantum bacterial foraging optimization algorithm". En 2014 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2014. http://dx.doi.org/10.1109/cec.2014.6900230.
Texto completoKasaiezadeh, Alireza, Amir Khajepour y Steven L. Waslander. "Spiral Bacterial Foraging Optimization method". En 2010 American Control Conference (ACC 2010). IEEE, 2010. http://dx.doi.org/10.1109/acc.2010.5530897.
Texto completoShao, Yichuan y Hanning Chen. "A novel cooperative bacterial foraging algorithm". En 2009 Fourth International Conference on Bio-Inspired Computing (BIC-TA). IEEE, 2009. http://dx.doi.org/10.1109/bicta.2009.5338157.
Texto completoRashtchi, Vahid, Akbar Bayat y Hesan Vahedi. "Adaptive step length bacterial foraging algorithm". En 2009 IEEE International Conference on Intelligent Computing and Intelligent Systems (ICIS 2009). IEEE, 2009. http://dx.doi.org/10.1109/icicisys.2009.5357834.
Texto completoShao, Yichuan y Hanning Chen. "The Optimization of Cooperative Bacterial Foraging". En 2009 WRI World Congress on Software Engineering. IEEE, 2009. http://dx.doi.org/10.1109/wcse.2009.195.
Texto completoSharifkhani, Fatemeh y Mohammad Reza Pakravan. "Bacterial foraging search in unstructured P2P networks". En 2014 IEEE 27th Canadian Conference on Electrical and Computer Engineering (CCECE). IEEE, 2014. http://dx.doi.org/10.1109/ccece.2014.6900982.
Texto completoBakwad, K. M., S. S. Pattnaik, B. S. Sohi, S. Devi, B. K. Panigrahi, Sanjoy Das y M. R. Lohokare. "Hybrid Bacterial Foraging with parameter free PSO". En 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC). IEEE, 2009. http://dx.doi.org/10.1109/nabic.2009.5393867.
Texto completoHanning Chen, Yunlong Zhu y Kunyuan Hu. "Cooperative Bacterial Foraging algorithm for global Optimization". En 2009 Chinese Control and Decision Conference (CCDC). IEEE, 2009. http://dx.doi.org/10.1109/ccdc.2009.5191509.
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