Статті в журналах: "BACTERIA 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

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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4

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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5

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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6

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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7

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 (September 24, 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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8

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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9

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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10

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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11

Ai, Ying, Yi Xin Su, Dan Hong Zhang, and Yao Peng. "Improved Chaotic Bacteria Foraging Optimization Algorithm Particle." Applied Mechanics and Materials 651-653 (September 2014): 2322–25. http://dx.doi.org/10.4028/www.scientific.net/amm.651-653.2322.

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. Aiming at the defects of weak global search ability and slow convergence speed in bacteria foraging algorithm optimization, this paper proposed an improved chaotic bacteria foraging optimization algorithm which has introduced the chaotic thoughts, improved the update operation of fitness and migration operation in optimization process. Using Logistic chaotic map initializes the bacteria population, so as to improve the convergence speed of the algorithm; Then adjust quorum sensing mechanism to optimize the chemotactic direction of the bacteria, and operate on perished bacteria with chaos disturbance in migration operation, so as to improve the global optimization ability of the algorithm. Simulation of two standard test functions show that the proposed algorithm has higher convergence speed and precision.

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12

Jiang, Jianguo, Jiawei Zhou, Yingchun Zheng, and Runsheng Zhou. "A double flora bacteria foraging optimization algorithm." Journal of Shenzhen University Science and Engineering 31, no. 1 (2014): 43. http://dx.doi.org/10.3724/sp.j.1249.2014.01043.

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13

Abdul Hameed, K., and S. Palani. "Robust design of power system stabilizer using bacterial foraging algorithm." Archives of Electrical Engineering 62, no. 1 (March 1, 2013): 141–52. http://dx.doi.org/10.2478/aee-2013-0010.

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Abstract In this paper, a novel bacterial foraging algorithm (BFA) based approach for robust and optimal design of PID controller connected to power system stabilizer (PSS) is proposed for damping low frequency power oscillations of a single machine infinite bus bar (SMIB) power system. This paper attempts to optimize three parameters (Kp, Ki, Kd) of PID-PSS based on foraging behaviour of Escherichia coli bacteria in human intestine. The problem of robustly selecting the parameters of the power system stabilizer is converted to an optimization problem which is solved by a bacterial foraging algorithm with a carefully selected objective function. The eigenvalue analysis and the simulation results obtained for internal and external disturbances for a wide range of operating conditions show the effectiveness and robustness of the proposed BFAPSS. Further, the time domain simulation results when compared with those obtained using conventional PSS and Genetic Algorithm (GA) based PSS show the superiority of the proposed design.

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14

LIU, Xiao-long, and Kui-ling ZHAO. "Bacteria foraging optimization algorithm based on immune algorithm." Journal of Computer Applications 32, no. 3 (April 1, 2013): 634–37. http://dx.doi.org/10.3724/sp.j.1087.2012.00634.

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15

Kaur, Rajinder, Akshay Girdhar, and Surbhi Gupta. "Color Image Quantization based on Bacteria Foraging Optimization." International Journal of Computer Applications 25, no. 7 (July 31, 2011): 33–42. http://dx.doi.org/10.5120/3042-4130.

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16

Wu, Shenli, Sun'an Wang, and Xiaohu Li. "A new dynamic bacterial foraging optimization and its application on model reduction." International Journal of Modeling, Simulation, and Scientific Computing 06, no. 02 (May 29, 2015): 1550018. http://dx.doi.org/10.1142/s179396231550018x.

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Inspired by the foraging behavior of E. coli bacteria, bacterial foraging optimization (BFO) has emerged as a powerful technique for solving optimization problems. However, BFO shows poor performance on complex and high-dimensional optimization problems. In order to improve the performance of BFO, a new dynamic bacterial foraging optimization based on clonal selection (DBFO-CS) is proposed. Instead of fixed step size in the chemotaxis operator, a new piecewise strategy adjusts the step size dynamically by regulatory factor in order to balance between exploration and exploitation during optimization process, which can improve convergence speed. Furthermore, reproduction operator based on clonal selection can add excellent genes to bacterial populations in order to improve bacterial natural selection and help good individuals to be protected, which can enhance convergence precision. Then, a set of benchmark functions have been used to test the proposed algorithm. The results show that DBFO-CS offers significant improvements than BFO on convergence, accuracy and robustness. A complex optimization problem of model reduction on stable and unstable linear systems based on DBFO-CS is presented. Results show that the proposed algorithm can efficiently approximate the systems.

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17

Yao, Yao, Jiankang Ren, Ran Bi, and Qian Liu. "Bacterial Foraging Algorithm Based on Activity of Bacteria for DNA Computing Sequence Design." IEEE Access 9 (2021): 2110–24. http://dx.doi.org/10.1109/access.2020.3047469.

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18

Agarwal, Kavita, and Amanda L. Lewis. "Vaginal sialoglycan foraging by Gardnerella vaginalis: mucus barriers as a meal for unwelcome guests?" Glycobiology 31, no. 6 (April 5, 2021): 667–80. http://dx.doi.org/10.1093/glycob/cwab024.

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Abstract Bacterial vaginosis (BV) is a condition of the vaginal microbiome in which there are few lactobacilli and abundant anaerobic bacteria. Members of the genus Gardnerella are often one of the most abundant bacteria in BV. BV is associated with a wide variety of poor health outcomes for women. It has been recognized since the 1980s that women with BV have detectable and sometimes markedly elevated levels of sialidase activity in vaginal fluids and that bacteria associated with this condition produce this activity in culture. Mounting evidence collected using diverse methodologies points to the conclusion that BV is associated with a reduction in intact sialoglycans in cervicovaginal secretions. Here we review evidence for the contributions of vaginal bacteria, especially Gardnerella, in the processes of mucosal sialoglycan degradation, uptake, metabolism and depletion. Our understanding of the impacts of vaginal sialoglycan degradation is still limited. However, the potential implications of sialic acid depletion are discussed in light of our current understanding of the roles played by sialoglycans in vaginal physiology.

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19

Ye, Fu-Lan, Chou-Yuan Lee, Zne-Jung Lee, Jian-Qiong Huang, and Jih-Fu Tu. "Incorporating Particle Swarm Optimization into Improved Bacterial Foraging Optimization Algorithm Applied to Classify Imbalanced Data." Symmetry 12, no. 2 (February 3, 2020): 229. http://dx.doi.org/10.3390/sym12020229.

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In this paper, particle swarm optimization is incorporated into an improved bacterial foraging optimization algorithm, which is applied to classifying imbalanced data to solve the problem of how original bacterial foraging optimization easily falls into local optimization. In this study, the borderline synthetic minority oversampling technique (Borderline-SMOTE) and Tomek link are used to pre-process imbalanced data. Then, the proposed algorithm is used to classify the imbalanced data. In the proposed algorithm, firstly, the chemotaxis process is improved. The particle swarm optimization (PSO) algorithm is used to search first and then treat the result as bacteria, improving the global searching ability of bacterial foraging optimization (BFO). Secondly, the reproduction operation is improved and the selection standard of survival of the cost is improved. Finally, we improve elimination and dispersal operation, and the population evolution factor is introduced to prevent the population from stagnating and falling into a local optimum. In this paper, three data sets are used to test the performance of the proposed algorithm. The simulation results show that the classification accuracy of the proposed algorithm is better than the existing approaches.

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20

Ding, Siyu Serena, Maksym Romenskyy, Karen S. Sarkisyan, and Andre E. X. Brown. "Measuring Caenorhabditis elegans Spatial Foraging and Food Intake Using Bioluminescent Bacteria." Genetics 214, no. 3 (January 7, 2020): 577–87. http://dx.doi.org/10.1534/genetics.119.302804.

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For most animals, feeding includes two behaviors: foraging to find a food patch and food intake once a patch is found. The nematode Caenorhabditis elegans is a useful model for studying the genetics of both behaviors. However, most methods of measuring feeding in worms quantify either foraging behavior or food intake, but not both. Imaging the depletion of fluorescently labeled bacteria provides information on both the distribution and amount of consumption, but even after patch exhaustion a prominent background signal remains, which complicates quantification. Here, we used a bioluminescent Escherichia coli strain to quantify C. elegans feeding. With light emission tightly coupled to active metabolism, only living bacteria are capable of bioluminescence, so the signal is lost upon ingestion. We quantified the loss of bioluminescence using N2 reference worms and eat-2 mutants, and found a nearly 100-fold increase in signal-to-background ratio and lower background compared to loss of fluorescence. We also quantified feeding using aggregating npr-1 mutant worms. We found that groups of npr-1 mutants first clear bacteria from within the cluster before foraging collectively for more food; similarly, during large population swarming, only worms at the migrating front are in contact with bacteria. These results demonstrate the usefulness of bioluminescent bacteria for quantifying feeding and generating insights into the spatial pattern of food consumption.

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21

K, Mani, and Mullai A. "Generation of Addition Chain using Bacteria Foraging Optimization Algorithm." International Journal of Engineering Trends and Technology 69, no. 2 (February 25, 2021): 32–38. http://dx.doi.org/10.14445/22315381/ijett-v69i2p205.

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22

Majumder, Arindam, and Dipak Laha. "Bacteria Foraging Optimization Algorithm for Robotic Cell Scheduling Problem." Materials Today: Proceedings 4, no. 2 (2017): 2129–36. http://dx.doi.org/10.1016/j.matpr.2017.02.059.

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23

Abd-Elazim, Sahar M., and Ehab S. Ali. "Power System Stability Enhancement via Bacteria Foraging Optimization Algorithm." Arabian Journal for Science and Engineering 38, no. 3 (November 28, 2012): 599–611. http://dx.doi.org/10.1007/s13369-012-0423-y.

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24

Jain, Arvind Kumar, Suresh Chandra Srivastava, Sri Niwas Singh, and Laxmi Srivastava. "Bacteria foraging optimization based bidding strategy under transmission congestion." IEEE Systems Journal 9, no. 1 (March 2015): 141–51. http://dx.doi.org/10.1109/jsyst.2013.2258229.

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25

Saha, Suman Kumar, Rajib Kar, Durbadal Mandal, and Sakti Prasad Ghoshal. "Bacteria foraging optimisation algorithm for optimal FIR filter design." International Journal of Bio-Inspired Computation 5, no. 1 (2013): 52. http://dx.doi.org/10.1504/ijbic.2013.053039.

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26

Mishra, Sudhansu Kumar, Ganapati Panda, and Ritanjali Majhi. "Constrained portfolio asset selection using multiobjective bacteria foraging optimization." Operational Research 14, no. 1 (December 17, 2013): 113–45. http://dx.doi.org/10.1007/s12351-013-0138-1.

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Panigrahi, B. K., V. Ravikumar Pandi, Renu Sharma, Swagatam Das, and Sanjoy Das. "Multiobjective bacteria foraging algorithm for electrical load dispatch problem." Energy Conversion and Management 52, no. 2 (February 2011): 1334–42. http://dx.doi.org/10.1016/j.enconman.2010.09.031.

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Zambrano Zambrano, Dannyll Michellc, Darío Vélez, Yohanna Daza, and José Manuel Palomares. "Parametric Analysis of BFOA for Minimization Problems Using a Benchmark Function." Enfoque UTE 10, no. 3 (September 30, 2019): 67–80. http://dx.doi.org/10.29019/enfoque.v10n3.490.

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This paper presents the social foraging behavior of Escherichia coli (E. Coli) bacteria based on Bacteria Foraging Optimization algorithms (BFOA) to find optimization and distributed control values. The search strategy for E. coli is very complex to express and the dynamics of the simulated chemotaxis stage in BFOA is analyzed with the help of a simple mathematical model. The methodology starts from a detailed analysis of the parameters of bacterial swimming and tumbling (C) and the probability of elimination and dispersion (Ped), then an adaptive variant of BFOA is proposed, in which the size of the chemotherapeutic step is adjusted according to the current suitability of a virtual bacterium. To evaluate the performance of the algorithm in obtaining optimal values, the resolution was applied to one of the benchmark functions, in this case the Ackley minimization function, a comparative analysis of the BFOA is then performed. The simulation results have shown the validity of the optimal values (minimum or maximum) obtained on a specific function for real world problems, with a function belonging to the benchmark group of optimization functions.

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Liang, Dong Ying, and Wei Kun Zheng. "An Intelligent Feature Selection Method Based on the Bacterial Foraging Algorithm." Applied Mechanics and Materials 50-51 (February 2011): 304–8. http://dx.doi.org/10.4028/www.scientific.net/amm.50-51.304.

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This paper puts forward an agent genetic algorithm based on bacteria foraging strategy (BFOA-L) as the feature selection method. The algorithm introduces the bacteria foraging (BF) behavior, and integrates the neural network and link agent structure to achieve fuzzy logic reasoning, so that the weights with no definite physical meaning in traditional neural network are endowed with the physical meaning of fuzzy logic reasoning parameters. The algorithm can maintain the diversity of the agent, so as to achieve satisfactory global optimization precision. The test result shows that this algorithm has good stability, little time complexity and high recognition accuracy.

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Scheiner, Ricarda, Sina Strauß, Markus Thamm, Gerard Farré-Armengol, and Robert R. Junker. "The Bacterium Pantoea ananatis Modifies Behavioral Responses to Sugar Solutions in Honeybees." Insects 11, no. 10 (October 12, 2020): 692. http://dx.doi.org/10.3390/insects11100692.

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1. Honeybees, which are among the most important pollinators globally, do not only collect pollen and nectar during foraging but may also disperse diverse microbes. Some of these can be deleterious to agricultural crops and forest trees, such as the bacterium Pantoea ananatis, an emerging pathogen in some systems. P. ananatis infections can lead to leaf blotches, die-back, bulb rot, and fruit rot. 2. We isolated P. ananatis bacteria from flowers with the aim of determining whether honeybees can sense these bacteria and if the bacteria affect behavioral responses of the bees to sugar solutions. 3. Honeybees decreased their responsiveness to different sugar solutions when these contained high concentrations of P. ananatis but were not deterred by solutions from which bacteria had been removed. This suggests that their reduced responsiveness was due to the taste of bacteria and not to the depletion of sugar in the solution or bacteria metabolites. Intriguingly, the bees appeared not to taste ecologically relevant low concentrations of bacteria. 4. Synthesis and applications. Our data suggest that honeybees may introduce P.ananatis bacteria into nectar in field-realistic densities during foraging trips and may thus affect nectar quality and plant fitness.

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Wang, Shujuan, Long He, and Guiru Cheng. "Cooperative Optimization QoS Cloud Routing Protocol Based on Bacterial Opportunistic Foraging and Chemotaxis Perception for Mobile Internet." Journal of Electrical and Computer Engineering 2015 (2015): 1–7. http://dx.doi.org/10.1155/2015/641062.

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In order to strengthen the mobile Internet mobility management and cloud platform resources utilization, optimizing the cloud routing efficiency is established, based on opportunistic bacterial foraging bionics, and puts forward a chemotaxis perception of collaborative optimization QoS (Quality of Services) cloud routing mechanism. The cloud routing mechanism is based on bacterial opportunity to feed and bacterial motility and to establish the data transmission and forwarding of the bacterial population behavior characteristics. This mechanism is based on the characteristics of drug resistance of bacteria and the structure of the field, and through many iterations of the individual behavior and population behavior the bacteria can be spread to the food gathering area with a certain probability. Finally, QoS cloud routing path would be selected and optimized based on bacterial bionic optimization and hedge mapping relationship between mobile Internet node and bacterial population evolution iterations. Experimental results show that, compared with the standard dynamic routing schemes, the proposed scheme has shorter transmission delay, lower packet error ratio, QoS cloud routing loading, and QoS cloud route request overhead.

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Sodsri, Parichart, Bongkoj Sookananta, and Mongkol Pusayatanont. "Optimal Placement of Distributed Generation Using Bacterial Foraging Optimization Algorithm." Applied Mechanics and Materials 781 (August 2015): 329–32. http://dx.doi.org/10.4028/www.scientific.net/amm.781.329.

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This paper presents the determination of the optimal distributed generation (DG) placement using bacterial foraging optimization algorithm (BFOA). The BFO mimics the seeking-nutrient behavior of the E. coli bacteria. It is utilized here to find the location and size of the DG installation in radial distribution system in order to obtain minimum system losses. The operation constraints include bus voltage limits, distribution line thermal limits, system power balance and generation power limits. The algorithm is tested on the IEEE 33 bus system. The result shows that the algorithm could be used as an alternative to the other techniques and improvement of the algorithm is required for acceleration and better accuracy of the calculation.

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Abbas, Nizar Hadi, and Farah Mahdi Ali. "Path Planning of an Autonomous Mobile Robot using Enhanced Bacterial Foraging Optimization Algorithm." Al-Khwarizmi Engineering Journal 12, no. 4 (December 18, 2017): 26–35. http://dx.doi.org/10.22153/kej.2016.01.001.

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This paper describes the problem of online autonomous mobile robot path planning, which is consisted of finding optimal paths or trajectories for an autonomous mobile robot from a starting point to a destination across a flat map of a terrain, represented by a 2-D workspace. An enhanced algorithm for solving the problem of path planning using Bacterial Foraging Optimization algorithm is presented. This nature-inspired metaheuristic algorithm, which imitates the foraging behavior of E-coli bacteria, was used to find the optimal path from a starting point to a target point. The proposed algorithm was demonstrated by simulations in both static and dynamic different environments. A comparative study was evaluated between the developed algorithm and other two state-of-the-art algorithms. This study showed that the proposed method is effective and produces trajectories with satisfactory results.

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Gupta, Kapil Kumar, Rizwan Beg, and Jitendra Kumar Niranjan. "An Enhanced Approach of Face Detection using Bacteria Foraging Technique." International Journal of Computer Vision and Image Processing 6, no. 1 (January 2016): 1–11. http://dx.doi.org/10.4018/ijcvip.2016010101.

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In this study, authors present an enhanced approach of face detection using bacteria foraging technique. This approach is based on chemotexis, reproduction and elimination and dispersal step. In this study the authors analysed face detection algorithm based on human skin color and fitting the ellipse as human face can be approximate by ellipse. Their approach for face detection requires no initial pre-processing of the image. A number of Bacteria agents with evolutionary behaviours are uniformly distributed in the 2-D image environment to search the skin-like pixels and locate each face-like region by evaluating the local color distribution. This approach has the advantage of very fast face detection by reducing pre-processing time of the image. This approach significantly improves face detection rate.

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35

Sun, Jianhui, and Shuai Zhang. "Rolling Bearing Fault Feature Extraction Based on Bacteria Foraging Optimization." Journal of Failure Analysis and Prevention 17, no. 6 (October 23, 2017): 1217–25. http://dx.doi.org/10.1007/s11668-017-0365-5.

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36

Nouri, Hossein, and Tang Sai Hong. "A bacteria foraging algorithm based cell formation considering operation time." Journal of Manufacturing Systems 31, no. 3 (July 2012): 326–36. http://dx.doi.org/10.1016/j.jmsy.2012.03.001.

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37

Vivekanandan, K., and D. Ramyachitra. "Bacteria foraging optimization for protein sequence analysis on the grid." Future Generation Computer Systems 28, no. 4 (April 2012): 647–56. http://dx.doi.org/10.1016/j.future.2011.10.009.

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38

Chen, Huang, Lide Wang, Jun Di, and Shen Ping. "Bacterial Foraging Optimization Based on Self-Adaptive Chemotaxis Strategy." Computational Intelligence and Neuroscience 2020 (May 27, 2020): 1–15. http://dx.doi.org/10.1155/2020/2630104.

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Bacterial foraging optimization (BFO) algorithm is a novel swarm intelligence optimization algorithm that has been adopted in a wide range of applications. However, at present, the classical BFO algorithm still has two major drawbacks: one is the fixed step size that makes it difficult to balance exploration and exploitation abilities; the other is the weak connection among the bacteria that takes the risk of getting to the local optimum instead of the global optimum. To overcome these two drawbacks of the classical BFO, the BFO based on self-adaptive chemotaxis strategy (SCBFO) is proposed in this paper. In the SCBFO algorithm, the self-adaptive chemotaxis strategy is designed considering two aspects: the self-adaptive swimming based on bacterial search state features and the improvement of chemotaxis flipping based on information exchange strategy. The optimization results of the SCBFO algorithm are analyzed with the CEC 2015 benchmark test set and compared with the results of the classical and other improved BFO algorithms. Through the test and comparison, the SCBFO algorithm proves to be effective in reducing the risk of local convergence, balancing the exploration and the exploitation, and enhancing the stability of the algorithm. Hence, the major contribution in this research is the SCBFO algorithm that provides a novel and practical strategy to deal with more complex optimization tasks.

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39

Chin, Kit Ling, Paik San H'ng, Wan Zhen Wong, Chuan Li Lee, Pui San Khoo, Abdullah Chuah Luqman, Zaidon Ashaari, and Seca Gandaseca. "Septicaemia of subterranean termites Coptotermes curvignathus caused by disturbance of bacteria isolated from termite gut and its foraging pathways." Royal Society Open Science 7, no. 8 (August 2020): 200847. http://dx.doi.org/10.1098/rsos.200847.

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Microbial pathogens continue to attract a great deal of attention to manage the termite population. Every bacterium has its own mode of action and in fact, the mechanisms used by bacteria to attack termites remain elusive at the moment. Hence, the objective of this study was to evaluate the susceptibility of subterranean termites Coptotermes curvignathus to opportunistic pathogens using culturable aerobic bacteria isolated from the termite gut and its foraging pathways. Bacterial suspensions were prepared in concentrations of 10 3 , 10 6 and 10 9 colony-forming units (CFU) ml −1 and introduced to the termites via oral-contact and physical contact treatment. The data show that contact method acted slower and gave lower mortality, compared to the oral-contact method. Coptotermes curvignathus were highly susceptible to Serratia marcescens and Pseudomonas aeruginosa . Serratia marcescens showed the highest mortality percentage of 68% and 54% at bacterial concentration of 10 9 CFU ml −1 via oral-contact and contact method, respectively. Serratia marcescens was also defined as the bacteria with the highest ability to induce the high mortality of C. curvignathus with the lowest concentration of bacterial suspension at a given time under laboratory condition. The results of this study indicate that P. aeruginosa and S. marcescens in particular may be attractive candidates worth further examination as a possible biocontrol agent against C. curvignathus in the field and to evaluate environmental and ecological risks of the biocontrol.

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40

Muni, Manoj Kumar, Dayal R. Parhi, and Priyadarshi Biplab Kumar. "Improved Motion Planning of Humanoid Robots Using Bacterial Foraging Optimization." Robotica 39, no. 1 (May 7, 2020): 123–36. http://dx.doi.org/10.1017/s0263574720000235.

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SUMMARYThis paper emphasizes on Bacterial Foraging Optimization Algorithm for effective and efficient navigation of humanoid NAO, which uses the foraging quality of bacteria Escherichia coli for getting shortest path between two locations in minimum time. The Gaussian cost function assigned to both attractant and repellent profile of bacterium performs a major role in obtaining the best path between any two locations. Mathematical formulations have been performed to design the control architecture for humanoid navigation using the proposed methodology. The developed approach has been tested in a simulation platform, and the simulation results have been validated in an experimental platform. Here, motion planning for both single and multiple humanoid robots on a common platform has been performed by integrating a petri-net architecture for multiple humanoid navigation. Finally, the results obtained from both the platforms are compared in terms of suitable navigational parameters, and proper agreements have been observed with minimal amount of error limits.

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Sarangi, Archana, Sasmita Kumari Padhy, Siba Prasada Panigrahi, and Shubhendu Kumar Sarangi. "GBF Trained Neuro-fuzzy Equalizer for Time Varying Channels." International Journal of Applied Evolutionary Computation 2, no. 3 (July 2011): 27–38. http://dx.doi.org/10.4018/jaec.2011070103.

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This paper proposes a neuro-fuzzy filter for equalization of time-varying channels. Additionally, it proposes to tune the equalizer with a hybrid algorithm between Genetic Algorithms (GA) and Bacteria Foraging (BFO), termed as GBF. The major advantage of the method developed in this paper is that all parameters of the neuro-fuzzy network, including the rule base, are tuned simultaneously through the proposed hybrid algorithm of genetic Algorithm and bacteria foraging. The performance of the Neuro-Fuzzy equalizer designed using the proposed approach is compared with Genetic algorithm based equalizers. The results confirm that the methodology used in the paper is much better than existing approaches. The proposed hybrid algorithm also eliminates the limitations of GA based equalizer, i.e. the inherent characteristic of GA, i.e. GAs risk finding a sub-optimal solution.

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42

Liu, Jianguo, Zhanying Liu, Yucheng Liu, Min Hao, and Xianzhi Hou. "Analysis of cellulolytic bacterial flora in the rumen of inner Mongolian sheep." BioResources 14, no. 4 (October 16, 2019): 9544–56. http://dx.doi.org/10.15376/biores.14.4.9544-9556.

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The cellulolytic bacterial flora present in the rumen of Inner Mongolian sheep are thought to have a high degree of cellulose-degrading activity because of their foraging feeding regimen. However, there are no report on the genetic and species composition of the cellulolytic bacterial flora. In this study, cellulolytic bacteria were isolated from the rumen of Inner Mongolian sheep using a combined method of transparent zone and filter paper degradation. Twenty-two strains were identified via morphological, physiological, and biochemical tests. Ten strains were further identified via DNA (G + C) mol%, together with 16S rDNA gene sequencing analysis. Four types of extracellular and total cellulase activities of representative strains were determined. The results demonstrated that the isolates included Butyrivibrio fibrisolvens, Rumincoccus albus, R. flavefaciens, Fibrobacter succinogenes, and Clostridium polysaccharolyticum. A big proportion of cellulolytic Butyrivibrio fibrisolvens was found in the rumen of Inner Mongolian sheep. This was the first study to analyze the cellulolytic bacterial flora in the rumen of foraging Inner Mongolian sheep. These results indicated that the rumen of Inner Mongolian sheep represents an attractive source for cellulolytic microorganisms and enzymes, and the research results have a certain guiding importance for the efficient degradation of cellulosic materials.

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43

Sivasubramanian, Gobi Mohan, and Murali Narayanamurthy. "Implementation of PWM AC chopper controller for capacitor run induction motor drive via bacterial foraging optimization algorithm." International Journal of Reconfigurable and Embedded Systems (IJRES) 9, no. 3 (November 1, 2020): 169. http://dx.doi.org/10.11591/ijres.v9.i3.pp169-177.

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<p>This paper focuses on design of closed-loop control for pulse width modulated AC chopper controlled capacitor run induction motor drive engaging enriched optimization algorithm based on foraging of bacteria. Capacitor run induction motor is a non-linear device and its parameter varies under different functional point of the system. A linearized increment model for PWM AC chopper is illustrated for a particular functional point of the drive. The conventional method does not provide acceptable performance under different load conditions. Bacteria foraging optimization technique categorizes accurate control parameters for the superlative dynamic response under unit step load variations. Field Programmable Gate Array is implemented practically for a particular functional point of the drive to exhibit accurate performance. Experimental and simulated results are obtained to authenticate the effectiveness of the optimized controller.</p><p> </p>

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44

Dubuisson, Félix, Miloud Rezkallah, Hussein Ibrahim, and Ambrish Chandra. "Real-Time Implementation of the Predictive-Based Control with Bacterial Foraging Optimization Technique for Power Management in Standalone Microgrid Application." Energies 14, no. 6 (March 19, 2021): 1723. http://dx.doi.org/10.3390/en14061723.

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In this paper, the predictive-based control with bacterial foraging optimization technique for power management in a standalone microgrid is studied and implemented. The heuristic optimization method based on the social foraging behavior of Escherichia coli bacteria is employed to determine the power references from the non-renewable energy sources and loads of the proposed configuration, which consists of a fixed speed diesel generator and battery storage system (BES). The two-stage configuration is controlled to maintain the DC-link voltage constant, regulate the AC voltage and frequency, and improve the power quality, simultaneously. For these tasks, on the AC side, the obtained power references are used as input signals to the predictive-based control. With the help of the system parameters, the predictive-based control computes all possible states of the system on the next sampling time and compares them with the estimated power references obtained using the bacterial foraging optimization (BFO) technique to get the inverter current reference. For the DC side, the same concept based on the predictive approach is employed to control the DC-DC buck-boost converter by regulating the DC-link voltage using the forward Euler method to generate the discrete-time model to predict in real-time the BES current. The proposed control strategies are evaluated using simulation results obtained with Matlab/Simulink in presence of different types of loads, as well as experimental results obtained with a small-scale microgrid.

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45

Rajeshwari, A. "Civilizing Energy Efficiency in Wireless Sensor Network Using Bacteria Foraging Algorithm." IOSR Journal of Computer Engineering 7, no. 5 (2012): 61–65. http://dx.doi.org/10.9790/0661-0756165.

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46

Bose, Pitchaimanickam. "Bacteria Foraging Algorithm for Optimal Topology Construction in Wireless Sensor Networks." International Journal of Applied Metaheuristic Computing 13, no. 1 (January 2022): 1–17. http://dx.doi.org/10.4018/ijamc.292512.

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Topology control is a significant method to reduce energy consumption and prolong the network lifetime. Connected Dominated Sets (CDS) are the emerging technologies to construct the energy- efficient optimal topology. Traditional topology construction algorithms are not utilized suitable optimization techniques for finding the optimum location of the active nodes in the networks. In this paper, Bacteria Foraging Algorithm (BFA) identifies the optimal location for active nodes to form the virtual backbone of the network. Residual energy and network connectivity are considered to evaluate the fitness function. The performance of the BFA is compared with other algorithms namely A3, A1, Genetic Algorithm (GA), and Gravitational Search Algorithm (GSA) algorithms for considering the performance metrics of the active nodes, residual energy, and connected sensing area coverage. Simulation results show that the proposed methodology performs well for reducing energy consumption and improving the connected sensing coverage area in the wireless sensor network.

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47

Halilu, B. D., L. Maijama’a, A. G Jumba, S. A Baraza, A. A Jimoh, and K. T. Aminu. "CONTROLLER DESIGN FOR MAGNETIC LEVITATION SYSTEM USING BACTERIA FORAGING ALGORITHM TECHNIQUE." International Journal of Advances in Scientific Research and Engineering 5, no. 5 (2019): 149–56. http://dx.doi.org/10.31695/ijasre.2019.33210.

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48

Rajinikanth, V., and K. Latha. "Controller Parameter Optimization for Nonlinear Systems Using Enhanced Bacteria Foraging Algorithm." Applied Computational Intelligence and Soft Computing 2012 (2012): 1–12. http://dx.doi.org/10.1155/2012/214264.

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An enhanced bacteria foraging optimization (EBFO) algorithm-based Proportional + integral + derivative (PID) controller tuning is proposed for a class of nonlinear process models. The EBFO algorithm is a modified form of standard BFO algorithm. A multiobjective performance index is considered to guide the EBFO algorithm for discovering the best possible value of controller parameters. The efficiency of the proposed scheme has been validated through a comparative study with classical BFO, adaptive BFO, PSO, and GA based controller tuning methods proposed in the literature. The proposed algorithm is tested in real time on a nonlinear spherical tank system. The real-time results show that, EBFO tuned PID controller gives a smooth response for setpoint tracking performance.

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49

Mishra, S., M. Tripathy, and J. Nanda. "Multi-machine power system stabilizer design by rule based bacteria foraging." Electric Power Systems Research 77, no. 12 (October 2007): 1595–607. http://dx.doi.org/10.1016/j.epsr.2006.11.006.

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50

Nouri, H., S. H. Tang, B. T. Hang Tuah, and M. K. Anuar. "BASE: A bacteria foraging algorithm for cell formation with sequence data." Journal of Manufacturing Systems 29, no. 2-3 (July 2010): 102–10. http://dx.doi.org/10.1016/j.jmsy.2010.11.004.

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