Relevant bibliographies by topics / BACTERIAL FORAGING

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Academic literature on the topic 'BACTERIAL FORAGING'

Author: Grafiati
Published: 11 September 2023
Last updated: 25 July 2025

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Journal articles on the topic "BACTERIAL FORAGING"

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

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The bacterial foraging optimization (BFO) algorithm mimics how bacteria forage over a landscape of nutrients to perform parallel nongradient optimization. In this article, the author provides a tutorial on BFO, including an overview of the biology of bacterial foraging and the pseudo-code that models this process. The algorithms features are briefly compared to those in genetic algorithms, other bio-inspired methods, and nongradient optimization. The applications and future directions of BFO are also presented.
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Panda, Rutuparna, and Manoj Kumar Naik. "A Crossover Bacterial Foraging Optimization Algorithm." Applied Computational Intelligence and Soft Computing 2012 (2012): 1–7. http://dx.doi.org/10.1155/2012/907853.

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This paper presents a modified bacterial foraging optimization algorithm called crossover bacterial foraging optimization algorithm, which inherits the crossover technique of genetic algorithm. This can be used for improvising the evaluation of optimal objective function values. The idea of using crossover mechanism is to search nearby locations by offspring (50 percent of bacteria), because they are randomly produced at different locations. In the traditional bacterial foraging optimization algorithm, search starts from the same locations (50 percent of bacteria are replicated) which is not 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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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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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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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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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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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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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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Dissertations / Theses on the topic "BACTERIAL FORAGING"

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Vetter, Yves-Alain. "Bacterial foraging with cell-free enzymes /." Thesis, Connect to this title online; UW restricted, 1998. http://hdl.handle.net/1773/11033.

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Tang, W. J. "Optimisation algorithms inspired from modelling of bacterial foraging patterns and their applications." Thesis, University of Liverpool, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.490623.

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Research in biologically-inspired optimisation has been fl<;lurishing over the past decades. This approach adopts a bott0!ll-up viewpoint to understand and mimic certain features of a biological system. It has been proved useful in developing nondeterministic algorithms, such as Evolutionary Algorithms (EAs) and Swarm Intelligence (SI). Bacteria, as the simplest creature in nature, are of particular interest in recent studies. In the past thousands of millions of years, bacteria have exhibited a self-organising behaviour to cope with the natural selection. For example, bacteria have developed
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Nasir, Ahmad. "Bacterial foraging and spiral dynamics based metaheuristic algorithms for global optimisation with engineering applications." Thesis, University of Sheffield, 2014. http://etheses.whiterose.ac.uk/7068/.

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Supriyono, Heru. "Novel bacterial foraging optimisation algorithms with application to modelling and control of flexible manipulator systems." Thesis, University of Sheffield, 2012. http://etheses.whiterose.ac.uk/2122/.

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Biologically-inspired soft-computing algorithms, which were developed by mimicking evolution and foraging techniques of animals in nature, have attracted significant attention of researchers. The works are including the development of the algorithm itself, its modification and its application in broad areas. This thesis presents works on biologically-inspired algorithm based on bacterial foraging algorithm (BFA) and its performance evaluation in modelling and control of dynamic systems. The main aim of the research is to develop new modifications of BFA and its combination with other soft comp
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Ladevèze, Simon. "Functional and structural insights into Glycoside Hydrolase family 130 enzymes : implications in carbohydrate foraging by human gut bacteria." Thesis, Toulouse, INSA, 2015. http://www.theses.fr/2015ISAT0010/document.

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Les relations entre bactéries intestinales, aliments et hôte jouent un rôle crucial dans lemaintien de la santé humaine. La caractérisation fonctionnelle d’Uhgb_MP, une enzyme dela famille 130 des glycoside hydrolases découverte par métagénomique fonctionnelle, arévélé une nouvelle fonction de dégradation par phosphorolyse des polysaccharides de laparoi végétale et des glycanes de l'hôte tapissant l'épithélium intestinal. Les déterminantsmoléculaires de la spécificité d’Uhgb_MP vis-à-vis des mannosides ont été identifiés grâce àla résolution de sa structure cristallographique, sous forme apo e
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Harso, Wahyu [Verfasser], Eckhard [Gutachter] George, Christof [Gutachter] Engels, and Klaus [Gutachter] Dittert. "The mycorrhizal plant root system : foraging activities and interaction with soil bacteria in heterogeneous soil environments / Wahyu Harso. Gutachter: Eckhard George ; Christof Engels ; Klaus Dittert." Berlin : Lebenswissenschaftliche Fakultät, 2016. http://d-nb.info/1112193022/34.

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

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碩士<br>大同大學<br>資訊經營學系(所)<br>101<br>This paper proposes an improved approach involving bacterial foraging optimization algorithm (BFOA) behavior. The new algorithm is called improved bacterial foraging optimization (IBFO). BFOA is a new swarm intelligence technique. Three main BFOA operation are chemotaxis, reproduction and elimination-dispersal, which are applied to global and local random searches. This powerful and effective algorithm has been used to solve various real-world optimization problem. However , BFOA has several shortages: many parameters needed to be set ; tumble angles are gener
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Lin, Guan-Yu, and 林冠喻. "Bacterial foraging for watermarkings applications." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/66p8px.

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碩士<br>國立高雄大學<br>電機工程學系碩士班<br>97<br>In recent years, along with the booming application of the Internet, digital files and associated multimedia contents can be easily acquired in our daily lives. With the inherent characteristics of lossless copying and easy spreading, the intellectual property or ownerships of the multimedia contents have become a rising problem. Data hiding and watermarking techniques aiming at protecting copyright-related issues are of considerable interest in academia and industry. In this thesis, we mainly focus on improving the requirements of watermarking applicati
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Allemneny, Raghuveer. "Bacterial Foraging Based Channel Equalizers." Thesis, 2006. http://ethesis.nitrkl.ac.in/24/1/raghuveer.pdf.

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A channel equalizer is one of the most important subsystems in any digital communication receiver. It is also the subsystem that consumes maximum computation time in the receiver. Traditionally maximum-likelihood sequence estimation (MLSE) was the most popular form of equalizer. Owing to non-stationary characteristics of the communication channel MLSE receivers perform poorly. Under these circumstances ‘Maximum A-posteriori Probability (MAP)’ receivers also called Bayesian receivers perform better. Natural selection tends to eliminate animals with poor “foraging strategies” and favor th
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Cheng, Hsiu-Tzu, and 鄭秀姿. "Bacterial Foraging Optimization for Portfolio Optimizations." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/79562681397598793645.

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碩士<br>大同大學<br>資訊經營學系(所)<br>100<br>Portfolio optimization (PO) is a mixed quadratic and integer programming problem, and an effective solution approach is essential for most investors in order to raise expected returns and reduce investment risks. To solve this problem, various heuristic algorithms, such as genetic algorithms and particle swarm optimization, have been proposed in the past. This paper aims to examine the potential of bacterial foraging optimization algorithms (BFO) for solving the portfolio optimization problem. Bacterial foraging optimization algorithm is a new swarm intelligen
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Books on the topic "BACTERIAL FORAGING"

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Stephenson, Steven. Secretive Slime Moulds. CSIRO Publishing, 2021. http://dx.doi.org/10.1071/9781486314140.

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Neither plants, nor animals, nor fungi, the myxomycetes are a surprisingly diverse and fascinating group of organisms. They spend the majority of their life out of sight as single-celled amoeboid individuals in leaf litter, soil or decaying wood, foraging for bacteria and other simple life forms. However, when conditions are right, two individual cells come together to give rise to a much larger, creeping structure called a plasmodium, which produces the even more complex and often beautiful fruiting bodies. Indeed, the fruiting bodies of myxomycetes are often miniature works of art!&#x0D; The
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Book chapters on the topic "BACTERIAL FORAGING"

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

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

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Parashar, Sonam, Nand K. Meena, Jin Yang, and Neeraj Kanwar. "Bacterial Foraging Optimization." In Swarm Intelligence Algorithms. CRC Press, 2020. http://dx.doi.org/10.1201/9780429422614-3.

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

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

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

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

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Liu, Wei, Yunlong Zhu, Ben Niu, and Hanning Chen. "Optimization Based on Bacterial Colony Foraging." In Communications in Computer and Information Science. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-31837-5_71.

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Chen, Hanning, Yunlong Zhu, Kunyuan Hu, Xiaoxian He, and Ben Niu. "Cooperative Approaches to Bacterial Foraging Optimization." In Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-85984-0_65.

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Kanwar, Neeraj, Nand K. Meena, Jin Yang, and Sonam Parashar. "Modified Bacterial Foraging Optimization and Application." In Swarm Intelligence Algorithms. CRC Press, 2020. http://dx.doi.org/10.1201/9780429422607-3.

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Conference papers on the topic "BACTERIAL FORAGING"

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Kumar, Dev, Sandeep Kumar Sunori, and Shilpa Jain. "Bacterial Foraging Optimization of Electric Vehicle Charging Systems." In 2024 International Conference on Sustainable Communication Networks and Application (ICSCNA). IEEE, 2024. https://doi.org/10.1109/icscna63714.2024.10863835.

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

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

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

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

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

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

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

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

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

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