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Artykuły w czasopismach na temat „BACTERIAL FORAGING”

Autor: Grafiati

Data publikacji: 11 września 2023

Data aktualizacji: 25 lipca 2025

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1

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 d

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Kanagasabai, Lenin. "Diminution of factual power loss by enhanced bacterial foraging optimization algorithm." International Journal of Applied Power Engineering 9, no. 3 (2022): 245~249. https://doi.org/10.5281/zenodo.7353285.

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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 Escherichia coli 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 th

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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 (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 a

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5

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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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

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6

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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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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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 decompos

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8

Narendhar., S., and T. Amudha. "A Hybrid Bacterial Foraging Algorithm For Solving Job Shop Scheduling Problems." International Journal of Programming Languages and Applications ( IJPLA ) 2, no. 4 (2020): 1–11. https://doi.org/10.5281/zenodo.3992701.

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Bio-Inspired computing is the subset of Nature-Inspired computing. Job Shop Scheduling Problem is categorized under popular scheduling problems. In this research work, Bacterial Foraging Optimization was hybridized with Ant Colony Optimization and a new technique Hybrid Bacterial Foraging Optimization for solving Job Shop Scheduling Problem was proposed. The optimal solutions obtained by proposed Hybrid Bacterial Foraging Optimization algorithms are much better when compared with the solutions obtained by Bacterial Foraging Optimization algorithm for well-known test problems of different sizes

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9

AMIT, D.PUROHIT, and S. T. KHANDARE PROF. "COLOR IMAGE SEGMENTATION TECHNIQUE USING COOPERATIVE BACTERIAL FORAGING ALGORITHM." JournalNX - A Multidisciplinary Peer Reviewed Journal 3, no. 5 (2017): 56–59. https://doi.org/10.5281/zenodo.1446386.

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 Image segmentation is a crucial and challenging problem in image processing and often a basic step for high level analysis. The intent of image segmentation is to divide an image into different classes based on features, such as color, intensity or histogram, where each pixel in the image should go to one class and only one class. According to the thresholds the segmented results whether or not consistent to the image is also an issue should be considered.Here wepropose a new method for color image segmentation using multilevel thresholding. This paper proposes multilevel thresholding fo

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10

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, de

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11

Stubbusch, Astrid K. M., François J. Peaudecerf, Kang Soo Lee, et al. "Antagonism as a foraging strategy in microbial communities." Science 388, no. 6752 (2025): 1214–17. https://doi.org/10.1126/science.adr8286.

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In natural habitats, nutrient availability limits bacterial growth. We discovered that bacteria can overcome this limitation by acquiring nutrients by lysing neighboring cells through contact-dependent antagonism. Using single-cell live imaging and isotopic markers, we found that during starvation, the type VI secretion system (T6SS) lysed neighboring cells and thus provided nutrients from lysing cells for growth. Genomic adaptations in antagonists, characterized by a reduced metabolic gene repertoire, and the previously unexplored distribution of the T6SS across bacterial taxa in natural envi

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12

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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13

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 (2007): 807–12. http://dx.doi.org/10.5391/jkiis.2007.17.6.807.

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14

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 greatl

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15

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 attac

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16

Mai, Xiong Fa, and Ling Li. "Bacterial Foraging Algorithm Based on PSO with Adaptive Inertia Weigh for Solving Nonlinear Equations Systems." Advanced Materials Research 655-657 (January 2013): 940–47. http://dx.doi.org/10.4028/www.scientific.net/amr.655-657.940.

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Bacterial Foraging Algorithm (BFA) has recently emerged as a very powerful technique for optimization,but it also confronts the problems of slow convergence and premature convergence. To overcome the drawbacks of BFA, This article merge the idea of particle swarm optimization algorithm with adaptive inertia weigh into the bacterial foraging to improve the speed and convergence capabilities of BFA, and according to this a bacterial foraging algorithm based on PSO(APSO-BFA) is presented. Simulation results on five systems of nonlinear equations show that the proposed algorithm is superior to the

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17

Huang, Mei-Ling, and Cheng-Jian Lin. "Nonlinear system control using a fuzzy cerebellar model articulation controller involving reinforcement-strategy-based bacterial foraging optimization." Advances in Mechanical Engineering 10, no. 9 (2018): 168781401879742. http://dx.doi.org/10.1177/1687814018797426.

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This article proposes a fuzzy cerebellar model articulation controller with reinforcement-strategy-based modified bacterial foraging optimization for solving the cart-pole balancing control problem. The proposed reinforcement-strategy-based modified bacterial foraging optimization is used to adjust the parameters of fuzzy receptive field functions and fuzzy weights for improving the accuracy of the fuzzy cerebellar model articulation controller output. An efficient strategic approach is applied in the chemotaxis step in the traditional bacterial foraging optimization algorithm. In the approach

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18

Periyasamy, S., and R. Kaniezhil. "Enhanced feature selection with bacterial foraging and rough set analysis for document clustering." Journal of Autonomous Intelligence 7, no. 5 (2024): 1631. http://dx.doi.org/10.32629/jai.v7i5.1631.

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<p>Most applications, such as Information Retrieval and Natural Language Processing (NLP), utilize document clustering to improve their analysis. The document consists of various features that are utilized to determine the similar and dissimilar documents. However, the traditional techniques consume high computation difficulties and convergence problems while analyzing high-dimensional data. The research difficulties are addressed with the help of Bacterial Foraging and Rough Set Analysis (BF-RSA). This study uses the TF-IDF features for analyzing similar documents. The extracted feature

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19

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 direct

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20

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 (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 optimiz

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21

Panda, Rutuparna, Manoj Kumar Naik, and B. K. Panigrahi. "Face recognition using bacterial foraging strategy." Swarm and Evolutionary Computation 1, no. 3 (2011): 138–46. http://dx.doi.org/10.1016/j.swevo.2011.06.001.

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Li, M. S., T. Y. Ji, W. J. Tang, Q. H. Wu, and J. R. Saunders. "Bacterial foraging algorithm with varying population." Biosystems 100, no. 3 (2010): 185–97. http://dx.doi.org/10.1016/j.biosystems.2010.03.003.

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23

Wan, Miao, Lixiang Li, Jinghua Xiao, Cong Wang, and Yixian Yang. "Data clustering using bacterial foraging optimization." Journal of Intelligent Information Systems 38, no. 2 (2011): 321–41. http://dx.doi.org/10.1007/s10844-011-0158-3.

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Abdul Hameed, K., and S. Palani. "Robust design of power system stabilizer using bacterial foraging algorithm." Archives of Electrical Engineering 62, no. 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 al

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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 experi

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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 (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)

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27

Zeng, Zhigao, Lianghua Guan, Wenqiu Zhu, Jing Dong, and Jun Li. "Face Recognition Based on SVM Optimized by the Improved Bacterial Foraging Optimization Algorithm." International Journal of Pattern Recognition and Artificial Intelligence 33, no. 07 (2019): 1956007. http://dx.doi.org/10.1142/s021800141956007x.

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Support vector machine (SVM) is always used for face recognition. However, kernel function selection (kernel selection and its parameters selection) is a key problem for SVMs, and it is difficult. This paper tries to make some contributions to this problem with focus on optimizing the parameters in the selected kernel function. Bacterial foraging optimization algorithm, inspired by the social foraging behavior of Escherichia coli, has been widely accepted as a global optimization algorithm of current interest for distributed optimization and control. Therefore, we proposed to optimize the para

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28

Yang, D. L., Xue Jun Li, K. Wang, and Ling Li Jiang. "Support Vector Machine Optimization Based on Bacterial Foraging Algorithm and Applied in Fault Diagnosis." Advanced Materials Research 216 (March 2011): 153–57. http://dx.doi.org/10.4028/www.scientific.net/amr.216.153.

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The parameter optimization is the key to study of support vector machine (SVM). With strong global search capability of bacterial foraging algorithm(BFA), the optimization method—support vector machine parameters optimization based on bacterial foraging algorithm was proposed, which can achieve the dynamic optimization of the parametersCandγ,and overcomes the problem of inefficiency for selecting reasonable parameters according to the experience in the traditional fault diagnosis. Compared with other methods, the BFA is simpler and easier for programming, and the optimization SVM model become

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29

RADHAMANI, A. S., and E. BABURAJ. "PERFORMANCE EVALUATION OF PARALLEL GENETIC AND PARTICLE SWARM OPTIMIZATION ALGORITHMS WITHIN THE MULTICORE ARCHITECTURE." International Journal of Computational Intelligence and Applications 13, no. 04 (2014): 1450024. http://dx.doi.org/10.1142/s1469026814500242.

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In recent studies we found that there are many optimization methods presented for multicore processor performance optimization, however each method is suffered from limitations. Hence in this paper we presented a new method which is a combination of bacterial Foraging Particle swarm Optimization with certain constraints named as Constraint based Bacterial Foraging Particle Swarm Optimization (CBFPSO) scheduling can be effectively implemented. The proposed Constraint based Bacterial Foraging Particle Swarm Optimization (CBFPSO) scheduling for multicore architecture, which updates the velocity a

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Ye, Man-Hong, Shu-Hang Fan, Xiao-Yuan Li, et al. "Microbiota dysbiosis in honeybee ( Apis mellifera L . ) larvae infected with brood diseases and foraging bees exposed to agrochemicals." Royal Society Open Science 8, no. 1 (2021): 201805. http://dx.doi.org/10.1098/rsos.201805.

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American foulbrood (AFB) disease and chalkbrood disease (CBD) are important bacterial and fungal diseases, respectively, that affect honeybee broods. Exposure to agrochemicals is an abiotic stressor that potentially weakens honeybee colonies. Gut microflora alterations in adult honeybees associated with these biotic and abiotic factors have been investigated. However, microbial compositions in AFB- and CBD-infected larvae and the profile of whole-body microbiota in foraging bees exposed to agrochemicals have not been fully studied. In this study, bacterial and fungal communities in healthy and

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P.D., Sathya. "MINIMUM CROSS ENTROPY BASED IMAGE SEGMENTATION USING NEW HEURISTIC OPTIMIZATION TECHNIQUE." GLOBAL JOURNAL OF ENGINEERING SCIENCE AND RESEARCHES 5, no. 7 (2019): 522–30. https://doi.org/10.5281/zenodo.2656443.

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Image thresholding is an important technique for image processing and pattern recognition. Multilevel thresholding problem is often treated as a problem of optimization of an objective function. In this paper, minimum cross entropy (MCE) is introduced for multilevel thresholding which uses Improved Bacterial Foraging (IBF) algorithm for minimizing the MCE objective function. Some examples of test images are presented to compare the segmentation methods based on the IBF approach, with bacterial foraging (BF) algorithm, particle swarm optimization (PSO) algorithm and genetic algorithm (GA). From

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32

Yudong Zhang, and Lenan Wu. "Bacterial Foraging Optimization Used in Cluster Analysis." International Journal of Digital Content Technology and its Applications 6, no. 22 (2012): 345–54. http://dx.doi.org/10.4156/jdcta.vol6.issue22.39.

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Zhang, Guo-yong, Yong-gang Wu, and Yu-xiang Tan. "Bacterial Foraging Optimization Algorithm with Quantum Behavior." Journal of Electronics & Information Technology 35, no. 3 (2014): 614–21. http://dx.doi.org/10.3724/sp.j.1146.2012.00892.

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Geng, Shuang, Xiaofu He, Yixin Wang, Hong Wang, Ben Niu, and Kris M. Law. "Multicriteria recommendation based on bacterial foraging optimization." International Journal of Intelligent Systems 37, no. 2 (2021): 1618–45. http://dx.doi.org/10.1002/int.22688.

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Tian, Hong Peng. "Image Matching Based on Bacterial Foraging Algorithm." Advanced Materials Research 301-303 (July 2011): 859–63. http://dx.doi.org/10.4028/www.scientific.net/amr.301-303.859.

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To increase the speed of image matching, this paper combines Bacterial Foraging Algorithm (BFA) of swarm intelligence with wavelet transform, and presents a fast matching method. The method regards the problem of image matching as a search for the optimal solution. To provide artificial bacterial swarm algorithm with an appropriate fitness function, the Normalized Product correlation (NPROD) is employed to measure the similarity between the template image and the searching image. Then the best coarse matching position is gradually approaching by chemotaxis, elimination and dispersal, and repro

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Liu, Wei, Ben Niu, Hanning Chen, and Yunlong Zhu. "Robot Path Planning Using Bacterial Foraging Algorithm." Journal of Computational and Theoretical Nanoscience 10, no. 12 (2013): 2890–96. http://dx.doi.org/10.1166/jctn.2013.3296.

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Lin, W., and P. X. Liu. "Hammerstein model identification based on bacterial foraging." Electronics Letters 42, no. 23 (2006): 1332. http://dx.doi.org/10.1049/el:20062743.

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Niu, Ben, Jing Liu, Teresa Wu, Xianghua Chu, Zhengxu Wang, and Yanmin Liu. "Coevolutionary Structure-Redesigned-Based Bacterial Foraging Optimization." IEEE/ACM Transactions on Computational Biology and Bioinformatics 15, no. 6 (2018): 1865–76. http://dx.doi.org/10.1109/tcbb.2017.2742946.

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Mirzaei, Seiyed Mohammad, and Mohammad Hossein Moattar. "Optimized PID Controller with Bacterial Foraging Algorithm." International Journal of Electrical and Computer Engineering (IJECE) 5, no. 6 (2015): 1372. http://dx.doi.org/10.11591/ijece.v5i6.pp1372-1380.

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<p><em>Fish robot precision depends on a variety of factors including the precision of motion sensors, mobility of links, elasticity of fish robot actuators system, and the precision of controllers. Among these factors, precision and efficiency of controllers play a key role in fish robot precision. In the present paper, a robot fish has been designed with dynamics and swimming mechanism of a real fish. According to equations of motion, this fish robot is designed with 3 hinged links. Subsequently, its control system was defined based on the same equations. In this paper, an approa

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Kao, Yucheng, and Hsiu-Tzu Cheng. "Bacterial Foraging Optimization Approach to Portfolio Optimization." Computational Economics 42, no. 4 (2013): 453–70. http://dx.doi.org/10.1007/s10614-012-9357-4.

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Gollapudi, Sastry V. R. S., Shyam S. Pattnaik, O. P. Bajpai, Swapna Devi, and K. M. Bakwad. "Velocity Modulated Bacterial Foraging Optimization Technique (VMBFO)." Applied Soft Computing 11, no. 1 (2011): 154–65. http://dx.doi.org/10.1016/j.asoc.2009.11.006.

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Hernandez-Ocana, Betania, Oscar Chavez-Bosquez, Jose Hernandez-Torruco, Juana Canul-Reich, and Pilar Pozos-Parra. "Bacterial Foraging Optimization Algorithm for Menu Planning." IEEE Access 6 (2018): 8619–29. http://dx.doi.org/10.1109/access.2018.2794198.

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Pan, Yongsheng, Yong Xia, Tao Zhou, and Michael Fulham. "Cell image segmentation using bacterial foraging optimization." Applied Soft Computing 58 (September 2017): 770–82. http://dx.doi.org/10.1016/j.asoc.2017.05.019.

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Khanduja, Vidhi, Om Prakash Verma, and Shampa Chakraverty. "Watermarking relational databases using bacterial foraging algorithm." Multimedia Tools and Applications 74, no. 3 (2013): 813–39. http://dx.doi.org/10.1007/s11042-013-1700-9.

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Wang, Liying, Weiguo Zhao, Yulong Tian, and Gangzhu Pan. "A bare bones bacterial foraging optimization algorithm." Cognitive Systems Research 52 (December 2018): 301–11. http://dx.doi.org/10.1016/j.cogsys.2018.07.022.

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Sathya, P. D., and R. Kayalvizhi. "Optimal multilevel thresholding using bacterial foraging algorithm." Expert Systems with Applications 38, no. 12 (2011): 15549–64. http://dx.doi.org/10.1016/j.eswa.2011.06.004.

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Panigrahi, B. K., and V. Ravikumar Pandi. "Congestion management using adaptive bacterial foraging algorithm." Energy Conversion and Management 50, no. 5 (2009): 1202–9. http://dx.doi.org/10.1016/j.enconman.2009.01.029.

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P.D., Sathya. "IMAGE SEGMENTATION WITH IMPROVED BACTERIAL FORAGING ALGORITHM." GLOBAL JOURNAL OF ENGINEERING SCIENCE AND RESEARCHES5 5, no. 3 (2018): 110–18. https://doi.org/10.5281/zenodo.1404195.

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Image thresholding is an important technique for image processing and pattern recognition. Multilevel thresholding problem is often treated as a problem of optimization of an objective function. In this paper, minimum cross entropy (MCE) is introduced for multilevel thresholding which uses Improved Bacterial Foraging (IBF) algorithm for minimizing the MCE objective function. Some examples of test images are presented to compare the segmentation methods based on the IBF approach, with bacterial foraging (BF) algorithm, particle swarm optimization (PSO) algorithm and genetic algorithm (GA). From

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Kim, Dong Hwa, and Jae Hoon Cho. "Robust Tuning of PID Controller Using Bacterial-Foraging-Based Optimization." Journal of Advanced Computational Intelligence and Intelligent Informatics 9, no. 6 (2005): 669–76. http://dx.doi.org/10.20965/jaciii.2005.p0669.

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We propose a design approach to PID controllers with resistance to external disturbance in motor-controlled systems using a bacterial foraging-based optimal algorithm. PID controllers are used to operate AC motor drives because of their practical implementation and simple structure. Inexperienced personnel find it difficult, however, to achieve optimal PID gain because this is manually tuned by trial and error in industrial environments full of disturbances. To design disturbance-resistance tuning, we use disturbance-resistance conditions based on H∞ and calculcate response the performance bas

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Lin, Cheng-Jian, and Hsueh-Yi Lin. "Mobile robot wall-following control using a fuzzy cerebellar model articulation controller with group-based strategy bacterial foraging optimization." International Journal of Advanced Robotic Systems 14, no. 4 (2017): 172988141772087. http://dx.doi.org/10.1177/1729881417720872.

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In this study, a fuzzy cerebellar model articulation controller based on group-based strategy bacterial foraging optimization is proposed for mobile robot wall-following control. In fuzzy cerebellar model articulation controller, the inputs are the distance between the sonar and the wall, and the outputs are the angular velocity of two wheels. The proposed group-based strategy bacterial foraging optimization learning algorithm is used to adjust the parameters of fuzzy cerebellar model articulation controller model. The proposed group-based strategy bacterial foraging optimization has the advan

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