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Автор: Grafiati
Опубліковано: 11 вересня 2023

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Статті в журналах з теми "BACTERIAL FORAGING"

1

Passino, Kevin M. "Bacterial Foraging Optimization." International Journal of Swarm Intelligence Research 1, no. 1 (January 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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2

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 desirable. Seven different benchmark functions are considered for performance evaluation. Also, comparison with the results of previous methods is presented to reveal the effectiveness of the proposed algorithm.
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3

Chen, Hanning, Yunlong Zhu, and Kunyuan Hu. "Adaptive Bacterial Foraging Optimization." Abstract and Applied Analysis 2011 (2011): 1–27. http://dx.doi.org/10.1155/2011/108269.

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Анотація:
Bacterial Foraging Optimization (BFO) is a recently developed nature-inspired optimization algorithm, which is based on the foraging behavior ofE. colibacteria. Up to now, BFO has been applied successfully to some engineering problems due to its simplicity and ease of implementation. However, BFO possesses a poor convergence behavior over complex optimization problems as compared to other nature-inspired optimization techniques. This paper first analyzes how the run-length unit parameter of BFO controls the exploration of the whole search space and the exploitation of the promising areas. Then it presents a variation on the original BFO, called the adaptive bacterial foraging optimization (ABFO), employing the adaptive foraging strategies to improve the performance of the original BFO. This improvement is achieved by enabling the bacterial foraging algorithm to adjust the run-length unit parameter dynamically during algorithm execution in order to balance the exploration/exploitation tradeoff. The experiments compare the performance of two versions of ABFO with the original BFO, the standard particle swarm optimization (PSO) and a real-coded genetic algorithm (GA) on four widely-used benchmark functions. The proposed ABFO shows a marked improvement in performance over the original BFO and appears to be comparable with the PSO and GA.
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4

Chen, Hanning, Ben Niu, Lianbo Ma, Weixing Su, and Yunlong Zhu. "Bacterial colony foraging optimization." Neurocomputing 137 (August 2014): 268–84. http://dx.doi.org/10.1016/j.neucom.2013.04.054.

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5

Chen, Hanning, Yunlong Zhu, and Kunyuan Hu. "Cooperative Bacterial Foraging Optimization." Discrete Dynamics in Nature and Society 2009 (2009): 1–17. http://dx.doi.org/10.1155/2009/815247.

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Анотація:
Bacterial Foraging Optimization (BFO) is a novel optimization algorithm based on the social foraging behavior ofE. colibacteria. This paper presents a variation on the original BFO algorithm, namely, the Cooperative Bacterial Foraging Optimization (CBFO), which significantly improve the original BFO in solving complex optimization problems. This significant improvement is achieved by applying two cooperative approaches to the original BFO, namely, the serial heterogeneous cooperation on the implicit space decomposition level and the serial heterogeneous cooperation on the hybrid space decomposition level. The experiments compare the performance of two CBFO variants with the original BFO, the standard PSO and a real-coded GA on four widely used benchmark functions. The new method shows a marked improvement in performance over the original BFO and appears to be comparable with the PSO and GA.
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6

Lenin, Kanagasabai. "Diminution of factual power loss by enhanced bacterial foraging optimization algorithm." International Journal of Applied Power Engineering (IJAPE) 9, no. 3 (December 1, 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 <em>Escherichia coli</em> 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 the population and progress the global search capability. The quantum rotation gate is utilized for chemo taxis to modernize the state of chromosome projected EBFO algorithm has been tested in standard IEEE 14,300 bus test system and simulation results show the projected algorithm reduced the real power loss extensively.</div>
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7

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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Анотація:
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, density perception mechanism. Areas of future emphasis and direction in development were also pointed out.
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8

Cho, Jae-Hoon, Dae-Jong Lee, and Myung-Geun Chun. "Parameter Optimization of Extreme Learning Machine Using Bacterial Foraging Algorithm." Journal of Korean Institute of Intelligent Systems 17, no. 6 (December 25, 2007): 807–12. http://dx.doi.org/10.5391/jkiis.2007.17.6.807.

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9

Niu, Ben, Hong Wang, Jingwen Wang, and Lijing Tan. "Multi-objective bacterial foraging optimization." Neurocomputing 116 (September 2013): 336–45. http://dx.doi.org/10.1016/j.neucom.2012.01.044.

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10

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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Анотація:
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 greatly accelerate convergence speed, improve searching precision, and strengthen robustness. The final result showed that ABFA was most feasible in optimizing variables.
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Дисертації з теми "BACTERIAL FORAGING"

1

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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2

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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Анотація:
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 a number of strategies to search for food sources with a very efficient manner. This thesis explores the potential of understanding of a biological system by modelling the' underlying mechanisms of bacterial foraging patterns and investigates their applicability to engineering optimisation problems. :rvlodelling plays a significant role in understanding bacterial foraging behaviour. Mathematical expressions and experimental observations have been utilised to represent biological systems. However, difficulties arise from the lack of systematic analysis of the developed models and experimental data. Recently, Systems Biology has be,en proposed to overcome this barrier, with the effort from a number of research fields, including Computer Science and Systems Engineering. At the same time, Individual-based Modelling (IbM) has emerged to assist the modelling of a biological system. Starting from a basic model of foraging and proliferation of bacteria, the development of an IbM of bacterial systems of this thesis focuses on a Varying Environment BActerial Model (VEBAM). Simulation results demonstrate that VEBAM is able to provide a new perspective to describe interactions between the bacteria and their food environment. Knowledge transfer from modelling of bacterial systems to solving optimisation problems also composes an important part of this study. Three Bacteriainspired Algorithms (BalAs) have been developed to bridge the gap between modelling and optimisation. These algorithms make use of the. self-adaptability of individual bacteria in the group searching activities described in VEBAM, while incorporating a variety of additional features. In particular, the new bacterial foraging algorithm with varying population (BFAVP) takes bacterial metabolism into consideration. The group behaviour in Particle Swarm Optimiser (PSO) is adopted in Bacterial Swarming Algorithm (BSA) to enhance searching ability. To reduce computational time, another algorithm, a Paired-bacteria Optimiser (PBO) is designed specifically to further explore the capability of BalAs. Simulation studies undertaken against a wide range of benchmark functions demonstrate a satisfying performance with a reasonable convergence speed. To explore the potential of bacterial searching ability in optimisation undertaken in a varying environment, a dynamic bacterial foraging algorithm (DBFA) is developed with the aim of solving optimisation in a time-varying environment. In this case, the balance between its convergence and exploration abilities is investigated, and a new scheme of reproduction is developed which is different froin that used for static optimisation problems. The simulation studies have been undertaken and the results show that the DBFA can adapt to various environmental changes rapidly. One of the challenging large-scale complex optimisation problems is optimal power flow (OPF) computation. BFAVP shows its advantage in solving this problem. A simulation study has been performed on an IEEE 30-bus system, and the results are compared with PSO algorithm and Fast Evolutionary Programming (FEP) algorithm, respectively. Furthermore, the OPF problem is extended for consideration in varying environments, on which DBFA has been evaluated. A simulation study has been undertaken on both the IEEE 30-bus system and the IEEE l1S-bus system, in compariso~ with a number of existing algorithms. The dynamic OPF problem has been tackled for the first time in the area of power systems, and the results obtained are encouraging, with a significant amount of energy could possibly being saved. Another application of BaIA in this thesis is concerned with estimating optimal parameters of a power transformer winding model using BSA. Compared with Genetic Algorithm (GA), BSA is able to obtain a more satisfying result in modelling the transformer winding, which could not be achieved using a theoretical transfer function model.
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3

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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4

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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Анотація:
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 computing techniques and test their performances in modelling and control of dynamic systems. Modification of BFA focuses for improving its convergence in terms of speed and accuracy. The performances of modified BFAs are assessed in comparison to that of original BFA. In the original BFA, in this thesis referred as standard BFA (SBFA), bacteria use constant chemotactic step size to head to global optimum location. Very small chemotactic step size around global optimum location will assure bacteria find the global optimum point. However, a large number of steps is needed for the whole optimisation process. Moreover, there is potential for the algorithm to be trapped in one of the local optima. On the contrary, big chemotactic step size will assure bacteria have faster convergence speed but the literature shows that it results oscillation around global optimum point and the algorithm potentially missing the global optimum point and leading to oscillation around the point. Thus SBFA can be improved by applying adaptable chemotactic step size which could change: very large when bacteria are in locations far away from the global optimum location, to speed up the convergence, and very small when bacteria are in the locations near the global optimum so that bacteria able to find global optimum point without oscillation. Here, four novel adaptation schemes allowing the chemotactic step size to depending on the cost function value have been proposed. The adaptation schemes are developed based on linear, quadratic and exponential functions as well as fuzzy logic (FL). Then, the proposed BFAs with adaptable chemotactic step size, i.e. linearly adaptable BFA (LABFA), quadratic adaptable BFA (QABFA), exponentially adaptable BFA (EABFA) and fuzzy adaptable chemotactic step size (FABFA), are validated by using them to find global minimum point of seven well-known benchmark functions commonly used in development of optimisation techniques development. The results show that all ABFAs achieve better accuracy and speed compared to those of SBFA. The ABFAs are then used in modelling and control of a single-link flexible manipulator system. This includes modelling (based on linear model structures, neural network (NN), and fuzzy logic (FL)), optimising joint-based collocated (JBC) proportional-derivative (PD) control, and optimising both PD and proportional integral derivative (PID) control of end-point acceleration feedback for vibration reduction of a single-link flexible manipulator. The results show that ABFAs outperform SBFA in terms of convergence speed and accuracy. Since all SBFA and ABFAs use the same general parameters and bacteria are initially placed randomly across the nutrient media (cost function), the superiority better performance of ABFAs are attributed to the proposed adaptable chemotactic step size.
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5

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 et en complexe avec sesligands. Un nouveau procédé de synthèse par phosphorolyse inverse d'oligosaccharidesmannosylés à haute valeur ajoutée, a aussi été développé. Enfin, la caractérisationfonctionnelle de la protéine BACOVA_03624 issue de Bacteroides ovatus ATCC 8483, unebactérie intestinale hautement prévalente, a révélé que la famille GH130 comprend à la foisdes glycoside-hydrolases et des glycoside-phosphorylases capables de dégrader lesmannosides et les galactosides, et de les synthétiser par phosphorolyse inverse et/outransglycosylation. L’ensemble de ces résultats, ainsi que l’identification d’inhibiteurs desenzymes de la famille GH130, ouvrent de nouvelles perspectives pour l'étude et le contrôledes interactions microbiote-hôte
The 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
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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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7

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

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Анотація:
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 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.
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Lin, Guan-Yu, and 林冠喻. "Bacterial foraging for watermarkings applications." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/66p8px.

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Анотація:
碩士
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.
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9

Allemneny, Raghuveer. "Bacterial Foraging Based Channel Equalizers." Thesis, 2006. http://ethesis.nitrkl.ac.in/24/1/raghuveer.pdf.

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A channel equalizer is one of the most important subsystems in any digital communication receiver. It is also the subsystem that consumes maximum computation time in the receiver. Traditionally maximum-likelihood sequence estimation (MLSE) was the most popular form of equalizer. Owing to non-stationary characteristics of the communication channel MLSE receivers perform poorly. Under these circumstances ‘Maximum A-posteriori Probability (MAP)’ receivers also called Bayesian receivers perform better. Natural selection tends to eliminate animals with poor “foraging strategies” and favor the propagation of genes of those animals that have successful foraging strategies since they are more likely to enjoy reproductive success. After many generations, poor foraging strategies are either eliminated or shaped into good ones (redesigned). Logically, such evolutionary principles have led scientists in the field of “foraging theory” to hypothesize that it is appropriate to model the activity of foraging as an optimization process. This thesis presents an investigation on design of bacterial foraging based channel equalizer for digital communication. Extensive simulation studies shows that the performance of the proposed receiver is close to optimal receiver for variety of channel conditions. The proposed receiver also provides near optimal performance when channel suffers from nonlinearities.
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Cheng, Hsiu-Tzu, and 鄭秀姿. "Bacterial Foraging Optimization for Portfolio Optimizations." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/79562681397598793645.

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Анотація:
碩士
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.
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Книги з теми "BACTERIAL FORAGING"

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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! Their small size (usually only a few millimetres tall) and fleeting fruiting phase mean that these organisms, although ubiquitous and sometimes abundant, are overlooked by most people. However, recent research by a few dedicated individuals has shown that Australia has a very diverse myxomycete biota with more than 330 species, the largest number known for any region of the Southern Hemisphere. This comprehensive monograph provides keys, descriptions and information on the known distribution for all of these species in addition to containing introductory material relating to their biology and ecology. Many species are illustrated, showing the diversity of their fruiting bodies, and greatly facilitating their identification. This book will give naturalists a new insight into an often overlooked group of organisms in addition to providing an incentive to search for the many species which have undoubtedly thus far escaped notice.
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Частини книг з теми "BACTERIAL FORAGING"

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Du, Ke-Lin, and M. N. S. Swamy. "Bacterial Foraging Algorithm." In Search and Optimization by Metaheuristics, 217–25. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-41192-7_13.

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Brabazon, Anthony, Michael O’Neill, and Seán McGarraghy. "Bacterial Foraging Algorithms." In Natural Computing Algorithms, 187–99. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-43631-8_11.

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Parashar, Sonam, Nand K. Meena, Jin Yang, and Neeraj Kanwar. "Bacterial Foraging Optimization." In Swarm Intelligence Algorithms, 31–42. First edition. | Boca Raton : Taylor and Francis, 2020.: CRC Press, 2020. http://dx.doi.org/10.1201/9780429422614-3.

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4

Iacca, Giovanni, Ferrante Neri, and Ernesto Mininno. "Compact Bacterial Foraging Optimization." In Swarm and Evolutionary Computation, 84–92. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-29353-5_10.

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Pattnaik, S. S., K. M. Bakwad, S. Devi, B. K. Panigrahi, and Sanjoy Das. "Parallel Bacterial Foraging Optimization." In Adaptation, Learning, and Optimization, 487–502. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-17390-5_21.

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Agrawal, Vivek, Harish Sharma, and Jagdish Chand Bansal. "Bacterial Foraging Optimization: A Survey." In Advances in Intelligent and Soft Computing, 227–42. India: Springer India, 2012. http://dx.doi.org/10.1007/978-81-322-0487-9_23.

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Brabazon, Anthony, and Seán McGarraghy. "Bacterial and Viral Foraging Algorithms." In Natural Computing Series, 267–95. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-59156-8_14.

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Liu, Wei, Yunlong Zhu, Ben Niu, and Hanning Chen. "Optimization Based on Bacterial Colony Foraging." In 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.

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Chen, Hanning, Yunlong Zhu, Kunyuan Hu, Xiaoxian He, and Ben Niu. "Cooperative Approaches to Bacterial Foraging Optimization." In 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.

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10

Kanwar, Neeraj, Nand K. Meena, Jin Yang, and Sonam Parashar. "Modified Bacterial Foraging Optimization and Application." In Swarm Intelligence Algorithms, 29–41. First edition. | Boca Raton : Taylor and Francis, 2020.: CRC Press, 2020. http://dx.doi.org/10.1201/9780429422607-3.

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Тези доповідей конференцій з теми "BACTERIAL FORAGING"

1

Yichuan Shao and Hanning Chen. "Cooperative Bacterial Foraging Optimization." In 2009 International Conference on Future BioMedical Information Engineering (FBIE). IEEE, 2009. http://dx.doi.org/10.1109/fbie.2009.5405806.

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2

Chen, Yanhai, and Weixing Lin. "An improved bacterial foraging optimization." In 2009 IEEE International Conference on Robotics and Biomimetics (ROBIO). IEEE, 2009. http://dx.doi.org/10.1109/robio.2009.5420524.

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3

Li, Fei, Yuting Zhang, Jiulong Wu, and Haibo Li. "Quantum bacterial foraging optimization algorithm." In 2014 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2014. http://dx.doi.org/10.1109/cec.2014.6900230.

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4

Kasaiezadeh, Alireza, Amir Khajepour, and Steven L. Waslander. "Spiral Bacterial Foraging Optimization method." In 2010 American Control Conference (ACC 2010). IEEE, 2010. http://dx.doi.org/10.1109/acc.2010.5530897.

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5

Shao, Yichuan, and Hanning Chen. "A novel cooperative bacterial foraging algorithm." In 2009 Fourth International Conference on Bio-Inspired Computing (BIC-TA). IEEE, 2009. http://dx.doi.org/10.1109/bicta.2009.5338157.

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6

Rashtchi, Vahid, Akbar Bayat, and Hesan Vahedi. "Adaptive step length bacterial foraging algorithm." In 2009 IEEE International Conference on Intelligent Computing and Intelligent Systems (ICIS 2009). IEEE, 2009. http://dx.doi.org/10.1109/icicisys.2009.5357834.

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7

Shao, Yichuan, and Hanning Chen. "The Optimization of Cooperative Bacterial Foraging." In 2009 WRI World Congress on Software Engineering. IEEE, 2009. http://dx.doi.org/10.1109/wcse.2009.195.

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8

Sharifkhani, Fatemeh, and Mohammad Reza Pakravan. "Bacterial foraging search in unstructured P2P networks." In 2014 IEEE 27th Canadian Conference on Electrical and Computer Engineering (CCECE). IEEE, 2014. http://dx.doi.org/10.1109/ccece.2014.6900982.

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9

Bakwad, K. M., S. S. Pattnaik, B. S. Sohi, S. Devi, B. K. Panigrahi, Sanjoy Das, and M. R. Lohokare. "Hybrid Bacterial Foraging with parameter free PSO." In 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC). IEEE, 2009. http://dx.doi.org/10.1109/nabic.2009.5393867.

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10

Hanning Chen, Yunlong Zhu, and Kunyuan Hu. "Cooperative Bacterial Foraging algorithm for global Optimization." In 2009 Chinese Control and Decision Conference (CCDC). IEEE, 2009. http://dx.doi.org/10.1109/ccdc.2009.5191509.

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