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Littérature scientifique sur le sujet « BACTERIA FORAGING »

Auteur : Grafiati
Publié le 11 septembre 2023
Mis à jour le 13 juin 2025

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Articles de revues sur le sujet "BACTERIA FORAGING"

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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 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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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 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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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 <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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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 directions in the chemotactic steps. Besides, adaptive step lengths are employed in chemotaxis. The new algorithm is named BFOLS and it is tested on a set of benchmark functions with dimensions of 2 and 20. Canonical BFO, PSO, and GA algorithms are employed for comparison. Experiment results and statistic analysis show that the BFOLS algorithm offers significant improvements than original BFO algorithm. Particulary with dimension of 20, it has the best performance among the four algorithms.
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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, density perception mechanism. Areas of future emphasis and direction in development were also pointed out.
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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 attached and planktonic lifestyles, departing particles when their nutrient concentration is more than hundredfold higher than background. In accordance with predictions from patch use theory, single-cell tracking reveals that bacteria spend less time on nutrient-poor particles and on particles within environments that are rich or in which the travel time between particles is smaller, indicating that bacteria tune the nutrient concentration at detachment to increase their fitness. A mathematical model shows that the observed behavioral switching between exploitation and dispersal is consistent with foraging optimality under limited information, namely, the ability to assess the harvest rate of nutrients leaking from particles by molecular diffusion. This work demonstrates how fundamental principles in behavioral ecology traditionally applied to animals can hold right down to the scale of microorganisms and highlights the exquisite adaptations of marine bacterial foraging. The present study thus provides a blueprint for a mechanistic understanding of bacterial uptake of dissolved organic matter and bacterial production in the ocean—processes that are fundamental to the global carbon cycle.
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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 relationships based on the index of iteration, index of bacteria, and fitness cost are adopted which can dynamically vary the step size of bacteria movement. The proposed algorithms are tested with several unimodal and multimodal benchmark functions in comparison with the original BFA. Moreover, the application of the proposed algorithms in modelling of a twin rotor system is presented. The results show that the proposed algorithms outperform the predecessor algorithm in all test functions and acquire better model for the twin rotor system.
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Mangaraj, Biswa Binayak, Manas Ranjan Jena, and Saumendra Kumar Mohanty. "Bacteria Foraging Algorithm in Antenna Design." Applied Computational Intelligence and Soft Computing 2016 (2016): 1–11. http://dx.doi.org/10.1155/2016/5983469.

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A simple design procedure to realize an optimum antenna using bacteria foraging algorithm (BFA) is proposed in this paper. The first antenna considered is imaginary. This antenna is optimized using the BFA along with a suitable fitness function formulated by considering some performance parameters and their best values. To justify the optimum design approach, one 12-element Yagi-Uda antenna is considered for an experiment. The optimized result of this antenna obtained using the optimization algorithm is compared with nonoptimized (conventional) result of the same antenna to appreciate the importance of optimization.
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Ackermann, Michael, Paul Prill, and Liliane Ruess. "Disentangling nematode-bacteria interactions using a modular soil model system and biochemical markers." Nematology 18, no. 4 (2016): 403–15. http://dx.doi.org/10.1163/15685411-00002965.

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Interactions between bacteria and nematode grazers are an important component of soil food webs yet, due to the cryptic habitat, they are almost exclusively investigated in artificial agar substrate. Transport, food choice and foraging experiments were performed in a modular microcosm system with the nematode Acrobeloides buetschlii and bacterial diets (Escherichia coli, Pseudomonas putida and Bacillus subtilis) in gamma-irradiated soil. Bacterial biomass was assessed by soil phospholipid fatty acids (PLFAs). Continuous random foraging of nematodes was affected by soil type. Food choice experiments revealed diet switch and time lag preference responses, suggesting that nematode population fluctuations are driven by multiple factors such as bacterial attractants, defence strategies or food quality. Application of PLFA markers revealed a strong nematode predation pressure, as biomass in P. putida declined by 50%, whereas no transport of bacteria through soil was indicated. Overall, semi-natural experimental systems are an essential prerequisite to gain a realistic picture in microbial-microfaunal interactions.
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Thèses sur le sujet "BACTERIA FORAGING"

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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 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<br>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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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 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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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 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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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 edge pixels in the neighborhood of bacteria. Next in the Elimination and Dispersal step if a bacterium found itself low on nutrients than it will be eliminated from its current location and dispersed to some other location. Now if we trace all the edge pixels, given by the movement of bacteria than we will get an image highlighted with all the associated edges. By using the proposed technique, a marked visible improvement in the important edges is observed on various test images over common edge detectors.
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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 implements a relative pixel similarity value algorithm.The similarity between two pixels is calculated using the weighted participation of each fuzzy rule. Low similarity between the pixels represents the probability of a pixel to be an edge pixel. The bacteria moves over the low similarity region, thus maximizing the edge content while minimizing the presence of non-edge content in the movement path. Directional Pixel Similarity is used to locate the similar pixel to the edge pixel and thus the movement of bacteria is decided.Bacteria with sufficient nutrients are reproduced, i.e., at the intersection of more than one edge a bacterium will split into the number of edges. If a bacterium found itself low on nutrients than it will be eliminated from its current location and dispersed to some other location. The path traced by the bacteria is the edge map. Thus with proposed method, edge pixels in a color image are detected simultaneously without any complex calculations such as gradient, Laplace and statistical calculations.
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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) can work together to accomplish the designated tasks that are difficult or impossible for a single robot to accomplish. Here Particle Swarm Optimization (PSO) and Bacteria Foraging Algorithm (BFA) have been used for coordination and path-planning of the robots. PSO is used for global path planning of all the robotic agents in the workspace. The calculated paths of the robots are further optimized using a localised BFA optimization technique. The problem considered in this project is coordination of multiple mobile agents in a predefined environment using multiple small mobile robots. This work demonstrates the use of a combinatorial PSO algorithm with a novel local search enhanced by the use of BFA to help in efficient path planning limiting the chances of PSO getting trapped in the local optima. The approach has been simulated on a graphical interface.
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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<br>大同大學<br>資訊經營學系(所)<br>101<br>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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Livres sur le sujet "BACTERIA 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!&#x0D; 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.&#x0D; 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.&#x0D; 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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Chapitres de livres sur le sujet "BACTERIA FORAGING"

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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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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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Dhondiyal, Shiv Ashish, Manisha Aeri, Paras Gulati, Deepak Singh Rana, and Sumeshwar Singh. "Energy Optimization in WSN Using Evolutionary Bacteria Foraging Optimization Method." In Advances in Intelligent Systems and Computing. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-8443-5_41.

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Actes de conférences sur le sujet "BACTERIA FORAGING"

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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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Pal, P. S., A. Ghosh, S. Choudhury, et al. "Identification of Hammerstein model using bacteria foraging optimization algorithm." In 2016 International Conference on Communication and Signal Processing (ICCSP). IEEE, 2016. http://dx.doi.org/10.1109/iccsp.2016.7754432.

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