573 research outputs found

    Novel Bacteria Foraging Optimization for Energy-efficient Communication in Wireless Sensor Network

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    Optimization techniques based on Swarm-intelligence has been reported to have significant benefits towards addressing communication issues in Wireless Sensor Network (WSN). We reviewed the most dominant swarm intelligence technique called as Bacteria Foraging Optimization (BFO) to find that there are very less significant model towards addressing the problems in WSN. Therefore, the proposed paper introduced a novel BFO algorithm which maintains a very good balance between the computational and communication demands of a sensor node unlike the conventional BFO algorithms. The significant contribution of the proposed study is to minimize the iterative steps and inclusion of minimization of both receiving / transmittance power in entire data aggregation process. The study outcome when compared with standard energy-efficient algorithm was found to offer superior network lifetime in terms of higher residual energy as well as data transmission performance

    Bacterial-foraging optimization algorithm for non-hazardous plant layouts

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    PresentationThe following article approaches a safe plant layout design problem based on a bacterial-foraging optimization algorithm. Our approach finds the position in the two dimensional plane for each main process unit and evaluates the possibility of secondary contention for pertinent units, in order to minimize capital costs associated to equipment loss, piping, secondary contention, and usage of area. Fire and Explosion hazard is considered as the relevant safety aspect for distribution, and it is assessed through Dow’s Fire and Explosion Index. The proposed solution approach provides an alternative to hard-optimization methods, by allowing greater flexibility in accounting for both safety and economic aspects, while providing high quality solutions in a limited computation time. The aim of our proposed solution approach is to provide support to expert decision-making during the early plant layout design steps. A case study based on an acrylic-acid production plant, which has been used by several other papers that appeared in the literature, serves the purposes of showing the appropriateness and effectiveness of the method

    Embodied Evolution in Collective Robotics: A Review

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    This paper provides an overview of evolutionary robotics techniques applied to on-line distributed evolution for robot collectives -- namely, embodied evolution. It provides a definition of embodied evolution as well as a thorough description of the underlying concepts and mechanisms. The paper also presents a comprehensive summary of research published in the field since its inception (1999-2017), providing various perspectives to identify the major trends. In particular, we identify a shift from considering embodied evolution as a parallel search method within small robot collectives (fewer than 10 robots) to embodied evolution as an on-line distributed learning method for designing collective behaviours in swarm-like collectives. The paper concludes with a discussion of applications and open questions, providing a milestone for past and an inspiration for future research.Comment: 23 pages, 1 figure, 1 tabl

    Reactive scheduling to treat disruptive events in the MRCPSP

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    Esta tesis se centra en diseñar y desarrollar una metodología para abordar el MRCPSP con diversas funciones objetivo y diferentes tipos de interrupciones. En esta tesis se exploran el MRCPSP con dos funciones objetivo, a saber: (1) minimizar la duración del proyecto y (2) maximizar el valor presente neto del proyecto. Luego, se tiene en cuenta dos tipos diferentes de interrupciones, (a) interrupción de duración, e (b) interrupción de recurso renovable. Para resolver el MRCPSP, en esta tesis se proponen tres estrategias metaheurísticas: (1) algoritmo memético para minimizar la duración del proyecto, (2) algoritmo adaptativo de forrajeo bacteriano para maximizar el valor presente neto del proyecto y (3) algoritmo de optimización multiobjetivo de forrajeo bacteriano (MBFO) para resolver el MRCPSP con eventos de interrupción. Para juzgar el rendimiento del algoritmo memético y de forrajeo bacteriano propuestos, se ha llevado a cabo un extenso análisis basado en diseño factorial y diseño Taguchi para controlar y optimizar los parámetros del algoritmo. Además se han puesto a prueba resolviendo las instancias de los conjuntos más importantes en la literatura: PSPLIB (10,12,14,16,18,20 y 30 actividades) y MMLIB (50 y 100 actividades). También se ha demostrado la superioridad de los algoritmos metaheurísticos propuestos sobre otros enfoques heurísticos y metaheurísticos del estado del arte. A partir de los estudios experimentales se ha ajustado la MBFO, utilizando un caso de estudio.DoctoradoDoctor en Ingeniería Industria

    A stochastic neuronal model predicts random search behaviors at multiple spatial scales in C. elegans

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    Random search is a behavioral strategy used by organisms from bacteria to humans to locate food that is randomly distributed and undetectable at a distance. We investigated this behavior in the nematode Caenorhabditis elegans, an organism with a small, well-described nervous system. Here we formulate a mathematical model of random search abstracted from the C. elegans connectome and fit to a large-scale kinematic analysis of C. elegans behavior at submicron resolution. The model predicts behavioral effects of neuronal ablations and genetic perturbations, as well as unexpected aspects of wild type behavior. The predictive success of the model indicates that random search in C. elegans can be understood in terms of a neuronal flip-flop circuit involving reciprocal inhibition between two populations of stochastic neurons. Our findings establish a unified theoretical framework for understanding C. elegans locomotion and a testable neuronal model of random search that can be applied to other organisms

    Modeling the ballistic-to-diffusive transition in nematode motility reveals variation in exploratory behavior across species

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    A quantitative understanding of organism-level behavior requires predictive models that can capture the richness of behavioral phenotypes, yet are simple enough to connect with underlying mechanistic processes. Here we investigate the motile behavior of nematodes at the level of their translational motion on surfaces driven by undulatory propulsion. We broadly sample the nematode behavioral repertoire by measuring motile trajectories of the canonical lab strain C.elegansC. elegans N2 as well as wild strains and distant species. We focus on trajectory dynamics over timescales spanning the transition from ballistic (straight) to diffusive (random) movement and find that salient features of the motility statistics are captured by a random walk model with independent dynamics in the speed, bearing and reversal events. We show that the model parameters vary among species in a correlated, low-dimensional manner suggestive of a common mode of behavioral control and a trade-off between exploration and exploitation. The distribution of phenotypes along this primary mode of variation reveals that not only the mean but also the variance varies considerably across strains, suggesting that these nematode lineages employ contrasting ``bet-hedging'' strategies for foraging.Comment: 46 pages, 18 figures, 6 table

    Modelling and control of a novel structure two-wheeled robot with an extendable intermediate body

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    A study on an integrated observation and collision avoiding support system for merchant ships

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    東京海洋大学博士学位論文 平成23年度(2011) 応用環境システム学 課程博士 甲第253号指導教員: 大津皓平全文公表年月日: 2016-12-13東京海洋大学201
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