Статті в журналах: "BACTERIAL FORAGING"

1

Passino, Kevin M. "Bacterial Foraging Optimization." International Journal of Swarm Intelligence Research 1, no. 1 (January 2010): 1–16. http://dx.doi.org/10.4018/jsir.2010010101.

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The bacterial foraging optimization (BFO) algorithm mimics how bacteria forage over a landscape of nutrients to perform parallel nongradient optimization. In this article, the author provides a tutorial on BFO, including an overview of the biology of bacterial foraging and the pseudo-code that models this process. The algorithms features are briefly compared to those in genetic algorithms, other bio-inspired methods, and nongradient optimization. The applications and future directions of BFO are also presented.

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2

Panda, Rutuparna, and Manoj Kumar Naik. "A Crossover Bacterial Foraging Optimization Algorithm." Applied Computational Intelligence and Soft Computing 2012 (2012): 1–7. http://dx.doi.org/10.1155/2012/907853.

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This paper presents a modified bacterial foraging optimization algorithm called crossover bacterial foraging optimization algorithm, which inherits the crossover technique of genetic algorithm. This can be used for improvising the evaluation of optimal objective function values. The idea of using crossover mechanism is to search nearby locations by offspring (50 percent of bacteria), because they are randomly produced at different locations. In the traditional bacterial foraging optimization algorithm, search starts from the same locations (50 percent of bacteria are replicated) which is not desirable. Seven different benchmark functions are considered for performance evaluation. Also, comparison with the results of previous methods is presented to reveal the effectiveness of the proposed algorithm.

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3

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

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

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4

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

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5

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

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

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6

Lenin, Kanagasabai. "Diminution of factual power loss by enhanced bacterial foraging optimization algorithm." International Journal of Applied Power Engineering (IJAPE) 9, no. 3 (December 1, 2020): 245. http://dx.doi.org/10.11591/ijape.v9.i3.pp245-249.

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<div data-canvas-width="126.37004132231402">This paper presents an enhanced bacterial foraging optimization (EBFO) algorithm for solving the optimal reactive power problem. Bacterial foraging optimization is based on foraging behaviour of <em>Escherichia coli</em> bacteria which present in the human intestine. Bacteria have inclination to congregate the nutrient-rich areas by an action called as Chemo taxis. The bacterial foraging process consists of four chronological methods i.e. chemo taxis, swarming and reproduction and elimination-dispersal. In this work rotation angle adaptively and incessantly modernized, which augment the diversity of the population and progress the global search capability. The quantum rotation gate is utilized for chemo taxis to modernize the state of chromosome projected EBFO algorithm has been tested in standard IEEE 14,300 bus test system and simulation results show the projected algorithm reduced the real power loss extensively.</div>

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7

Shen, Hai, and Mo Zhang. "Bacterial Foraging Optimization Algorithm with Quorum Sensing Mechanism." Applied Mechanics and Materials 556-562 (May 2014): 3844–48. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.3844.

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Quorum sensing is widely distributed in bacteria and make bacteria are similar to complex adaptive systems, with intelligent features such as emerging and non-linear, the ultimate expression of the adaptive to changes in the environment. Based on the phenomenon of bacterial quorum sensing and Bacterial Foraging Optimization Algorithm, some new optimization algorithms have been proposed. In this paper, it presents research situations, such as environment-dependent quorum sensing mechanism, quorum sensing mechanism with quantum behavior, cell-to-cell communication, multi-colony communication, density perception mechanism. Areas of future emphasis and direction in development were also pointed out.

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8

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

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9

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

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10

Wei, Zhong-hua, Xia Zhao, Ke-wen Wang, and Yan Xiong. "Bus Dispatching Interval Optimization Based on Adaptive Bacteria Foraging Algorithm." Mathematical Problems in Engineering 2012 (2012): 1–10. http://dx.doi.org/10.1155/2012/389086.

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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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11

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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12

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 other two kinds of bacterial foraging algorithm

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13

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 (September 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, each virtual bacterium swims for different run lengths and increases the bacterial diversity. Experimental results are presented to show the performance and effectiveness of the proposed reinforcement-strategy-based modified bacterial foraging optimization method.

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14

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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15

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

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16

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 (June 2010): 185–97. http://dx.doi.org/10.1016/j.biosystems.2010.03.003.

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17

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 (April 9, 2011): 321–41. http://dx.doi.org/10.1007/s10844-011-0158-3.

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18

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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19

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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20

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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21

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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22

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 (June 7, 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 parameters in SVM by an improved bacterial foraging optimization algorithm (IBFOA). In the improved version of bacterial foraging optimization algorithm, a dynamical elimination-dispersal probability in the elimination-dispersal step and a dynamical step size in the chemotactic step are used to improve the performance of bacterial foraging optimization algorithm. Then the optimized SVM is used for face recognition. Simultaneously, an improved local binary pattern is proposed to extract features of face images in this paper to improve the accuracy rate of face recognition. Numerical results show the advantage of our algorithm over a range of existing algorithms.

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23

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 smaller. The rolling bearing fault diagnosis results show that bacterial foraging algorithm is suitable for support vector machine parameter optimization.

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24

Yudong Zhang, and Lenan Wu. "Bacterial Foraging Optimization Used in Cluster Analysis." International Journal of Digital Content Technology and its Applications 6, no. 22 (December 31, 2012): 345–54. http://dx.doi.org/10.4156/jdcta.vol6.issue22.39.

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25

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 (January 20, 2014): 614–21. http://dx.doi.org/10.3724/sp.j.1146.2012.00892.

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26

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 (October 6, 2021): 1618–45. http://dx.doi.org/10.1002/int.22688.

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27

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 reproduction behaviors of artificial bacterial. Finally, the best matching position is found out according to the coarse matching position. Experimental results show that the proposed method is fast and efficient.

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28

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 (December 1, 2013): 2890–96. http://dx.doi.org/10.1166/jctn.2013.3296.

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29

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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30

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 (November 1, 2018): 1865–76. http://dx.doi.org/10.1109/tcbb.2017.2742946.

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31

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 (December 1, 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 approach is suggested to control fish robot trajectory using optimized PID controller through Bacterial Foraging algorithm, so as to adjust the gains. Then, this controller is compared to the powerful Fuzzy controller and optimized PID controller through PSO algorithm when applying step and sine inputs. The research findings revealed that optimized PID controller through Bacterial Foraging Algorithm had better performance than other approaches in terms of decreasing of the settling time, reduction of the maximum overshoot and desired steady state error in response to step input. Efficiency of the suggested method has been analyzed by MATLAB software.</em></p>

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32

Kao, Yucheng, and Hsiu-Tzu Cheng. "Bacterial Foraging Optimization Approach to Portfolio Optimization." Computational Economics 42, no. 4 (January 9, 2013): 453–70. http://dx.doi.org/10.1007/s10614-012-9357-4.

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33

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 (January 2011): 154–65. http://dx.doi.org/10.1016/j.asoc.2009.11.006.

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34

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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35

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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36

Khanduja, Vidhi, Om Prakash Verma, and Shampa Chakraverty. "Watermarking relational databases using bacterial foraging algorithm." Multimedia Tools and Applications 74, no. 3 (October 13, 2013): 813–39. http://dx.doi.org/10.1007/s11042-013-1700-9.

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37

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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38

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

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39

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

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40

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 (December 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 and position by two bacterial behaviours, i.e. reproduction and elimination dispersal. The performance of CBFPSO is compared with the simulation results of GA, and the result shows that the proposed algorithm has pretty good performance on almost all types of cores compared to GA with respect to completion time and energy consumption.

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Ye, Man-Hong, Shu-Hang Fan, Xiao-Yuan Li, Islam Mohd Tarequl, Chun-Xiang Yan, Wan-Hong Wei, Sheng-Mei Yang, and Bin Zhou. "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 (January 13, 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 diseased (AFB/CBD) honeybee larvae were characterized by amplicon sequencing of bacterial 16S rRNA gene and fungal internal transcribed spacer1 region, respectively. The bacterial and fungal communities in disordered foraging bees poisoned by agrochemicals were analysed. Our results revealed that healthy larvae were significantly enriched in bacterial genera Lactobacillus and Stenotrophomonas and the fungal genera Alternaria and Aspergillus . The enrichment of these microorganisms, which had antagonistic activities against the etiologic agents for AFB and CBD, respectively, may protect larvae from potential infection. In disordered foraging bees, the relative abundance of bacterial genus Gilliamella and fungal species Cystofilobasidium macerans were significantly reduced, which may compromise hosts' capacities in nutrient absorption and immune defence against pathogens. Significantly higher frequency of environmentally derived fungi was observed in disordered foraging bees, which reflected the perturbed microbiota communities of hosts. Results from PICRUSt and FUNGuild analyses revealed significant differences in gene clusters of bacterial communities and fungal function profiles. Overall, results of this study provide references for the composition and function of microbial communities in AFB- and CBD-infected honeybee larvae and foraging bees exposed to agrochemicals.

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Li, Xue Jun, Da Lian Yang, Zong Qun Deng, and Ling Li Jiang. "KPCA Feature Extraction Based on Bacterial Foraging Algorithm." Advanced Engineering Forum 2-3 (December 2011): 200–204. http://dx.doi.org/10.4028/www.scientific.net/aef.2-3.200.

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KPCA is a commonly used method for feature extraction, for the problems of kernel function and its parameters have a great influence on performance of KPCA feature extraction but the optimal parameters are difficult to select. This paper applied the bacterial foraging algorithm on KPCA feature extraction and the method of KPCA feature extraction based on bacterial foraging algorithm was proposed. The experiment of bearing feature extraction shows that the method which proposed in this paper is effective.

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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 (November 20, 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 based on bacterial foraging for the PID controller as an integral of time-weighted squared error. Hence, parameters for the PID controller are selected by our bacterial foraging-based optimal algorithm to obtain the required response.

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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 (July 1, 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 advantages of global search, evolutionary strategies, and group evolution to speed up the convergent rate. A new fitness function is defined to evaluate the performance of mobile robot wall-following control. The fitness function includes four assessment factors which are defined as follows: (1) maintaining safe distance between the mobile robot and the wall, (2) ensuring successfully running a cycle, (3) avoiding mobile robot collisions, and (4) mobile robot running at a maximum speed. The experimental results show that the proposed group-based strategy bacterial foraging optimization obtains a better wall-following control than other methods in unknown environments.

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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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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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Mo, Hongwei, and Yujing Yin. "Image Segmentation Based on Bacterial Foraging and FCM Algorithm." International Journal of Swarm Intelligence Research 2, no. 3 (July 2011): 16–28. http://dx.doi.org/10.4018/jsir.2011070102.

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This paper addresses the issue of image segmentation by clustering in the domain of image processing. The clustering algorithm taken account here is the Fuzzy C-Means which is widely adopted in this field. Bacterial Foraging Optimization Algorithm is an optimal algorithm inspired by the foraging behavior of E.coli. For the purpose to reinforce the global search capability of FCM, the Bacterial Foraging Algorithm was employed to optimize the objective criterion function which is interrelated to centroids in FCM. To evaluate the validation of the composite algorithm, cluster validation indexes were used to obtain numerical results and guide the possible best solution found by BF-FCM. Several experiments were conducted on three UCI data sets. For image segmentation, BF-FCM successfully segmented 8 typical grey scale images, and most of them obtained the desired effects. All the experiment results show that BF-FCM has better performance than that of standard FCM.

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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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Soumya, M. "Brain Tumor Image Analysis Using FCM with Enhanced BFA Algorithm." International Academic Journal of Science and Engineering 9, no. 2 (November 7, 2022): 74–82. http://dx.doi.org/10.9756/iajse/v9i2/iajse0917.

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In this particular piece of research, we will be integrating the clustering technique with the segmentation algorithm. The fuzzy c means algorithm was utilized for the clustering, while the bacterial foraging algorithm was employed for the segmentation. An algorithm for optimizing bacterial foraging that takes into account the dynamics of infections as shown in medical imaging. The objective function was optimized by the use of bacterial foraging. Cluster validation indexes were applied in order to get the numerical results and best solution discovered by BF-FCM. This was done so that the validation of the composite algorithm could be examined. The newly developed algorithm BF-FCM was able to segment the images of the brain tumor and locate the various components of the tumor. The findings of the trial indicate that the BF-FCM has superior performance.

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Shamsi, Mousa, and MohamadAmin Bakhshali. "Facial skin segmentation using bacterial foraging optimization algorithm." Journal of Medical Signals & Sensors 2, no. 4 (2012): 203. http://dx.doi.org/10.4103/2228-7477.110331.

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