Littérature scientifique sur le sujet « BACTERIAL FORAGING »
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Articles de revues sur le sujet "BACTERIAL FORAGING"
Passino, Kevin M. « Bacterial Foraging Optimization ». International Journal of Swarm Intelligence Research 1, no 1 (janvier 2010) : 1–16. http://dx.doi.org/10.4018/jsir.2010010101.
Texte intégralPanda, Rutuparna, et 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.
Texte intégralChen, Hanning, Yunlong Zhu et Kunyuan Hu. « Adaptive Bacterial Foraging Optimization ». Abstract and Applied Analysis 2011 (2011) : 1–27. http://dx.doi.org/10.1155/2011/108269.
Texte intégralChen, Hanning, Ben Niu, Lianbo Ma, Weixing Su et Yunlong Zhu. « Bacterial colony foraging optimization ». Neurocomputing 137 (août 2014) : 268–84. http://dx.doi.org/10.1016/j.neucom.2013.04.054.
Texte intégralChen, Hanning, Yunlong Zhu et Kunyuan Hu. « Cooperative Bacterial Foraging Optimization ». Discrete Dynamics in Nature and Society 2009 (2009) : 1–17. http://dx.doi.org/10.1155/2009/815247.
Texte intégralLenin, Kanagasabai. « Diminution of factual power loss by enhanced bacterial foraging optimization algorithm ». International Journal of Applied Power Engineering (IJAPE) 9, no 3 (1 décembre 2020) : 245. http://dx.doi.org/10.11591/ijape.v9.i3.pp245-249.
Texte intégralShen, Hai, et Mo Zhang. « Bacterial Foraging Optimization Algorithm with Quorum Sensing Mechanism ». Applied Mechanics and Materials 556-562 (mai 2014) : 3844–48. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.3844.
Texte intégralCho, Jae-Hoon, Dae-Jong Lee et Myung-Geun Chun. « Parameter Optimization of Extreme Learning Machine Using Bacterial Foraging Algorithm ». Journal of Korean Institute of Intelligent Systems 17, no 6 (25 décembre 2007) : 807–12. http://dx.doi.org/10.5391/jkiis.2007.17.6.807.
Texte intégralNiu, Ben, Hong Wang, Jingwen Wang et Lijing Tan. « Multi-objective bacterial foraging optimization ». Neurocomputing 116 (septembre 2013) : 336–45. http://dx.doi.org/10.1016/j.neucom.2012.01.044.
Texte intégralWei, Zhong-hua, Xia Zhao, Ke-wen Wang et 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.
Texte intégralThèses sur le sujet "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.
Texte intégralTang, 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.
Texte intégralNasir, 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/.
Texte intégralSupriyono, 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/.
Texte intégralLadevèze, 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.
Texte intégralThe 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 et 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.
Texte intégralLee, Kuo-Wei, et 李國維. « Improved Bacterial Foraging Optimization ». Thesis, 2013. http://ndltd.ncl.edu.tw/handle/22851452298832117486.
Texte intégral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, et 林冠喻. « Bacterial foraging for watermarkings applications ». Thesis, 2009. http://ndltd.ncl.edu.tw/handle/66p8px.
Texte intégral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.
Texte intégralCheng, Hsiu-Tzu, et 鄭秀姿. « Bacterial Foraging Optimization for Portfolio Optimizations ». Thesis, 2012. http://ndltd.ncl.edu.tw/handle/79562681397598793645.
Texte intégral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.
Livres sur le sujet "BACTERIAL FORAGING"
Stephenson, Steven. Secretive Slime Moulds. CSIRO Publishing, 2021. http://dx.doi.org/10.1071/9781486314140.
Texte intégralChapitres de livres sur le sujet "BACTERIAL FORAGING"
Du, Ke-Lin, et M. N. S. Swamy. « Bacterial Foraging Algorithm ». Dans Search and Optimization by Metaheuristics, 217–25. Cham : Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-41192-7_13.
Texte intégralBrabazon, Anthony, Michael O’Neill et Seán McGarraghy. « Bacterial Foraging Algorithms ». Dans Natural Computing Algorithms, 187–99. Berlin, Heidelberg : Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-43631-8_11.
Texte intégralParashar, Sonam, Nand K. Meena, Jin Yang et Neeraj Kanwar. « Bacterial Foraging Optimization ». Dans Swarm Intelligence Algorithms, 31–42. First edition. | Boca Raton : Taylor and Francis, 2020. : CRC Press, 2020. http://dx.doi.org/10.1201/9780429422614-3.
Texte intégralIacca, Giovanni, Ferrante Neri et Ernesto Mininno. « Compact Bacterial Foraging Optimization ». Dans Swarm and Evolutionary Computation, 84–92. Berlin, Heidelberg : Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-29353-5_10.
Texte intégralPattnaik, S. S., K. M. Bakwad, S. Devi, B. K. Panigrahi et Sanjoy Das. « Parallel Bacterial Foraging Optimization ». Dans Adaptation, Learning, and Optimization, 487–502. Berlin, Heidelberg : Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-17390-5_21.
Texte intégralAgrawal, Vivek, Harish Sharma et Jagdish Chand Bansal. « Bacterial Foraging Optimization : A Survey ». Dans Advances in Intelligent and Soft Computing, 227–42. India : Springer India, 2012. http://dx.doi.org/10.1007/978-81-322-0487-9_23.
Texte intégralBrabazon, Anthony, et Seán McGarraghy. « Bacterial and Viral Foraging Algorithms ». Dans Natural Computing Series, 267–95. Cham : Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-59156-8_14.
Texte intégralLiu, Wei, Yunlong Zhu, Ben Niu et Hanning Chen. « Optimization Based on Bacterial Colony Foraging ». Dans 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.
Texte intégralChen, Hanning, Yunlong Zhu, Kunyuan Hu, Xiaoxian He et Ben Niu. « Cooperative Approaches to Bacterial Foraging Optimization ». Dans 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.
Texte intégralKanwar, Neeraj, Nand K. Meena, Jin Yang et Sonam Parashar. « Modified Bacterial Foraging Optimization and Application ». Dans Swarm Intelligence Algorithms, 29–41. First edition. | Boca Raton : Taylor and Francis, 2020. : CRC Press, 2020. http://dx.doi.org/10.1201/9780429422607-3.
Texte intégralActes de conférences sur le sujet "BACTERIAL FORAGING"
Yichuan Shao et Hanning Chen. « Cooperative Bacterial Foraging Optimization ». Dans 2009 International Conference on Future BioMedical Information Engineering (FBIE). IEEE, 2009. http://dx.doi.org/10.1109/fbie.2009.5405806.
Texte intégralChen, Yanhai, et Weixing Lin. « An improved bacterial foraging optimization ». Dans 2009 IEEE International Conference on Robotics and Biomimetics (ROBIO). IEEE, 2009. http://dx.doi.org/10.1109/robio.2009.5420524.
Texte intégralLi, Fei, Yuting Zhang, Jiulong Wu et Haibo Li. « Quantum bacterial foraging optimization algorithm ». Dans 2014 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2014. http://dx.doi.org/10.1109/cec.2014.6900230.
Texte intégralKasaiezadeh, Alireza, Amir Khajepour et Steven L. Waslander. « Spiral Bacterial Foraging Optimization method ». Dans 2010 American Control Conference (ACC 2010). IEEE, 2010. http://dx.doi.org/10.1109/acc.2010.5530897.
Texte intégralShao, Yichuan, et Hanning Chen. « A novel cooperative bacterial foraging algorithm ». Dans 2009 Fourth International Conference on Bio-Inspired Computing (BIC-TA). IEEE, 2009. http://dx.doi.org/10.1109/bicta.2009.5338157.
Texte intégralRashtchi, Vahid, Akbar Bayat et Hesan Vahedi. « Adaptive step length bacterial foraging algorithm ». Dans 2009 IEEE International Conference on Intelligent Computing and Intelligent Systems (ICIS 2009). IEEE, 2009. http://dx.doi.org/10.1109/icicisys.2009.5357834.
Texte intégralShao, Yichuan, et Hanning Chen. « The Optimization of Cooperative Bacterial Foraging ». Dans 2009 WRI World Congress on Software Engineering. IEEE, 2009. http://dx.doi.org/10.1109/wcse.2009.195.
Texte intégralSharifkhani, Fatemeh, et Mohammad Reza Pakravan. « Bacterial foraging search in unstructured P2P networks ». Dans 2014 IEEE 27th Canadian Conference on Electrical and Computer Engineering (CCECE). IEEE, 2014. http://dx.doi.org/10.1109/ccece.2014.6900982.
Texte intégralBakwad, K. M., S. S. Pattnaik, B. S. Sohi, S. Devi, B. K. Panigrahi, Sanjoy Das et M. R. Lohokare. « Hybrid Bacterial Foraging with parameter free PSO ». Dans 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC). IEEE, 2009. http://dx.doi.org/10.1109/nabic.2009.5393867.
Texte intégralHanning Chen, Yunlong Zhu et Kunyuan Hu. « Cooperative Bacterial Foraging algorithm for global Optimization ». Dans 2009 Chinese Control and Decision Conference (CCDC). IEEE, 2009. http://dx.doi.org/10.1109/ccdc.2009.5191509.
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