Reliability-Based and Cost-Oriented Product Optimization Integrating Fuzzy Reasoning Petri Nets, Interval Expert Evaluation and Cultural-Based DMOPSO Using Crowding Distance Sorting

Directory of Open Access Journals (Sweden)

Zhaoxi Hong

2017-08-01

Full Text Available In reliability-based and cost-oriented product optimization, the target product reliability is apportioned to subsystems or components to achieve the maximum reliability and minimum cost. Main challenges to conducting such optimization design lie in how to simultaneously consider subsystem division, uncertain evaluation provided by experts for essential factors, and dynamic propagation of product failure. To overcome these problems, a reliability-based and cost-oriented product optimization method integrating fuzzy reasoning Petri net (FRPN, interval expert evaluation and cultural-based dynamic multi-objective particle swarm optimization (DMOPSO using crowding distance sorting is proposed in this paper. Subsystem division is performed based on failure decoupling, and then subsystem weights are calculated with FRPN reflecting dynamic and uncertain failure propagation, as well as interval expert evaluation considering six essential factors. A mathematical model of reliability-based and cost-oriented product optimization is established, and the cultural-based DMOPSO with crowding distance sorting is utilized to obtain the optimized design scheme. The efficiency and effectiveness of the proposed method are demonstrated by the numerical example of the optimization design for a computer numerically controlled (CNC machine tool.

Swarm Intelligence for Urban Dynamics Modelling

International Nuclear Information System (INIS)

Ghnemat, Rawan; Bertelle, Cyrille; Duchamp, Gerard H. E.

2009-01-01

In this paper, we propose swarm intelligence algorithms to deal with dynamical and spatial organization emergence. The goal is to model and simulate the developement of spatial centers using multi-criteria. We combine a decentralized approach based on emergent clustering mixed with spatial constraints or attractions. We propose an extension of the ant nest building algorithm with multi-center and adaptive process. Typically, this model is suitable to analyse and simulate urban dynamics like gentrification or the dynamics of the cultural equipment in urban area.

Swarm Intelligence for Urban Dynamics Modelling

Science.gov (United States)

Ghnemat, Rawan; Bertelle, Cyrille; Duchamp, Gérard H. E.

2009-04-01

In this paper, we propose swarm intelligence algorithms to deal with dynamical and spatial organization emergence. The goal is to model and simulate the developement of spatial centers using multi-criteria. We combine a decentralized approach based on emergent clustering mixed with spatial constraints or attractions. We propose an extension of the ant nest building algorithm with multi-center and adaptive process. Typically, this model is suitable to analyse and simulate urban dynamics like gentrification or the dynamics of the cultural equipment in urban area.

Turbulence modulation induced by bubble swarm in oscillating-grid turbulence

International Nuclear Information System (INIS)

Morikawa, Koichi; Urano, Shigeyuki; Saito, Takayuki

2007-01-01

In the present study, liquid-phase turbulence modulation induced by a bubble swarm ascending in arbitrary turbulence was experimentally investigated. Liquid-phase homogeneous isotropic turbulence was formed using an oscillating grid in a cylindrical acrylic vessel of 149 mm in inner diameter. A bubble swarm consisting of 19 bubbles of 2.8 mm in equivalent diameter was examined; the bubble size and launching time were completely controlled using a bubble launching device through audio speakers. This bubble launching device was able to repeatedly control the bubble swarm arbitrarily and precisely. The bubble swarm was launched at a frequency of 4 Hz. The liquid phase motion was measured via two LDA (Laser Doppler Anemometer) probes. The turbulence intensity, spatial correlation and integral scale were calculated from LDA data obtained by the two spatially-separate-point measurement. When the bubble swarm was added, the turbulence intensity dramatically changed. The original isotropic turbulence was modulated to the anisotropic turbulence by the mutual interference between the bubble swarm and ambient isotropic turbulence. The integral scales were calculated from the spatial correlation function. The effects of the bubble swarm on the integral scales showed the tendencies similar to those on turbulence intensity. (author)

Velocity correlations in laboratory insect swarms

Science.gov (United States)

Ni, R.; Ouellette, N. T.

2015-12-01

In contrast to animal groups such as bird flocks or migratory herds that display net, directed motion, insect swarms do not possess global order. Without such order, it is difficult to define and characterize the transition to collective behavior in swarms; nevertheless, visual observation of swarms strongly suggests that swarming insects do behave collectively. It has recently been suggested that correlation rather than order is the hallmark of emergent collective behavior. Here, we report measurements of spatial velocity correlation functions in laboratory mating swarms of the non-biting midge Chironomus riparius. Although we find some correlation at short distances, our swarms are in general only weakly correlated, in contrast to what has been observed in field studies. Our results hint at the potentially important role of environmental conditions on collective behavior, and suggest that general indicators of the collective nature of swarming are still needed.

Swarm Intelligence systems

International Nuclear Information System (INIS)

Beni, G.

1994-01-01

We review the characteristics of Swarm Intelligence and discuss systems exhibiting it. The recently developed mathematical description of Swarm behavior is also reviewed and discussed. The self-organization of Swarms is described as the reconfiguring asynchronously and conservatively of a distribution. Swarm reconfigurations are based on producing distributions that are solutions to systems of linear equations. Conservation and asynchronicity are related, respectively, to the global and local nature of the Swarm problem. The conditions for the convergence of the Swarm algorithm are presented. The important point is that, under very general conditions, the Swarm reconfigures in a time which is independent of the size of the Swarm. This fact implies that a centralized controller can never reconfigure as fast as a Swarm provided the size of the Swarm is large enough. This result is related to the unpredictability of the Swarm, a basic property of Swarm Intelligence. Finally, the conditions under which Swarm algorithms become of practical importance are discussed and examples given. (author)

Spatial sorting unlikely to promote maladaptive hybridization: response to Lowe, Muhlfeld, and Allendorf

Czech Academy of Sciences Publication Activity Database

Phillips, B. L.; Baird, Stuart J. E.

2015-01-01

Roč. 30, č. 10 (2015), s. 564-565 ISSN 0169-5347 Institutional support: RVO:68081766 Keywords : Spatial sorting * Maladaptive hybridisation * Moving hybrid zones * Introgression * Hybrid invasion Subject RIV: EB - Genetics ; Molecular Biology Impact factor: 16.735, year: 2015

Continuous spatial public goods game with self and peer punishment based on particle swarm optimization

Science.gov (United States)

Quan, Ji; Yang, Xiukang; Wang, Xianjia

2018-07-01

How cooperative behavior emerges and evolves in human society remains a puzzle. It has been observed that the sense of guilt rooted from free-riding and the sense of justice for punishing the free-riders are prevalent in the real world. Inspired by this observation, two punishment mechanisms have been introduced in the spatial public goods game which are called self-punishment and peer punishment respectively in this paper. In each situation, we have introduced a corresponding parameter to describe the level of individual tolerance or social tolerance. For each individual, whether to punish others or whether it will be punished by others depends on the corresponding tolerance parameter. We focus on the effects of the two kinds of tolerance parameters on the cooperation of the population. The particle swarm optimization (PSO)-based learning rule is used to describe the strategy updating process of individuals. We consider both of the memory and the imitation in our model. Via simulation experiments, we find that both of the two punishment mechanisms could facilitate the promotion of cooperation to a large extent. For the self-punishment and for most parameters in the peer punishment, the smaller the tolerance parameter, the more conducive it is to promote cooperation. These results can help us to better understand the prevailing phenomenon of cooperation in the real world.

An algorithm for 4D CT image sorting using spatial continuity.

Science.gov (United States)

Li, Chen; Liu, Jie

2013-01-01

4D CT, which could locate the position of the movement of the tumor in the entire respiratory cycle and reduce image artifacts effectively, has been widely used in making radiation therapy of tumors. The current 4D CT methods required external surrogates of respiratory motion obtained from extra instruments. However, respiratory signals recorded by these external makers may not always accurately represent the internal tumor and organ movements, especially when irregular breathing patterns happened. In this paper we have proposed a novel automatic 4D CT sorting algorithm that performs without these external surrogates. The sorting algorithm requires collecting the image data with a cine scan protocol. Beginning with the first couch position, images from the adjacent couch position are selected out according to spatial continuity. The process is continued until images from all couch positions are sorted and the entire 3D volume is produced. The algorithm is verified by respiratory phantom image data and clinical image data. The primary test results show that the 4D CT images created by our algorithm have eliminated the motion artifacts effectively and clearly demonstrated the movement of tumor and organ in the breath period.

A Swarm-Based Learning Method Inspired by Social Insects

Science.gov (United States)

He, Xiaoxian; Zhu, Yunlong; Hu, Kunyuan; Niu, Ben

Inspired by cooperative transport behaviors of ants, on the basis of Q-learning, a new learning method, Neighbor-Information-Reference (NIR) learning method, is present in the paper. This is a swarm-based learning method, in which principles of swarm intelligence are strictly complied with. In NIR learning, the i-interval neighbor's information, namely its discounted reward, is referenced when an individual selects the next state, so that it can make the best decision in a computable local neighborhood. In application, different policies of NIR learning are recommended by controlling the parameters according to time-relativity of concrete tasks. NIR learning can remarkably improve individual efficiency, and make swarm more "intelligent".

4D CT sorting based on patient internal anatomy

Science.gov (United States)

Li, Ruijiang; Lewis, John H.; Cerviño, Laura I.; Jiang, Steve B.

2009-08-01

Respiratory motion during free-breathing computed tomography (CT) scan may cause significant errors in target definition for tumors in the thorax and upper abdomen. A four-dimensional (4D) CT technique has been widely used for treatment simulation of thoracic and abdominal cancer radiotherapy. The current 4D CT techniques require retrospective sorting of the reconstructed CT slices oversampled at the same couch position. Most sorting methods depend on external surrogates of respiratory motion recorded by extra instruments. However, respiratory signals obtained from these external surrogates may not always accurately represent the internal target motion, especially when irregular breathing patterns occur. We have proposed a new sorting method based on multiple internal anatomical features for multi-slice CT scan acquired in the cine mode. Four features are analyzed in this study, including the air content, lung area, lung density and body area. We use a measure called spatial coherence to select the optimal internal feature at each couch position and to generate the respiratory signals for 4D CT sorting. The proposed method has been evaluated for ten cancer patients (eight with thoracic cancer and two with abdominal cancer). For nine patients, the respiratory signals generated from the combined internal features are well correlated to those from external surrogates recorded by the real-time position management (RPM) system (average correlation: 0.95 ± 0.02), which is better than any individual internal measures at 95% confidence level. For these nine patients, the 4D CT images sorted by the combined internal features are almost identical to those sorted by the RPM signal. For one patient with an irregular breathing pattern, the respiratory signals given by the combined internal features do not correlate well with those from RPM (correlation: 0.68 ± 0.42). In this case, the 4D CT image sorted by our method presents fewer artifacts than that from the RPM signal. Our

Analysis of swarm behaviors based on an inversion of the fluctuation theorem.

Science.gov (United States)

Hamann, Heiko; Schmickl, Thomas; Crailsheim, Karl

2014-01-01

A grand challenge in the field of artificial life is to find a general theory of emergent self-organizing systems. In swarm systems most of the observed complexity is based on motion of simple entities. Similarly, statistical mechanics focuses on collective properties induced by the motion of many interacting particles. In this article we apply methods from statistical mechanics to swarm systems. We try to explain the emergent behavior of a simulated swarm by applying methods based on the fluctuation theorem. Empirical results indicate that swarms are able to produce negative entropy within an isolated subsystem due to frozen accidents. Individuals of a swarm are able to locally detect fluctuations of the global entropy measure and store them, if they are negative entropy productions. By accumulating these stored fluctuations over time the swarm as a whole is producing negative entropy and the system ends up in an ordered state. We claim that this indicates the existence of an inverted fluctuation theorem for emergent self-organizing dissipative systems. This approach bears the potential of general applicability.

Swarm-based Sequencing Recommendations in E-learning

NARCIS (Netherlands)

Van den Berg, Bert; Tattersall, Colin; Janssen, José; Brouns, Francis; Kurvers, Hub; Koper, Rob

2005-01-01

Van den Berg, B., Tattersall, C., Janssen, J., Brouns, F., Kurvers, H., & Koper, R. (2006). Swarm-based Sequencing Recommendations in E-learning. International Journal of Computer Science & Applications, III(III), 1-11.

Decision-making in honeybee swarms based on quality and distance information of candidate nest sites.

Science.gov (United States)

Laomettachit, Teeraphan; Termsaithong, Teerasit; Sae-Tang, Anuwat; Duangphakdee, Orawan

2015-01-07

In the nest-site selection process of honeybee swarms, an individual bee performs a waggle dance to communicate information about direction, quality, and distance of a discovered site to other bees at the swarm. Initially, different groups of bees dance to represent different potential sites, but eventually the swarm usually reaches an agreement for only one site. Here, we model the nest-site selection process in honeybee swarms of Apis mellifera and show how the swarms make adaptive decisions based on a trade-off between the quality and distance to candidate nest sites. We use bifurcation analysis and stochastic simulations to reveal that the swarm's site distance preference is moderate>near>far when the swarms choose between low quality sites. However, the distance preference becomes near>moderate>far when the swarms choose between high quality sites. Our simulations also indicate that swarms with large population size prefer nearer sites and, in addition, are more adaptive at making decisions based on available information compared to swarms with smaller population size. Copyright © 2014 Elsevier Ltd. All rights reserved.

DualTrust: A Trust Management Model for Swarm-Based Autonomic Computing Systems

Energy Technology Data Exchange (ETDEWEB)

Maiden, Wendy M. [Washington State Univ., Pullman, WA (United States)

2010-05-01

Trust management techniques must be adapted to the unique needs of the application architectures and problem domains to which they are applied. For autonomic computing systems that utilize mobile agents and ant colony algorithms for their sensor layer, certain characteristics of the mobile agent ant swarm -- their lightweight, ephemeral nature and indirect communication -- make this adaptation especially challenging. This thesis looks at the trust issues and opportunities in swarm-based autonomic computing systems and finds that by monitoring the trustworthiness of the autonomic managers rather than the swarming sensors, the trust management problem becomes much more scalable and still serves to protect the swarm. After analyzing the applicability of trust management research as it has been applied to architectures with similar characteristics, this thesis specifies the required characteristics for trust management mechanisms used to monitor the trustworthiness of entities in a swarm-based autonomic computing system and describes a trust model that meets these requirements.

An Orthogonal Multi-Swarm Cooperative PSO Algorithm with a Particle Trajectory Knowledge Base

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Jun Yang

2017-01-01

Full Text Available A novel orthogonal multi-swarm cooperative particle swarm optimization (PSO algorithm with a particle trajectory knowledge base is presented in this paper. Different from the traditional PSO algorithms and other variants of PSO, the proposed orthogonal multi-swarm cooperative PSO algorithm not only introduces an orthogonal initialization mechanism and a particle trajectory knowledge base for multi-dimensional optimization problems, but also conceives a new adaptive cooperation mechanism to accomplish the information interaction among swarms and particles. Experiments are conducted on a set of benchmark functions, and the results show its better performance compared with traditional PSO algorithm in aspects of convergence, computational efficiency and avoiding premature convergence.

Mafic microgranular enclave swarms in the Chenar granitoid stock, NW of Kerman, Iran: evidence for magma mingling

Science.gov (United States)

Arvin, M.; Dargahi, S.; Babaei, A. A.

2004-10-01

Mafic microgranular enclaves (MME) are common in the Early to Middle Miocene Chenar granitoid stock, northwest of Kerman, which is a part of Central Iranian Eocene volcanic belt. They occur individually and in homogeneous or heterogeneous swarms. The MME form a number of two-dimensional structural arrangements, such as dykes, small rafts, vortices, folded lens-shapes and late swarms. The enclaves are elongated, rounded to non-elongated and subrounded in shape and often show some size-sorting parallel to direction of flow. Variation in the elongation of enclaves could reflect variations in the viscosity of the enclave, the time available for enclave deformation and differential strain during flow of the host granitoid magma. The most effective mechanism in the formation of enclave swarms in the Chenar granitoid stock was velocity gradient-related convection currents in the granitoid magma chamber. Gravitational sorting and the break-up of heterogeneous dykes also form MME swarms. The MME (mainly diorite to diorite gabbro) have igneous mineralogy and texture, and are marked by sharp contacts next to their host granitoid rocks. The contact is often marked by a chilled margin with no sign of solid state deformation. Evidence of disequilibrium is manifested in feldspars by oscillatory zoning, resorbed rims, mantling and punctuated growth, together with overgrowth of clinopyroxene/amphibole on quartz crystals, the acicular habit of apatites and the development of Fe-Ti oxides along clinopyroxene cleavages. These observations suggest that the MMEs are derived from a hybrid-magma formed as a result of the intrusion of a mafic magma into the base of a felsic magma chamber. The density contrast between hybrid-magma and the overlying felsic magma was reduced by the release of dissolved fluids and the ascent of exsolved gas bubbles from the mafic magma into the hybrid zone. Further convection in the magma chamber dispersed the hybridized magma as globules in the upper parts of

Swarm-Aurora: A web-based tool for quickly identifying multi-instrument auroral events

Science.gov (United States)

Chaddock, D.; Donovan, E.; Spanswick, E.; Knudsen, D. J.; Frey, H. U.; Kauristie, K.; Partamies, N.; Jackel, B. J.; Gillies, M.; Holmdahl Olsen, P. E.

2016-12-01

In recent years there has been a dramatic increase in ground-based auroral imaging systems. These include the continent-wide THEMIS-ASI network, and imagers operated by other programs including GO-Canada, MIRACLE, AGO, OMTI, and more. In the near future, a new Canadian program called TREx will see the deployment of new narrow-band ASIs that will provide multi-wavelength imaging across Western Canada. At the same time, there is an unprecedented fleet of international spacecraft probing geospace at low and high altitudes. We are now in the position to simultaneously observe the magnetospheric drivers of aurora, observe in situ the waves, currents, and particles associated with MI coupling, and the conjugate aurora. Whereas a decade ago, a single magnetic conjunction between one ASI and a low altitude satellite was a relatively rare event, we now have a plethora of triple conjunctions between imagers, low-altitude spacecraft, and near-equatorial magnetospheric probes. But with these riches comes a new level of complexity. It is often difficult to identify the many useful conjunctions for a specific line of inquiry from the multitude of conjunctions where the geospace conditions are often not relevant and/or the imaging is compromised by clouds, moon, or other factors. Swarm-Aurora was designed to facilitate and drive the use of Swarm in situ measurements in auroral science. The project seeks to build a bridge between the Swarm science community, Swarm data, and the complimentary auroral data and community. Swarm-Aurora (http://swarm-aurora.phys.ucalgary.ca) incorporates a web-based tool which provides access to quick-look summary data for a large array of instruments, with Swarm in situ and ground-based ASI data as the primary focus. This web interface allows researchers to quickly and efficiently browse Swarm and ASI data to identify auroral events of interest to them. This allows researchers to be able to easily and quickly identify Swarm overflights of ASIs that

A distance weighted-based approach for self-organized aggregation in robot swarms

KAUST Repository

Khaldi, Belkacem

2017-12-14

In this paper, a Distance-Weighted K Nearest Neighboring (DW-KNN) topology is proposed to study self-organized aggregation as an emergent swarming behavior within robot swarms. A virtual physics approach is applied among the proposed neighborhood topology to keep the robots together. A distance-weighted function based on a Smoothed Particle Hydrodynamic (SPH) interpolation approach is used as a key factor to identify the K-Nearest neighbors taken into account when aggregating the robots. The intra virtual physical connectivity among these neighbors is achieved using a virtual viscoelastic-based proximity model. With the ARGoS based-simulator, we model and evaluate the proposed approach showing various self-organized aggregations performed by a swarm of N foot-bot robots.

Lifecycle-Based Swarm Optimization Method for Numerical Optimization

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Hai Shen

2014-01-01

Full Text Available Bioinspired optimization algorithms have been widely used to solve various scientific and engineering problems. Inspired by biological lifecycle, this paper presents a novel optimization algorithm called lifecycle-based swarm optimization (LSO. Biological lifecycle includes four stages: birth, growth, reproduction, and death. With this process, even though individual organism died, the species will not perish. Furthermore, species will have stronger ability of adaptation to the environment and achieve perfect evolution. LSO simulates Biological lifecycle process through six optimization operators: chemotactic, assimilation, transposition, crossover, selection, and mutation. In addition, the spatial distribution of initialization population meets clumped distribution. Experiments were conducted on unconstrained benchmark optimization problems and mechanical design optimization problems. Unconstrained benchmark problems include both unimodal and multimodal cases the demonstration of the optimal performance and stability, and the mechanical design problem was tested for algorithm practicability. The results demonstrate remarkable performance of the LSO algorithm on all chosen benchmark functions when compared to several successful optimization techniques.

Interacting Brownian Swarms: Some Analytical Results

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Guillaume Sartoretti

2016-01-01

Full Text Available We consider the dynamics of swarms of scalar Brownian agents subject to local imitation mechanisms implemented using mutual rank-based interactions. For appropriate values of the underlying control parameters, the swarm propagates tightly and the distances separating successive agents are iid exponential random variables. Implicitly, the implementation of rank-based mutual interactions, requires that agents have infinite interaction ranges. Using the probabilistic size of the swarm’s support, we analytically estimate the critical interaction range below that flocked swarms cannot survive. In the second part of the paper, we consider the interactions between two flocked swarms of Brownian agents with finite interaction ranges. Both swarms travel with different barycentric velocities, and agents from both swarms indifferently interact with each other. For appropriate initial configurations, both swarms eventually collide (i.e., all agents interact. Depending on the values of the control parameters, one of the following patterns emerges after collision: (i Both swarms remain essentially flocked, or (ii the swarms become ultimately quasi-free and recover their nominal barycentric speeds. We derive a set of analytical flocking conditions based on the generalized rank-based Brownian motion. An extensive set of numerical simulations corroborates our analytical findings.

The Dynamics of Interacting Swarms

Science.gov (United States)

2018-04-04

have been used as a means of realistically modeling swarming behaviors [26, 38, 44]. Systematic numerical studies of discrete flocking based on...The model for the swarm we use is based on the the employed in [9], which describe a mathe - matically swarm model using the Morse potential. Recently

A many-sorted calculus based on resolution and paramodulation

CERN Document Server

Walther, Christoph

1987-01-01

A Many-Sorted Calculus Based on Resolution and Paramodulation emphasizes the utilization of advantages and concepts of many-sorted logic for resolution and paramodulation based automated theorem proving.This book considers some first-order calculus that defines how theorems from given hypotheses by pure syntactic reasoning are obtained, shifting all the semantic and implicit argumentation to the syntactic and explicit level of formal first-order reasoning. This text discusses the efficiency of many-sorted reasoning, formal preliminaries for the RP- and ?RP-calculus, and many-sorted term rewrit

Swarm intelligence-based approach for optimal design of CMOS differential amplifier and comparator circuit using a hybrid salp swarm algorithm

Science.gov (United States)

Asaithambi, Sasikumar; Rajappa, Muthaiah

2018-05-01

In this paper, an automatic design method based on a swarm intelligence approach for CMOS analog integrated circuit (IC) design is presented. The hybrid meta-heuristics optimization technique, namely, the salp swarm algorithm (SSA), is applied to the optimal sizing of a CMOS differential amplifier and the comparator circuit. SSA is a nature-inspired optimization algorithm which mimics the navigating and hunting behavior of salp. The hybrid SSA is applied to optimize the circuit design parameters and to minimize the MOS transistor sizes. The proposed swarm intelligence approach was successfully implemented for an automatic design and optimization of CMOS analog ICs using Generic Process Design Kit (GPDK) 180 nm technology. The circuit design parameters and design specifications are validated through a simulation program for integrated circuit emphasis simulator. To investigate the efficiency of the proposed approach, comparisons have been carried out with other simulation-based circuit design methods. The performances of hybrid SSA based CMOS analog IC designs are better than the previously reported studies.

Details of microearthquake swarms in the Columbia basin, Washington

International Nuclear Information System (INIS)

Malone, S.D.; Rothe, G.H.; Smith, S.W.

1975-01-01

Three microearthquake swarms in the Columbia River basin of eastern Washington were studied by means of a small portable seismic network. Earthquakes in this area typically occur in swarms, concentrated both temporally and spatially. One unusual characteristic of the three swarms studied was the shallow focal depths of all events. Most events located had depths less than 1 km; none were deeper than 2 km. Composite focal mechanism solutions indicate that more than one fault surface is active in any one swarm. All events had some thrust component with the axis of maximum compression oriented roughly in a north-south direction. (auth)

Swarm-based adaptation: wayfinding support for lifelong learners

NARCIS (Netherlands)

Tattersall, Colin; Van den Berg, Bert; Van Es, René; Janssen, José; Manderveld, Jocelyn; Koper, Rob

2004-01-01

Please refer to the orinigal publication in: Tattersall, C. Van den Berg, B., Van Es, R., Janssen, J., Manderveld, J., Koper, R. (2004). Swarm-based adaptation: wayfinding support for lifelong learners. In P. de Bra & W. Nejdl, Adaptive Hypermedia and Adaptive Web-Based Systems (LNCS3137), (pp.

Research on Multiple Particle Swarm Algorithm Based on Analysis of Scientific Materials

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Zhao Hongwei

2017-01-01

Full Text Available This paper proposed an improved particle swarm optimization algorithm based on analysis of scientific materials. The core thesis of MPSO (Multiple Particle Swarm Algorithm is to improve the single population PSO to interactive multi-swarms, which is used to settle the problem of being trapped into local minima during later iterations because it is lack of diversity. The simulation results show that the convergence rate is fast and the search performance is good, and it has achieved very good results.

Particle Swarm Optimization

Science.gov (United States)

Venter, Gerhard; Sobieszczanski-Sobieski Jaroslaw

2002-01-01

The purpose of this paper is to show how the search algorithm known as particle swarm optimization performs. Here, particle swarm optimization is applied to structural design problems, but the method has a much wider range of possible applications. The paper's new contributions are improvements to the particle swarm optimization algorithm and conclusions and recommendations as to the utility of the algorithm, Results of numerical experiments for both continuous and discrete applications are presented in the paper. The results indicate that the particle swarm optimization algorithm does locate the constrained minimum design in continuous applications with very good precision, albeit at a much higher computational cost than that of a typical gradient based optimizer. However, the true potential of particle swarm optimization is primarily in applications with discrete and/or discontinuous functions and variables. Additionally, particle swarm optimization has the potential of efficient computation with very large numbers of concurrently operating processors.

Log-Linear Model Based Behavior Selection Method for Artificial Fish Swarm Algorithm

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Zhehuang Huang

2015-01-01

Full Text Available Artificial fish swarm algorithm (AFSA is a population based optimization technique inspired by social behavior of fishes. In past several years, AFSA has been successfully applied in many research and application areas. The behavior of fishes has a crucial impact on the performance of AFSA, such as global exploration ability and convergence speed. How to construct and select behaviors of fishes are an important task. To solve these problems, an improved artificial fish swarm algorithm based on log-linear model is proposed and implemented in this paper. There are three main works. Firstly, we proposed a new behavior selection algorithm based on log-linear model which can enhance decision making ability of behavior selection. Secondly, adaptive movement behavior based on adaptive weight is presented, which can dynamically adjust according to the diversity of fishes. Finally, some new behaviors are defined and introduced into artificial fish swarm algorithm at the first time to improve global optimization capability. The experiments on high dimensional function optimization showed that the improved algorithm has more powerful global exploration ability and reasonable convergence speed compared with the standard artificial fish swarm algorithm.

Log-linear model based behavior selection method for artificial fish swarm algorithm.

Science.gov (United States)

Huang, Zhehuang; Chen, Yidong

2015-01-01

Artificial fish swarm algorithm (AFSA) is a population based optimization technique inspired by social behavior of fishes. In past several years, AFSA has been successfully applied in many research and application areas. The behavior of fishes has a crucial impact on the performance of AFSA, such as global exploration ability and convergence speed. How to construct and select behaviors of fishes are an important task. To solve these problems, an improved artificial fish swarm algorithm based on log-linear model is proposed and implemented in this paper. There are three main works. Firstly, we proposed a new behavior selection algorithm based on log-linear model which can enhance decision making ability of behavior selection. Secondly, adaptive movement behavior based on adaptive weight is presented, which can dynamically adjust according to the diversity of fishes. Finally, some new behaviors are defined and introduced into artificial fish swarm algorithm at the first time to improve global optimization capability. The experiments on high dimensional function optimization showed that the improved algorithm has more powerful global exploration ability and reasonable convergence speed compared with the standard artificial fish swarm algorithm.

The influence of swarm deformation on the velocity behavior of falling swarms of particles

Science.gov (United States)

Mitchell, C. A.; Pyrak-Nolte, L. J.; Nitsche, L.

2017-12-01

Cohesive particle swarms have been shown to exhibit enhanced sedimentation in fractures for an optimal range of fracture apertures. Within this range, swarms travel farther and faster than a disperse (particulate) solution. This study aims to uncover the physics underlying the enhanced sedimentation. Swarm behavior at low Reynolds number in a quiescent unbounded fluid and between smooth rigid planar boundaries is investigated numerically using direct-summation, particle-mesh (PM) and particle-particle particle-mesh (P3M) methods - based upon mutually interacting viscous point forces (Stokeslet fields). Wall effects are treated with a least-squares boundary singularity method. Sub-structural effects beyond pseudo-liquid behavior (i.e., particle-scale interactions) are approximated by the P3M method much more efficiently than with direct summation. The model parameters are selected from particle swarm experiments to enable comparison. From the simulations, if the initial swarm geometry at release is unaffected by the fracture aperture, no enhanced transport occurs. The swarm velocity as a function of apertures increases monotonically until it asymptotes to the swarm velocity in an open tank. However, if the fracture aperture affects the initial swarm geometry, the swarm velocity no longer exhibits a monotonic behavior. When swarms are released between two parallel smooth walls with very small apertures, the swarm is forced to reorganize and quickly deform, which results in dramatically reduced swarm velocities. At large apertures, the swarm evolution is similar to that of a swarm in open tank and quickly flattens into a slow speed torus. In the optimal aperture range, the swarm maintains a cohesive unit behaving similarly to a falling sphere. Swarms falling in apertures less than or greater than the optimal aperture range, experience a level of anisotropy that considerably decreases velocities. Unraveling the physics that drives swarm behavior in fractured porous

Fuzzy ranking based non-dominated sorting genetic algorithm-II for network overload alleviation

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Pandiarajan K.

2014-09-01

Full Text Available This paper presents an effective method of network overload management in power systems. The three competing objectives 1 generation cost 2 transmission line overload and 3 real power loss are optimized to provide pareto-optimal solutions. A fuzzy ranking based non-dominated sorting genetic algorithm-II (NSGA-II is used to solve this complex nonlinear optimization problem. The minimization of competing objectives is done by generation rescheduling. Fuzzy ranking method is employed to extract the best compromise solution out of the available non-dominated solutions depending upon its highest rank. N-1 contingency analysis is carried out to identify the most severe lines and those lines are selected for outage. The effectiveness of the proposed approach is demonstrated for different contingency cases in IEEE 30 and IEEE 118 bus systems with smooth cost functions and their results are compared with other single objective evolutionary algorithms like Particle swarm optimization (PSO and Differential evolution (DE. Simulation results show the effectiveness of the proposed approach to generate well distributed pareto-optimal non-dominated solutions of multi-objective problem

A measurement-based fault detection approach applied to monitor robots swarm

KAUST Repository

Khaldi, Belkacem

2017-07-10

Swarm robotics requires continuous monitoring to detect abnormal events and to sustain normal operations. Indeed, swarm robotics with one or more faulty robots leads to degradation of performances complying with the target requirements. This paper present an innovative data-driven fault detection method for monitoring robots swarm. The method combines the flexibility of principal component analysis (PCA) models and the greater sensitivity of the exponentially-weighted moving average control chart to incipient changes. We illustrate through simulated data collected from the ARGoS simulator that a significant improvement in fault detection can be obtained by using the proposed methods as compared to the use of the conventional PCA-based methods.

A Two Teraflop Swarm

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Simon Jones

2018-02-01

Full Text Available We introduce the Xpuck swarm, a research platform with an aggregate raw processing power in excess of two teraflops. The swarm uses 16 e-puck robots augmented with custom hardware that uses the substantial CPU and GPU processing power available from modern mobile system-on-chip devices. The augmented robots, called Xpucks, have at least an order of magnitude greater performance than previous swarm robotics platforms. The platform enables new experiments that require high individual robot computation and multiple robots. Uses include online evolution or learning of swarm controllers, simulation for answering what-if questions about possible actions, distributed super-computing for mobile platforms, and real-world applications of swarm robotics that requires image processing, or SLAM. The teraflop swarm could also be used to explore swarming in nature by providing platforms with similar computational power as simple insects. We demonstrate the computational capability of the swarm by implementing a fast physics-based robot simulator and using this within a distributed island model evolutionary system, all hosted on the Xpucks.

A Swarm Optimization Genetic Algorithm Based on Quantum-Behaved Particle Swarm Optimization.

Science.gov (United States)

Sun, Tao; Xu, Ming-Hai

2017-01-01

Quantum-behaved particle swarm optimization (QPSO) algorithm is a variant of the traditional particle swarm optimization (PSO). The QPSO that was originally developed for continuous search spaces outperforms the traditional PSO in search ability. This paper analyzes the main factors that impact the search ability of QPSO and converts the particle movement formula to the mutation condition by introducing the rejection region, thus proposing a new binary algorithm, named swarm optimization genetic algorithm (SOGA), because it is more like genetic algorithm (GA) than PSO in form. SOGA has crossover and mutation operator as GA but does not need to set the crossover and mutation probability, so it has fewer parameters to control. The proposed algorithm was tested with several nonlinear high-dimension functions in the binary search space, and the results were compared with those from BPSO, BQPSO, and GA. The experimental results show that SOGA is distinctly superior to the other three algorithms in terms of solution accuracy and convergence.

Real/binary co-operative and co-evolving swarms based multivariable PID controller design of ball mill pulverizing system

International Nuclear Information System (INIS)

Menhas, Muhammad Ilyas; Fei Minrui; Wang Ling; Qian Lin

2012-01-01

Highlights: ► We extend the concept of co-operation and co-evolution in some PSO variants. ► We use developed co-operative PSOs in multivariable PID controller design/tuning. ► We find that co-operative PSOs converge faster and give high quality solutions. ► Dividing the search space among swarms improves search efficiency. ► The proposed methods allow the practitioner for heterogeneous problem formulation. - Abstract: In this paper, multivariable PID controller design based on cooperative and coevolving multiple swarms is demonstrated. A simplified multi-variable MIMO process model of a ball mill pulverizing system with steady state decoupler is considered. In order to formulate computational models of cooperative and coevolving multiple swarms three different algorithms like real coded PSO, discrete binary PSO (DBPSO) and probability based discrete binary PSO (PBPSO) are employed. Simulations are carried out on three composite functions simultaneously considering multiple objectives. The cooperative and coevolving multiple swarms based results are compared with the results obtained through single swarm based methods like real coded particle swarm optimization (PSO), discrete binary PSO (DBPSO), and probability based discrete binary PSO (PBPSO) algorithms. The cooperative and coevolving swarms based techniques outperform the real coded PSO, PBPSO, and the standard discrete binary PSO (DBPSO) algorithm in escaping from local optima. Furthermore, statistical analysis of the simulation results is performed to calculate the comparative reliability of various techniques. All of the techniques employed are suitable for controller tuning, however, the multiple cooperative and coevolving swarms based results are considerably better in terms of mean fitness, variance of fitness, and success rate in finding a feasible solution in comparison to those obtained using single swarm based methods.

SWARM-BOT: From Concept to Implementation

OpenAIRE

Mondada, F.; Guignard, A.; Bonani, M.; Bär, D.; Lauria, M.; Floreano, D.

2003-01-01

This paper presents a new robotic concept, called SWARM-BOT, based on a swarm of autonomous mobile robots with self-assembling capabilities. SWARM-BOT takes advantage from collective and distributed approaches to ensure robustness to failures and to hard environment conditions in tasks such as navigation, search and transportation in rough terrain. One SWARM-BOT is composed of a number of simpler robots, called s-bots, physically interconnected. The SWARM-BOT is provided with self-assembling...

Gold rush - A swarm dynamics in games

Science.gov (United States)

Zelinka, Ivan; Bukacek, Michal

2017-07-01

This paper is focused on swarm intelligence techniques and its practical use in computer games. The aim is to show how a swarm dynamics can be generated by multiplayer game, then recorded, analyzed and eventually controlled. In this paper we also discuss possibility to use swarm intelligence instead of game players. Based on our previous experiments two games, using swarm algorithms are mentioned briefly here. The first one is strategy game StarCraft: Brood War, and TicTacToe in which SOMA algorithm has also take a role of player against human player. Open research reported here has shown potential benefit of swarm computation in the field of strategy games and players strategy based on swarm behavior record and analysis. We propose new game called Gold Rush as an experimental environment for human or artificial swarm behavior and consequent analysis.

Swarm robotics and complex behaviour of continuum material

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dell'Erba, Ramiro

2018-05-01

In swarm robotics, just as for an animal swarm in nature, one of the aims is to reach and maintain a desired configuration. One of the possibilities for the team, to reach this aim, is to see what its neighbours are doing. This approach generates a rules system governing the movement of the single robot just by reference to neighbour's motion. The same approach is used in position-based dynamics to simulate behaviour of complex continuum materials under deformation. Therefore, in some previous works, we have considered a two-dimensional lattice of particles and calculated its time evolution by using a rules system derived from our experience in swarm robotics. The new position of a particle, like the element of a swarm, is determined by the spatial position of the other particles. No dynamic is considered, but it can be thought as being hidden in the behaviour rules. This method has given good results in some simple situations reproducing the behaviour of deformable bodies under imposed strain. In this paper we try to stress our model to highlight its limits and how they can be improved. Some other, more complex, examples are computed and discussed. Shear test, different lattices, different fracture mechanisms and ASTM shape sample behaviour have been investigated by the software tool we have developed.

Formations of Robotic Swarm: An Artificial Force Based Approach

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Samitha W. Ekanayake

2009-03-01

Full Text Available Cooperative control of multiple mobile robots is an attractive and challenging problem which has drawn considerable attention in the recent past. This paper introduces a scalable decentralized control algorithm to navigate a group of mobile robots (swarm into a predefined shape in 2D space. The proposed architecture uses artificial forces to control mobile agents into the shape and spread them inside the shape while avoiding inter-member collisions. The theoretical analysis of the swarm behavior describes the motion of the complete swarm and individual members in relevant situations. We use computer simulated case studies to verify the theoretical assertions and to demonstrate the robustness of the swarm under external disturbances such as death of agents, change of shape etc. Also the performance of the proposed distributed swarm control architecture was investigated in the presence of realistic implementation issues such as localization errors, communication range limitations, boundedness of forces etc.

Formations of Robotic Swarm: An Artificial Force Based Approach

Directory of Open Access Journals (Sweden)

Samitha W. Ekanayake

2010-09-01

Full Text Available Cooperative control of multiple mobile robots is an attractive and challenging problem which has drawn considerable attention in the recent past. This paper introduces a scalable decentralized control algorithm to navigate a group of mobile robots (swarm into a predefined shape in 2D space. The proposed architecture uses artificial forces to control mobile agents into the shape and spread them inside the shape while avoiding inter-member collisions. The theoretical analysis of the swarm behavior describes the motion of the complete swarm and individual members in relevant situations. We use computer simulated case studies to verify the theoretical assertions and to demonstrate the robustness of the swarm under external disturbances such as death of agents, change of shape etc. Also the performance of the proposed distributed swarm control architecture was investigated in the presence of realistic implementation issues such as localization errors, communication range limitations, boundedness of forces etc.

Formations of Robotic Swarm: An Artificial Force Based Approach

Directory of Open Access Journals (Sweden)

Samitha W. Ekanayake

2009-03-01

Full Text Available Cooperative control of multiple mobile robots is an attractive and challenging problem which has drawn considerable attention in the recent past. This paper introduces a scalable decentralized control algorithm to navigate a group of mobile robots (swarm into a predefined shape in 2D space. The proposed architecture uses artificial forces to control mobile agents into the shape and spread them inside the shape while avoiding inter- member collisions. The theoretical analysis of the swarm behavior describes the motion of the complete swarm and individual members in relevant situations. We use computer simulated case studies to verify the theoretical assertions and to demonstrate the robustness of the swarm under external disturbances such as death of agents, change of shape etc. Also the performance of the proposed distributed swarm control architecture was investigated in the presence of realistic implementation issues such as localization errors, communication range limitations, boundedness of forces etc.

Formations of Robotic Swarm: An Artificial Force Based Approach

Directory of Open Access Journals (Sweden)

Samitha W. Ekanayake

2010-09-01

Full Text Available Cooperative control of multiple mobile robots is an attractive and challenging problem which has drawn considerable attention in the recent past. This paper introduces a scalable decentralized control algorithm to navigate a group of mobile robots (swarm into a predefined shape in 2D space. The proposed architecture uses artificial forces to control mobile agents into the shape and spread them inside the shape while avoiding inter- member collisions. The theoretical analysis of the swarm behavior describes the motion of the complete swarm and individual members in relevant situations. We use computer simulated case studies to verify the theoretical assertions and to demonstrate the robustness of the swarm under external disturbances such as death of agents, change of shape etc. Also the performance of the proposed distributed swarm control architecture was investigated in the presence of realistic implementation issues such as localization errors, communication range limitations, boundedness of forces etc.

Swarm-based wayfinding support in open and distance learning

NARCIS (Netherlands)

Tattersall, Colin; Manderveld, Jocelyn; Van den Berg, Bert; Van Es, René; Janssen, José; Koper, Rob

2005-01-01

Please refer to the original source: Tattersall, C. Manderveld, J., Van den Berg, B., Van Es, R., Janssen, J., & Koper, R. (2005). Swarm-based wayfinding support in open and distance learning. In Alkhalifa, E.M. (Ed). Cognitively Informed Systems: Utilizing Practical Approaches to Enrich Information

Support Vector Machine Based on Adaptive Acceleration Particle Swarm Optimization

Science.gov (United States)

Abdulameer, Mohammed Hasan; Othman, Zulaiha Ali

2014-01-01

Existing face recognition methods utilize particle swarm optimizer (PSO) and opposition based particle swarm optimizer (OPSO) to optimize the parameters of SVM. However, the utilization of random values in the velocity calculation decreases the performance of these techniques; that is, during the velocity computation, we normally use random values for the acceleration coefficients and this creates randomness in the solution. To address this problem, an adaptive acceleration particle swarm optimization (AAPSO) technique is proposed. To evaluate our proposed method, we employ both face and iris recognition based on AAPSO with SVM (AAPSO-SVM). In the face and iris recognition systems, performance is evaluated using two human face databases, YALE and CASIA, and the UBiris dataset. In this method, we initially perform feature extraction and then recognition on the extracted features. In the recognition process, the extracted features are used for SVM training and testing. During the training and testing, the SVM parameters are optimized with the AAPSO technique, and in AAPSO, the acceleration coefficients are computed using the particle fitness values. The parameters in SVM, which are optimized by AAPSO, perform efficiently for both face and iris recognition. A comparative analysis between our proposed AAPSO-SVM and the PSO-SVM technique is presented. PMID:24790584

Support Vector Machine Based on Adaptive Acceleration Particle Swarm Optimization

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Mohammed Hasan Abdulameer

2014-01-01

Full Text Available Existing face recognition methods utilize particle swarm optimizer (PSO and opposition based particle swarm optimizer (OPSO to optimize the parameters of SVM. However, the utilization of random values in the velocity calculation decreases the performance of these techniques; that is, during the velocity computation, we normally use random values for the acceleration coefficients and this creates randomness in the solution. To address this problem, an adaptive acceleration particle swarm optimization (AAPSO technique is proposed. To evaluate our proposed method, we employ both face and iris recognition based on AAPSO with SVM (AAPSO-SVM. In the face and iris recognition systems, performance is evaluated using two human face databases, YALE and CASIA, and the UBiris dataset. In this method, we initially perform feature extraction and then recognition on the extracted features. In the recognition process, the extracted features are used for SVM training and testing. During the training and testing, the SVM parameters are optimized with the AAPSO technique, and in AAPSO, the acceleration coefficients are computed using the particle fitness values. The parameters in SVM, which are optimized by AAPSO, perform efficiently for both face and iris recognition. A comparative analysis between our proposed AAPSO-SVM and the PSO-SVM technique is presented.

Competitive Swarm Optimizer Based Gateway Deployment Algorithm in Cyber-Physical Systems.

Science.gov (United States)

Huang, Shuqiang; Tao, Ming

2017-01-22

Wireless sensor network topology optimization is a highly important issue, and topology control through node selection can improve the efficiency of data forwarding, while saving energy and prolonging lifetime of the network. To address the problem of connecting a wireless sensor network to the Internet in cyber-physical systems, here we propose a geometric gateway deployment based on a competitive swarm optimizer algorithm. The particle swarm optimization (PSO) algorithm has a continuous search feature in the solution space, which makes it suitable for finding the geometric center of gateway deployment; however, its search mechanism is limited to the individual optimum (pbest) and the population optimum (gbest); thus, it easily falls into local optima. In order to improve the particle search mechanism and enhance the search efficiency of the algorithm, we introduce a new competitive swarm optimizer (CSO) algorithm. The CSO search algorithm is based on an inter-particle competition mechanism and can effectively avoid trapping of the population falling into a local optimum. With the improvement of an adaptive opposition-based search and its ability to dynamically parameter adjustments, this algorithm can maintain the diversity of the entire swarm to solve geometric K -center gateway deployment problems. The simulation results show that this CSO algorithm has a good global explorative ability as well as convergence speed and can improve the network quality of service (QoS) level of cyber-physical systems by obtaining a minimum network coverage radius. We also find that the CSO algorithm is more stable, robust and effective in solving the problem of geometric gateway deployment as compared to the PSO or Kmedoids algorithms.

Competitive Swarm Optimizer Based Gateway Deployment Algorithm in Cyber-Physical Systems

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Shuqiang Huang

2017-01-01

Full Text Available Wireless sensor network topology optimization is a highly important issue, and topology control through node selection can improve the efficiency of data forwarding, while saving energy and prolonging lifetime of the network. To address the problem of connecting a wireless sensor network to the Internet in cyber-physical systems, here we propose a geometric gateway deployment based on a competitive swarm optimizer algorithm. The particle swarm optimization (PSO algorithm has a continuous search feature in the solution space, which makes it suitable for finding the geometric center of gateway deployment; however, its search mechanism is limited to the individual optimum (pbest and the population optimum (gbest; thus, it easily falls into local optima. In order to improve the particle search mechanism and enhance the search efficiency of the algorithm, we introduce a new competitive swarm optimizer (CSO algorithm. The CSO search algorithm is based on an inter-particle competition mechanism and can effectively avoid trapping of the population falling into a local optimum. With the improvement of an adaptive opposition-based search and its ability to dynamically parameter adjustments, this algorithm can maintain the diversity of the entire swarm to solve geometric K-center gateway deployment problems. The simulation results show that this CSO algorithm has a good global explorative ability as well as convergence speed and can improve the network quality of service (QoS level of cyber-physical systems by obtaining a minimum network coverage radius. We also find that the CSO algorithm is more stable, robust and effective in solving the problem of geometric gateway deployment as compared to the PSO or Kmedoids algorithms.

Competitive Swarm Optimizer Based Gateway Deployment Algorithm in Cyber-Physical Systems

Science.gov (United States)

Huang, Shuqiang; Tao, Ming

2017-01-01

Wireless sensor network topology optimization is a highly important issue, and topology control through node selection can improve the efficiency of data forwarding, while saving energy and prolonging lifetime of the network. To address the problem of connecting a wireless sensor network to the Internet in cyber-physical systems, here we propose a geometric gateway deployment based on a competitive swarm optimizer algorithm. The particle swarm optimization (PSO) algorithm has a continuous search feature in the solution space, which makes it suitable for finding the geometric center of gateway deployment; however, its search mechanism is limited to the individual optimum (pbest) and the population optimum (gbest); thus, it easily falls into local optima. In order to improve the particle search mechanism and enhance the search efficiency of the algorithm, we introduce a new competitive swarm optimizer (CSO) algorithm. The CSO search algorithm is based on an inter-particle competition mechanism and can effectively avoid trapping of the population falling into a local optimum. With the improvement of an adaptive opposition-based search and its ability to dynamically parameter adjustments, this algorithm can maintain the diversity of the entire swarm to solve geometric K-center gateway deployment problems. The simulation results show that this CSO algorithm has a good global explorative ability as well as convergence speed and can improve the network quality of service (QoS) level of cyber-physical systems by obtaining a minimum network coverage radius. We also find that the CSO algorithm is more stable, robust and effective in solving the problem of geometric gateway deployment as compared to the PSO or Kmedoids algorithms. PMID:28117735

QoE Guarantee Scheme Based on Cooperative Cognitive Cloud and Opportunistic Weight Particle Swarm

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Weihang Shi

2015-01-01

Full Text Available It is well known that the Internet application of cloud services may be affected by the inefficiency of cloud computing and inaccurate evaluation of quality of experience (QoE seriously. In our paper, based on construction algorithms of cooperative cognitive cloud platform and optimization algorithm of opportunities weight particle swarm clustering, the QoE guarantee mechanism was proposed. The mechanism, through the sending users of requests and the cognitive neighbor users’ cooperation, combined the cooperation of subcloud platforms and constructed the optimal cloud platform with the different service. At the same time, the particle swarm optimization algorithm could be enhanced dynamically according to all kinds of opportunity request weight, which could optimize the cooperative cognitive cloud platform. Finally, the QoE guarantee scheme was proposed with the opportunity weight particle swarm optimization algorithm and collaborative cognitive cloud platform. The experimental results show that the proposed mechanism compared is superior to the QoE guarantee scheme based on cooperative cloud and QoE guarantee scheme based on particle swarm optimization, compared with optimization fitness and high cloud computing service execution efficiency and high throughput performance advantages.

Particle Swarm Optimization with Double Learning Patterns.

Science.gov (United States)

Shen, Yuanxia; Wei, Linna; Zeng, Chuanhua; Chen, Jian

2016-01-01

Particle Swarm Optimization (PSO) is an effective tool in solving optimization problems. However, PSO usually suffers from the premature convergence due to the quick losing of the swarm diversity. In this paper, we first analyze the motion behavior of the swarm based on the probability characteristic of learning parameters. Then a PSO with double learning patterns (PSO-DLP) is developed, which employs the master swarm and the slave swarm with different learning patterns to achieve a trade-off between the convergence speed and the swarm diversity. The particles in the master swarm and the slave swarm are encouraged to explore search for keeping the swarm diversity and to learn from the global best particle for refining a promising solution, respectively. When the evolutionary states of two swarms interact, an interaction mechanism is enabled. This mechanism can help the slave swarm in jumping out of the local optima and improve the convergence precision of the master swarm. The proposed PSO-DLP is evaluated on 20 benchmark functions, including rotated multimodal and complex shifted problems. The simulation results and statistical analysis show that PSO-DLP obtains a promising performance and outperforms eight PSO variants.

Particle Swarm Optimization with Double Learning Patterns

Science.gov (United States)

Shen, Yuanxia; Wei, Linna; Zeng, Chuanhua; Chen, Jian

2016-01-01

Particle Swarm Optimization (PSO) is an effective tool in solving optimization problems. However, PSO usually suffers from the premature convergence due to the quick losing of the swarm diversity. In this paper, we first analyze the motion behavior of the swarm based on the probability characteristic of learning parameters. Then a PSO with double learning patterns (PSO-DLP) is developed, which employs the master swarm and the slave swarm with different learning patterns to achieve a trade-off between the convergence speed and the swarm diversity. The particles in the master swarm and the slave swarm are encouraged to explore search for keeping the swarm diversity and to learn from the global best particle for refining a promising solution, respectively. When the evolutionary states of two swarms interact, an interaction mechanism is enabled. This mechanism can help the slave swarm in jumping out of the local optima and improve the convergence precision of the master swarm. The proposed PSO-DLP is evaluated on 20 benchmark functions, including rotated multimodal and complex shifted problems. The simulation results and statistical analysis show that PSO-DLP obtains a promising performance and outperforms eight PSO variants. PMID:26858747

A fluid-driven earthquake swarm on the margin of the Yellowstone caldera

Science.gov (United States)

Shelly, David R.; Hill, David P.; Massin, Frederick; Farrell, Jamie; Smith, Robert B.; Taira, Taka'aki

2013-01-01

Over the past several decades, the Yellowstone caldera has experienced frequent earthquake swarms and repeated cycles of uplift and subsidence, reflecting dynamic volcanic and tectonic processes. Here, we examine the detailed spatial-temporal evolution of the 2010 Madison Plateau swarm, which occurred near the northwest boundary of the Yellowstone caldera. To fully explore the evolution of the swarm, we integrated procedures for seismic waveform-based earthquake detection with precise double-difference relative relocation. Using cross-correlation of continuous seismic data and waveform templates constructed from cataloged events, we detected and precisely located 8710 earthquakes during the three-week swarm, nearly four times the number of events included in the standard catalog. This high-resolution analysis reveals distinct migration of earthquake activity over the course of the swarm. The swarm initiated abruptly on January 17, 2010 at about 10 km depth and expanded dramatically outward (both shallower and deeper) over time, primarily along a NNW-striking, ~55º ENE-dipping structure. To explain these characteristics, we hypothesize that the swarm was triggered by the rupture of a zone of confined high-pressure aqueous fluids into a pre-existing crustal fault system, prompting release of accumulated stress. The high-pressure fluid injection may have been accommodated by hybrid shear and dilatational failure, as is commonly observed in exhumed hydrothermally affected fault zones. This process has likely occurred repeatedly in Yellowstone as aqueous fluids exsolved from magma migrate into the brittle crust, and it may be a key element in the observed cycles of caldera uplift and subsidence.

Microscopy with spatial filtering for sorting particles and monitoring subcellular morphology

Science.gov (United States)

Zheng, Jing-Yi; Qian, Zhen; Pasternack, Robert M.; Boustany, Nada N.

2009-02-01

Optical scatter imaging (OSI) was developed to non-invasively track real-time changes in particle morphology with submicron sensitivity in situ without exogenous labeling, cell fixing, or organelle isolation. For spherical particles, the intensity ratio of wide-to-narrow angle scatter (OSIR, Optical Scatter Image Ratio) was shown to decrease monotonically with diameter and agree with Mie theory. In living cells, we recently reported this technique is able to detect mitochondrial morphological alterations, which were mediated by the Bcl-xL transmembrane domain, and could not be observed by fluorescence or differential interference contrast images. Here we further extend the ability of morphology assessment by adopting a digital micromirror device (DMD) for Fourier filtering. When placed in the Fourier plane the DMD can be used to select scattering intensities at desired combination of scattering angles. We designed an optical filter bank consisting of Gabor-like filters with various scales and rotations based on Gabor filters, which have been widely used for localization of spatial and frequency information in digital images and texture analysis. Using a model system consisting of mixtures of polystyrene spheres and bacteria, we show how this system can be used to sort particles on a microscopic slide based on their size, orientation and aspect ratio. We are currently applying this technique to characterize the morphology of subcellular organelles to help understand fundamental biological processes.

Modeling of pedestrian evacuation based on the particle swarm optimization algorithm

Science.gov (United States)

Zheng, Yaochen; Chen, Jianqiao; Wei, Junhong; Guo, Xiwei

2012-09-01

By applying the evolutionary algorithm of Particle Swarm Optimization (PSO), we have developed a new pedestrian evacuation model. In the new model, we first introduce the local pedestrian’s density concept which is defined as the number of pedestrians distributed in a certain area divided by the area. Both the maximum velocity and the size of a particle (pedestrian) are supposed to be functions of the local density. An attempt to account for the impact consequence between pedestrians is also made by introducing a threshold of injury into the model. The updating rule of the model possesses heterogeneous spatial and temporal characteristics. Numerical examples demonstrate that the model is capable of simulating the typical features of evacuation captured by CA (Cellular Automata) based models. As contrast to CA-based simulations, in which the velocity (via step size) of a pedestrian in each time step is a constant value and limited in several directions, the new model is more flexible in describing pedestrians’ velocities since they are not limited in discrete values and directions according to the new updating rule.

Unsupervised spike sorting based on discriminative subspace learning.

Science.gov (United States)

Keshtkaran, Mohammad Reza; Yang, Zhi

2014-01-01

Spike sorting is a fundamental preprocessing step for many neuroscience studies which rely on the analysis of spike trains. In this paper, we present two unsupervised spike sorting algorithms based on discriminative subspace learning. The first algorithm simultaneously learns the discriminative feature subspace and performs clustering. It uses histogram of features in the most discriminative projection to detect the number of neurons. The second algorithm performs hierarchical divisive clustering that learns a discriminative 1-dimensional subspace for clustering in each level of the hierarchy until achieving almost unimodal distribution in the subspace. The algorithms are tested on synthetic and in-vivo data, and are compared against two widely used spike sorting methods. The comparative results demonstrate that our spike sorting methods can achieve substantially higher accuracy in lower dimensional feature space, and they are highly robust to noise. Moreover, they provide significantly better cluster separability in the learned subspace than in the subspace obtained by principal component analysis or wavelet transform.

Reducing 4D CT artifacts using optimized sorting based on anatomic similarity.

Science.gov (United States)

Johnston, Eric; Diehn, Maximilian; Murphy, James D; Loo, Billy W; Maxim, Peter G

2011-05-01

Four-dimensional (4D) computed tomography (CT) has been widely used as a tool to characterize respiratory motion in radiotherapy. The two most commonly used 4D CT algorithms sort images by the associated respiratory phase or displacement into a predefined number of bins, and are prone to image artifacts at transitions between bed positions. The purpose of this work is to demonstrate a method of reducing motion artifacts in 4D CT by incorporating anatomic similarity into phase or displacement based sorting protocols. Ten patient datasets were retrospectively sorted using both the displacement and phase based sorting algorithms. Conventional sorting methods allow selection of only the nearest-neighbor image in time or displacement within each bin. In our method, for each bed position either the displacement or the phase defines the center of a bin range about which several candidate images are selected. The two dimensional correlation coefficients between slices bordering the interface between adjacent couch positions are then calculated for all candidate pairings. Two slices have a high correlation if they are anatomically similar. Candidates from each bin are then selected to maximize the slice correlation over the entire data set using the Dijkstra's shortest path algorithm. To assess the reduction of artifacts, two thoracic radiation oncologists independently compared the resorted 4D datasets pairwise with conventionally sorted datasets, blinded to the sorting method, to choose which had the least motion artifacts. Agreement between reviewers was evaluated using the weighted kappa score. Anatomically based image selection resulted in 4D CT datasets with significantly reduced motion artifacts with both displacement (P = 0.0063) and phase sorting (P = 0.00022). There was good agreement between the two reviewers, with complete agreement 34 times and complete disagreement 6 times. Optimized sorting using anatomic similarity significantly reduces 4D CT motion

Sorting live stem cells based on Sox2 mRNA expression.

Directory of Open Access Journals (Sweden)

Hans M Larsson

Full Text Available While cell sorting usually relies on cell-surface protein markers, molecular beacons (MBs offer the potential to sort cells based on the presence of any expressed mRNA and in principle could be extremely useful to sort rare cell populations from primary isolates. We show here how stem cells can be purified from mixed cell populations by sorting based on MBs. Specifically, we designed molecular beacons targeting Sox2, a well-known stem cell marker for murine embryonic (mES and neural stem cells (NSC. One of our designed molecular beacons displayed an increase in fluorescence compared to a nonspecific molecular beacon both in vitro and in vivo when tested in mES and NSCs. We sorted Sox2-MB(+SSEA1(+ cells from a mixed population of 4-day retinoic acid-treated mES cells and effectively isolated live undifferentiated stem cells. Additionally, Sox2-MB(+ cells isolated from primary mouse brains were sorted and generated neurospheres with higher efficiency than Sox2-MB(- cells. These results demonstrate the utility of MBs for stem cell sorting in an mRNA-specific manner.

Antarctic krill swarm characteristics in the Southeast Atlantic sector of the Southern Ocean

KAUST Repository

Krafft, BA

2012-09-28

Knowledge about swarm dynamics and underlying causes is essential to understand the ecology and distribution of Antarctic krill Euphausia superba. We collected acoustic data and key environmental data continuously across extensive gradients in the little-studied Southeast Atlantic sector of the Southern Ocean. A total of 4791 krill swarms with swarm descriptors including swarm height and length, packing density, swimming depth and inter-swarm distance were extracted. Through multivariate statistics, swarms were categorized into 4 groups. Group 2 swarms were largest (median length 108 m and thickness 18 m), whereas swarms in both Groups 1 and 4 were on average small, but differed markedly in depth distribution (median: 52 m for Group 1 vs. 133 m for Group 4). There was a strong spatial autocorrelation in the occurrence of swarms, and an autologistic regression model found no prediction of swarm occurrence from environmental variables for any of the Groups 1, 2 or 4. Probability of occurrence of Group 3 swarms, however, increased with increasing depth and temperature. Group 3 was the most distinctive swarm group with an order of magnitude higher packing density (median: 226 ind. m−3) than swarms from any of the other groups and about twice the distance to nearest neighbor swarm (median: 493 m). The majority of the krill were present in Group 3 swarms, and the absence of association with hydrographic or topographic concentrating mechanisms strongly suggests that these swarms aggregate through their own locomotion, possibly associated with migration.

Antarctic krill swarm characteristics in the Southeast Atlantic sector of the Southern Ocean

KAUST Repository

Krafft, BA; Skaret, G; Knutsen, T; Melle, W; Klevjer, Thor; Sø iland, H

2012-01-01

Knowledge about swarm dynamics and underlying causes is essential to understand the ecology and distribution of Antarctic krill Euphausia superba. We collected acoustic data and key environmental data continuously across extensive gradients in the little-studied Southeast Atlantic sector of the Southern Ocean. A total of 4791 krill swarms with swarm descriptors including swarm height and length, packing density, swimming depth and inter-swarm distance were extracted. Through multivariate statistics, swarms were categorized into 4 groups. Group 2 swarms were largest (median length 108 m and thickness 18 m), whereas swarms in both Groups 1 and 4 were on average small, but differed markedly in depth distribution (median: 52 m for Group 1 vs. 133 m for Group 4). There was a strong spatial autocorrelation in the occurrence of swarms, and an autologistic regression model found no prediction of swarm occurrence from environmental variables for any of the Groups 1, 2 or 4. Probability of occurrence of Group 3 swarms, however, increased with increasing depth and temperature. Group 3 was the most distinctive swarm group with an order of magnitude higher packing density (median: 226 ind. m−3) than swarms from any of the other groups and about twice the distance to nearest neighbor swarm (median: 493 m). The majority of the krill were present in Group 3 swarms, and the absence of association with hydrographic or topographic concentrating mechanisms strongly suggests that these swarms aggregate through their own locomotion, possibly associated with migration.

Multispecies Coevolution Particle Swarm Optimization Based on Previous Search History

Directory of Open Access Journals (Sweden)

Danping Wang

2017-01-01

Full Text Available A hybrid coevolution particle swarm optimization algorithm with dynamic multispecies strategy based on K-means clustering and nonrevisit strategy based on Binary Space Partitioning fitness tree (called MCPSO-PSH is proposed. Previous search history memorized into the Binary Space Partitioning fitness tree can effectively restrain the individuals’ revisit phenomenon. The whole population is partitioned into several subspecies and cooperative coevolution is realized by an information communication mechanism between subspecies, which can enhance the global search ability of particles and avoid premature convergence to local optimum. To demonstrate the power of the method, comparisons between the proposed algorithm and state-of-the-art algorithms are grouped into two categories: 10 basic benchmark functions (10-dimensional and 30-dimensional, 10 CEC2005 benchmark functions (30-dimensional, and a real-world problem (multilevel image segmentation problems. Experimental results show that MCPSO-PSH displays a competitive performance compared to the other swarm-based or evolutionary algorithms in terms of solution accuracy and statistical tests.

Feature and Intensity Based Medical Image Registration Using Particle Swarm Optimization.

Science.gov (United States)

Abdel-Basset, Mohamed; Fakhry, Ahmed E; El-Henawy, Ibrahim; Qiu, Tie; Sangaiah, Arun Kumar

2017-11-03

Image registration is an important aspect in medical image analysis, and kinds use in a variety of medical applications. Examples include diagnosis, pre/post surgery guidance, comparing/merging/integrating images from multi-modal like Magnetic Resonance Imaging (MRI), and Computed Tomography (CT). Whether registering images across modalities for a single patient or registering across patients for a single modality, registration is an effective way to combine information from different images into a normalized frame for reference. Registered datasets can be used for providing information relating to the structure, function, and pathology of the organ or individual being imaged. In this paper a hybrid approach for medical images registration has been developed. It employs a modified Mutual Information (MI) as a similarity metric and Particle Swarm Optimization (PSO) method. Computation of mutual information is modified using a weighted linear combination of image intensity and image gradient vector flow (GVF) intensity. In this manner, statistical as well as spatial image information is included into the image registration process. Maximization of the modified mutual information is effected using the versatile Particle Swarm Optimization which is developed easily with adjusted less parameter. The developed approach has been tested and verified successfully on a number of medical image data sets that include images with missing parts, noise contamination, and/or of different modalities (CT, MRI). The registration results indicate the proposed model as accurate and effective, and show the posture contribution in inclusion of both statistical and spatial image data to the developed approach.

Swarms, swarming and entanglements of fungal hyphae and of plant roots

Science.gov (United States)

Barlow, Peter W.; Fisahn, Joachim

2013-01-01

There has been recent interest in the possibility that plant roots can show oriented collective motion, or swarming behavior. We examine the evidence supportive of root swarming and we also present new observations on this topic. Seven criteria are proposed for the definition of a swarm, whose application can help identify putative swarming behavior in plants. Examples where these criteria are fulfilled, at many levels of organization, are presented in relation to plant roots and root systems, as well as to the root-like mycelial cords (rhizomorphs) of fungi. The ideas of both an “active” swarming, directed by a signal which imposes a common vector on swarm element aggregation, and a “passive” swarming, where aggregation results from external constraint, are introduced. Active swarming is a pattern of cooperative behavior peculiar to the sporophyte generation of vascular plants and is the antithesis of the competitive behavior shown by the gametophyte generation of such plants, where passive swarming may be found. Fungal mycelial cords could serve as a model example of swarming in a multi-cellular, non-animal system. PMID:24255743

Markerless human motion tracking using hierarchical multi-swarm cooperative particle swarm optimization.

Science.gov (United States)

Saini, Sanjay; Zakaria, Nordin; Rambli, Dayang Rohaya Awang; Sulaiman, Suziah

2015-01-01

The high-dimensional search space involved in markerless full-body articulated human motion tracking from multiple-views video sequences has led to a number of solutions based on metaheuristics, the most recent form of which is Particle Swarm Optimization (PSO). However, the classical PSO suffers from premature convergence and it is trapped easily into local optima, significantly affecting the tracking accuracy. To overcome these drawbacks, we have developed a method for the problem based on Hierarchical Multi-Swarm Cooperative Particle Swarm Optimization (H-MCPSO). The tracking problem is formulated as a non-linear 34-dimensional function optimization problem where the fitness function quantifies the difference between the observed image and a projection of the model configuration. Both the silhouette and edge likelihoods are used in the fitness function. Experiments using Brown and HumanEva-II dataset demonstrated that H-MCPSO performance is better than two leading alternative approaches-Annealed Particle Filter (APF) and Hierarchical Particle Swarm Optimization (HPSO). Further, the proposed tracking method is capable of automatic initialization and self-recovery from temporary tracking failures. Comprehensive experimental results are presented to support the claims.

Simulation Study of Swarm Intelligence Based on Life Evolution Behavior

Directory of Open Access Journals (Sweden)

Yanmin Liu

2015-01-01

Full Text Available Swarm intelligence (SI is a new evolutionary computation technology, and its performance efficacy is usually affected by each individual behavior in the swarm. According to the genetic and sociological theory, the life evolution behavior process is influenced by the external and internal factors, so the mechanisms of external and internal environment change must be analyzed and explored. Therefore, in this paper, we used the thought of the famous American genetic biologist Morgan, “life = DNA + environment + interaction of environment + gene,” to propose the mutation and crossover operation of DNA fragments by the environmental change to improve the performance efficiency of intelligence algorithms. Additionally, PSO is a random swarm intelligence algorithm with the genetic and sociological property, so we embed the improved mutation and crossover operation to particle swarm optimization (PSO and designed DNA-PSO algorithm to optimize single and multiobjective optimization problems. Simulation experiments in single and multiobjective optimization problems show that the proposed strategies can effectively improve the performance of swarm intelligence.

Image de-noising based on mathematical morphology and multi-objective particle swarm optimization

Science.gov (United States)

Dou, Liyun; Xu, Dan; Chen, Hao; Liu, Yicheng

2017-07-01

To overcome the problem of image de-noising, an efficient image de-noising approach based on mathematical morphology and multi-objective particle swarm optimization (MOPSO) is proposed in this paper. Firstly, constructing a series and parallel compound morphology filter based on open-close (OC) operation and selecting a structural element with different sizes try best to eliminate all noise in a series link. Then, combining multi-objective particle swarm optimization (MOPSO) to solve the parameters setting of multiple structural element. Simulation result shows that our algorithm can achieve a superior performance compared with some traditional de-noising algorithm.

Automated spike sorting algorithm based on Laplacian eigenmaps and k-means clustering.

Science.gov (United States)

Chah, E; Hok, V; Della-Chiesa, A; Miller, J J H; O'Mara, S M; Reilly, R B

2011-02-01

This study presents a new automatic spike sorting method based on feature extraction by Laplacian eigenmaps combined with k-means clustering. The performance of the proposed method was compared against previously reported algorithms such as principal component analysis (PCA) and amplitude-based feature extraction. Two types of classifier (namely k-means and classification expectation-maximization) were incorporated within the spike sorting algorithms, in order to find a suitable classifier for the feature sets. Simulated data sets and in-vivo tetrode multichannel recordings were employed to assess the performance of the spike sorting algorithms. The results show that the proposed algorithm yields significantly improved performance with mean sorting accuracy of 73% and sorting error of 10% compared to PCA which combined with k-means had a sorting accuracy of 58% and sorting error of 10%.A correction was made to this article on 22 February 2011. The spacing of the title was amended on the abstract page. No changes were made to the article PDF and the print version was unaffected.

Particle Swarm Optimization Toolbox

Science.gov (United States)

Grant, Michael J.

2010-01-01

The Particle Swarm Optimization Toolbox is a library of evolutionary optimization tools developed in the MATLAB environment. The algorithms contained in the library include a genetic algorithm (GA), a single-objective particle swarm optimizer (SOPSO), and a multi-objective particle swarm optimizer (MOPSO). Development focused on both the SOPSO and MOPSO. A GA was included mainly for comparison purposes, and the particle swarm optimizers appeared to perform better for a wide variety of optimization problems. All algorithms are capable of performing unconstrained and constrained optimization. The particle swarm optimizers are capable of performing single and multi-objective optimization. The SOPSO and MOPSO algorithms are based on swarming theory and bird-flocking patterns to search the trade space for the optimal solution or optimal trade in competing objectives. The MOPSO generates Pareto fronts for objectives that are in competition. A GA, based on Darwin evolutionary theory, is also included in the library. The GA consists of individuals that form a population in the design space. The population mates to form offspring at new locations in the design space. These offspring contain traits from both of the parents. The algorithm is based on this combination of traits from parents to hopefully provide an improved solution than either of the original parents. As the algorithm progresses, individuals that hold these optimal traits will emerge as the optimal solutions. Due to the generic design of all optimization algorithms, each algorithm interfaces with a user-supplied objective function. This function serves as a "black-box" to the optimizers in which the only purpose of this function is to evaluate solutions provided by the optimizers. Hence, the user-supplied function can be numerical simulations, analytical functions, etc., since the specific detail of this function is of no concern to the optimizer. These algorithms were originally developed to support entry

Machine-vision based optofluidic cell sorting

DEFF Research Database (Denmark)

Glückstad, Jesper; Bañas, Andrew

the available light and creating 2D or 3D beam distributions aimed at the positions of the detected cells. Furthermore, the beam shaping freedom provided by GPC can allow optimizations in the beam’s propagation and its interaction with the laser catapulted and sorted cells....... machine vision1. This approach is gentler, less invasive and more economical compared to conventional FACS-systems. As cells are less responsive to plastic or glass objects commonly used in the optical manipulation literature2, and since laser safety would be an issue in clinical use, we develop efficient...... approaches in utilizing lasers and light modulation devices. The Generalized Phase Contrast (GPC) method3-9 that can be used for efficiently illuminating spatial light modulators10 or creating well-defined contiguous optical traps11 is supplemented by diffractive techniques capable of integrating...

A Parameter Estimation Method for Nonlinear Systems Based on Improved Boundary Chicken Swarm Optimization

Directory of Open Access Journals (Sweden)

Shaolong Chen

2016-01-01

Full Text Available Parameter estimation is an important problem in nonlinear system modeling and control. Through constructing an appropriate fitness function, parameter estimation of system could be converted to a multidimensional parameter optimization problem. As a novel swarm intelligence algorithm, chicken swarm optimization (CSO has attracted much attention owing to its good global convergence and robustness. In this paper, a method based on improved boundary chicken swarm optimization (IBCSO is proposed for parameter estimation of nonlinear systems, demonstrated and tested by Lorenz system and a coupling motor system. Furthermore, we have analyzed the influence of time series on the estimation accuracy. Computer simulation results show it is feasible and with desirable performance for parameter estimation of nonlinear systems.

Chaotic particle swarm optimization with mutation for classification.

Science.gov (United States)

Assarzadeh, Zahra; Naghsh-Nilchi, Ahmad Reza

2015-01-01

In this paper, a chaotic particle swarm optimization with mutation-based classifier particle swarm optimization is proposed to classify patterns of different classes in the feature space. The introduced mutation operators and chaotic sequences allows us to overcome the problem of early convergence into a local minima associated with particle swarm optimization algorithms. That is, the mutation operator sharpens the convergence and it tunes the best possible solution. Furthermore, to remove the irrelevant data and reduce the dimensionality of medical datasets, a feature selection approach using binary version of the proposed particle swarm optimization is introduced. In order to demonstrate the effectiveness of our proposed classifier, mutation-based classifier particle swarm optimization, it is checked out with three sets of data classifications namely, Wisconsin diagnostic breast cancer, Wisconsin breast cancer and heart-statlog, with different feature vector dimensions. The proposed algorithm is compared with different classifier algorithms including k-nearest neighbor, as a conventional classifier, particle swarm-classifier, genetic algorithm, and Imperialist competitive algorithm-classifier, as more sophisticated ones. The performance of each classifier was evaluated by calculating the accuracy, sensitivity, specificity and Matthews's correlation coefficient. The experimental results show that the mutation-based classifier particle swarm optimization unequivocally performs better than all the compared algorithms.

Chaotic Particle Swarm Optimization with Mutation for Classification

Science.gov (United States)

Assarzadeh, Zahra; Naghsh-Nilchi, Ahmad Reza

2015-01-01

In this paper, a chaotic particle swarm optimization with mutation-based classifier particle swarm optimization is proposed to classify patterns of different classes in the feature space. The introduced mutation operators and chaotic sequences allows us to overcome the problem of early convergence into a local minima associated with particle swarm optimization algorithms. That is, the mutation operator sharpens the convergence and it tunes the best possible solution. Furthermore, to remove the irrelevant data and reduce the dimensionality of medical datasets, a feature selection approach using binary version of the proposed particle swarm optimization is introduced. In order to demonstrate the effectiveness of our proposed classifier, mutation-based classifier particle swarm optimization, it is checked out with three sets of data classifications namely, Wisconsin diagnostic breast cancer, Wisconsin breast cancer and heart-statlog, with different feature vector dimensions. The proposed algorithm is compared with different classifier algorithms including k-nearest neighbor, as a conventional classifier, particle swarm-classifier, genetic algorithm, and Imperialist competitive algorithm-classifier, as more sophisticated ones. The performance of each classifier was evaluated by calculating the accuracy, sensitivity, specificity and Matthews's correlation coefficient. The experimental results show that the mutation-based classifier particle swarm optimization unequivocally performs better than all the compared algorithms. PMID:25709937

A particle-based simplified swarm optimization algorithm for reliability redundancy allocation problems

International Nuclear Information System (INIS)

Huang, Chia-Ling

2015-01-01

This paper proposes a new swarm intelligence method known as the Particle-based Simplified Swarm Optimization (PSSO) algorithm while undertaking a modification of the Updating Mechanism (UM), called N-UM and R-UM, and simultaneously applying an Orthogonal Array Test (OA) to solve reliability–redundancy allocation problems (RRAPs) successfully. One difficulty of RRAP is the need to maximize system reliability in cases where the number of redundant components and the reliability of corresponding components in each subsystem are simultaneously decided with nonlinear constraints. In this paper, four RRAP benchmarks are used to display the applicability of the proposed PSSO that advances the strengths of both PSO and SSO to enable optimizing the RRAP that belongs to mixed-integer nonlinear programming. When the computational results are compared with those of previously developed algorithms in existing literature, the findings indicate that the proposed PSSO is highly competitive and performs well. - Highlights: • This paper proposes a particle-based simplified swarm optimization algorithm (PSSO) to optimize RRAP. • Furthermore, the UM and an OA are adapted to advance in optimizing RRAP. • Four systems are introduced and the results demonstrate the PSSO performs particularly well

Physics-based approach to chemical source localization using mobile robotic swarms

Science.gov (United States)

Zarzhitsky, Dimitri

2008-07-01

Recently, distributed computation has assumed a dominant role in the fields of artificial intelligence and robotics. To improve system performance, engineers are combining multiple cooperating robots into cohesive collectives called swarms. This thesis illustrates the application of basic principles of physicomimetics, or physics-based design, to swarm robotic systems. Such principles include decentralized control, short-range sensing and low power consumption. We show how the application of these principles to robotic swarms results in highly scalable, robust, and adaptive multi-robot systems. The emergence of these valuable properties can be predicted with the help of well-developed theoretical methods. In this research effort, we have designed and constructed a distributed physicomimetics system for locating sources of airborne chemical plumes. This task, called chemical plume tracing (CPT), is receiving a great deal of attention due to persistent homeland security threats. For this thesis, we have created a novel CPT algorithm called fluxotaxis that is based on theoretical principles of fluid dynamics. Analytically, we show that fluxotaxis combines the essence, as well as the strengths, of the two most popular biologically-inspired CPT methods-- chemotaxis and anemotaxis. The chemotaxis strategy consists of navigating in the direction of the chemical density gradient within the plume, while the anemotaxis approach is based on an upwind traversal of the chemical cloud. Rigorous and extensive experimental evaluations have been performed in simulated chemical plume environments. Using a suite of performance metrics that capture the salient aspects of swarm-specific behavior, we have been able to evaluate and compare the three CPT algorithms. We demonstrate the improved performance of our fluxotaxis approach over both chemotaxis and anemotaxis in these realistic simulation environments, which include obstacles. To test our understanding of CPT on actual hardware

Robust and Blind 3D Mesh Watermarking in Spatial Domain Based on Faces Categorization and Sorting

Science.gov (United States)

Molaei, Amir Masoud; Ebrahimnezhad, Hossein; Sedaaghi, Mohammad Hossein

2016-06-01

In this paper, a 3D watermarking algorithm in spatial domain is presented with blind detection. In the proposed method, a negligible visual distortion is observed in host model. Initially, a preprocessing is applied on the 3D model to make it robust against geometric transformation attacks. Then, a number of triangle faces are determined as mark triangles using a novel systematic approach in which faces are categorized and sorted robustly. In order to enhance the capability of information retrieval by attacks, block watermarks are encoded using Reed-Solomon block error-correcting code before embedding into the mark triangles. Next, the encoded watermarks are embedded in spherical coordinates. The proposed method is robust against additive noise, mesh smoothing and quantization attacks. Also, it is stout next to geometric transformation, vertices and faces reordering attacks. Moreover, the proposed algorithm is designed so that it is robust against the cropping attack. Simulation results confirm that the watermarked models confront very low distortion if the control parameters are selected properly. Comparison with other methods demonstrates that the proposed method has good performance against the mesh smoothing attacks.

Do small swarms have an advantage when house hunting? The effect of swarm size on nest-site selection by Apis mellifera.

Science.gov (United States)

Schaerf, T M; Makinson, J C; Myerscough, M R; Beekman, M

2013-10-06

Reproductive swarms of honeybees are faced with the problem of finding a good site to establish a new colony. We examined the potential effects of swarm size on the quality of nest-site choice through a combination of modelling and field experiments. We used an individual-based model to examine the effects of swarm size on decision accuracy under the assumption that the number of bees actively involved in the decision-making process (scouts) is an increasing function of swarm size. We found that the ability of a swarm to choose the best of two nest sites decreases as swarm size increases when there is some time-lag between discovering the sites, consistent with Janson & Beekman (Janson & Beekman 2007 Proceedings of European Conference on Complex Systems, pp. 204-211.). However, when simulated swarms were faced with a realistic problem of choosing between many nest sites discoverable at all times, larger swarms were more accurate in their decisions than smaller swarms owing to their ability to discover nest sites more rapidly. Our experimental fieldwork showed that large swarms invest a larger number of scouts into the decision-making process than smaller swarms. Preliminary analysis of waggle dances from experimental swarms also suggested that large swarms could indeed discover and advertise nest sites at a faster rate than small swarms.

Observatory data and the Swarm mission

DEFF Research Database (Denmark)

Macmillan, S.; Olsen, Nils

2013-01-01

products. We describe here the preparation of the data set of ground observatory hourly mean values, including procedures to check and select observatory data spanning the modern magnetic survey satellite era. We discuss other possible combined uses of satellite and observatory data, in particular those......The ESA Swarm mission to identify and measure very accurately the different magnetic signals that arise in the Earth’s core, mantle, crust, oceans, ionosphere and magnetosphere, which together form the magnetic field around the Earth, has increased interest in magnetic data collected on the surface...... of the Earth at observatories. The scientific use of Swarm data and Swarm-derived products is greatly enhanced by combination with observatory data and indices. As part of the Swarm Level-2 data activities plans are in place to distribute such ground-based data along with the Swarm data as auxiliary data...

Cat swarm optimization based evolutionary framework for multi document summarization

Science.gov (United States)

Rautray, Rasmita; Balabantaray, Rakesh Chandra

2017-07-01

Today, World Wide Web has brought us enormous quantity of on-line information. As a result, extracting relevant information from massive data has become a challenging issue. In recent past text summarization is recognized as one of the solution to extract useful information from vast amount documents. Based on number of documents considered for summarization, it is categorized as single document or multi document summarization. Rather than single document, multi document summarization is more challenging for the researchers to find accurate summary from multiple documents. Hence in this study, a novel Cat Swarm Optimization (CSO) based multi document summarizer is proposed to address the problem of multi document summarization. The proposed CSO based model is also compared with two other nature inspired based summarizer such as Harmony Search (HS) based summarizer and Particle Swarm Optimization (PSO) based summarizer. With respect to the benchmark Document Understanding Conference (DUC) datasets, the performance of all algorithms are compared in terms of different evaluation metrics such as ROUGE score, F score, sensitivity, positive predicate value, summary accuracy, inter sentence similarity and readability metric to validate non-redundancy, cohesiveness and readability of the summary respectively. The experimental analysis clearly reveals that the proposed approach outperforms the other summarizers included in the study.

An immune-inspired swarm aggregation algorithm for self-healing swarm robotic systems.

Science.gov (United States)

Timmis, J; Ismail, A R; Bjerknes, J D; Winfield, A F T

2016-08-01

Swarm robotics is concerned with the decentralised coordination of multiple robots having only limited communication and interaction abilities. Although fault tolerance and robustness to individual robot failures have often been used to justify the use of swarm robotic systems, recent studies have shown that swarm robotic systems are susceptible to certain types of failure. In this paper we propose an approach to self-healing swarm robotic systems and take inspiration from the process of granuloma formation, a process of containment and repair found in the immune system. We use a case study of a swarm performing team work where previous works have demonstrated that partially failed robots have the most detrimental effect on overall swarm behaviour. We have developed an immune inspired approach that permits the recovery from certain failure modes during operation of the swarm, overcoming issues that effect swarm behaviour associated with partially failed robots. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Particle Swarm Optimization to the U-tube steam generator in the nuclear power plant

International Nuclear Information System (INIS)

Ibrahim, Wesam Zakaria

2014-01-01

Highlights: • We establish stability mathematical model of steam generator and reactor core. • We propose a new Particle Swarm Optimization algorithm. • The algorithm can overcome premature phenomenon and has a high search precision. • Optimal weight of steam generator is 15.1% less than the original. • Sensitivity analysis and optimal design provide reference for steam generator design. - Abstract: This paper, proposed an improved Particle Swarm Optimization approach for optimize a U-tube steam generator mathematical model. The UTSG is one of the most important component related to safety of most of the pressurized water reactor. The purpose of this article is to present an approach to optimization in which every target is considered as a separate objective to be optimized. Multi-objective optimization is a powerful tool for resolving conflicting objectives in engineering design and numerous other fields. One approach to solve multi-objective optimization problems is the non-dominated sorting Particle Swarm Optimization. PSO was applied in regarding the choice of the time intervals for the periodic testing of the model of the steam generator

Particle Swarm Optimization to the U-tube steam generator in the nuclear power plant

Energy Technology Data Exchange (ETDEWEB)

Ibrahim, Wesam Zakaria, E-mail: mimi9_m@yahoo.com

2014-12-15

Highlights: • We establish stability mathematical model of steam generator and reactor core. • We propose a new Particle Swarm Optimization algorithm. • The algorithm can overcome premature phenomenon and has a high search precision. • Optimal weight of steam generator is 15.1% less than the original. • Sensitivity analysis and optimal design provide reference for steam generator design. - Abstract: This paper, proposed an improved Particle Swarm Optimization approach for optimize a U-tube steam generator mathematical model. The UTSG is one of the most important component related to safety of most of the pressurized water reactor. The purpose of this article is to present an approach to optimization in which every target is considered as a separate objective to be optimized. Multi-objective optimization is a powerful tool for resolving conflicting objectives in engineering design and numerous other fields. One approach to solve multi-objective optimization problems is the non-dominated sorting Particle Swarm Optimization. PSO was applied in regarding the choice of the time intervals for the periodic testing of the model of the steam generator.

Swarm formation control utilizing elliptical surfaces and limiting functions.

Science.gov (United States)

Barnes, Laura E; Fields, Mary Anne; Valavanis, Kimon P

2009-12-01

In this paper, we present a strategy for organizing swarms of unmanned vehicles into a formation by utilizing artificial potential fields that were generated from normal and sigmoid functions. These functions construct the surface on which swarm members travel, controlling the overall swarm geometry and the individual member spacing. Nonlinear limiting functions are defined to provide tighter swarm control by modifying and adjusting a set of control variables that force the swarm to behave according to set constraints, formation, and member spacing. The artificial potential functions and limiting functions are combined to control swarm formation, orientation, and swarm movement as a whole. Parameters are chosen based on desired formation and user-defined constraints. This approach is computationally efficient and scales well to different swarm sizes, to heterogeneous systems, and to both centralized and decentralized swarm models. Simulation results are presented for a swarm of 10 and 40 robots that follow circle, ellipse, and wedge formations. Experimental results are included to demonstrate the applicability of the approach on a swarm of four custom-built unmanned ground vehicles (UGVs).

A real-time spike sorting method based on the embedded GPU.

Science.gov (United States)

Zelan Yang; Kedi Xu; Xiang Tian; Shaomin Zhang; Xiaoxiang Zheng

2017-07-01

Microelectrode arrays with hundreds of channels have been widely used to acquire neuron population signals in neuroscience studies. Online spike sorting is becoming one of the most important challenges for high-throughput neural signal acquisition systems. Graphic processing unit (GPU) with high parallel computing capability might provide an alternative solution for increasing real-time computational demands on spike sorting. This study reported a method of real-time spike sorting through computing unified device architecture (CUDA) which was implemented on an embedded GPU (NVIDIA JETSON Tegra K1, TK1). The sorting approach is based on the principal component analysis (PCA) and K-means. By analyzing the parallelism of each process, the method was further optimized in the thread memory model of GPU. Our results showed that the GPU-based classifier on TK1 is 37.92 times faster than the MATLAB-based classifier on PC while their accuracies were the same with each other. The high-performance computing features of embedded GPU demonstrated in our studies suggested that the embedded GPU provide a promising platform for the real-time neural signal processing.

Global volcanic earthquake swarm database and preliminary analysis of volcanic earthquake swarm duration

Directory of Open Access Journals (Sweden)

S. R. McNutt

1996-06-01

Full Text Available Global data from 1979 to 1989 pertaining to volcanic earthquake swarms have been compiled into a custom-designed relational database. The database is composed of three sections: 1 a section containing general information on volcanoes, 2 a section containing earthquake swarm data (such as dates of swarm occurrence and durations, and 3 a section containing eruption information. The most abundant and reliable parameter, duration of volcanic earthquake swarms, was chosen for preliminary analysis. The distribution of all swarm durations was found to have a geometric mean of 5.5 days. Precursory swarms were then separated from those not associated with eruptions. The geometric mean precursory swarm duration was 8 days whereas the geometric mean duration of swarms not associated with eruptive activity was 3.5 days. Two groups of precursory swarms are apparent when duration is compared with the eruption repose time. Swarms with durations shorter than 4 months showed no clear relationship with the eruption repose time. However, the second group, lasting longer than 4 months, showed a significant positive correlation with the log10 of the eruption repose period. The two groups suggest that different suites of physical processes are involved in the generation of volcanic earthquake swarms.

Adaptive differential correspondence imaging based on sorting technique

Directory of Open Access Journals (Sweden)

Heng Wu

2017-04-01

Full Text Available We develop an adaptive differential correspondence imaging (CI method using a sorting technique. Different from the conventional CI schemes, the bucket detector signals (BDS are first processed by a differential technique, and then sorted in a descending (or ascending order. Subsequently, according to the front and last several frames of the sorted BDS, the positive and negative subsets (PNS are created by selecting the relative frames from the reference detector signals. Finally, the object image is recovered from the PNS. Besides, an adaptive method based on two-step iteration is designed to select the optimum number of frames. To verify the proposed method, a single-detector computational ghost imaging (GI setup is constructed. We experimentally and numerically compare the performance of the proposed method with different GI algorithms. The results show that our method can improve the reconstruction quality and reduce the computation cost by using fewer measurement data.

Oscillators that sync and swarm.

Science.gov (United States)

O'Keeffe, Kevin P; Hong, Hyunsuk; Strogatz, Steven H

2017-11-15

Synchronization occurs in many natural and technological systems, from cardiac pacemaker cells to coupled lasers. In the synchronized state, the individual cells or lasers coordinate the timing of their oscillations, but they do not move through space. A complementary form of self-organization occurs among swarming insects, flocking birds, or schooling fish; now the individuals move through space, but without conspicuously altering their internal states. Here we explore systems in which both synchronization and swarming occur together. Specifically, we consider oscillators whose phase dynamics and spatial dynamics are coupled. We call them swarmalators, to highlight their dual character. A case study of a generalized Kuramoto model predicts five collective states as possible long-term modes of organization. These states may be observable in groups of sperm, Japanese tree frogs, colloidal suspensions of magnetic particles, and other biological and physical systems in which self-assembly and synchronization interact.

Seismotectonics of the Nicobar Swarm and the geodynamic implications for the 2004 Great Sumatran Earthquake

Science.gov (United States)

Lister, Gordon

2017-04-01

The Great Sumatran Earthquake took place on 26th December 2004. One month into the aftershock sequence, a dense swarm of earthquakes took place beneath the Andaman Sea, northeast of the Nicobar Islands. The swarm continued for ˜11 days, rapidly decreasing in intensity towards the end of that period. Unlike most earthquake swarms, the Nicobar cluster was characterised by a large number of shocks with moment magnitude exceeding five. This meant that centroid moment tensor data could be determined, and this data in turn allows geometric analysis of inferred fault plane motions. The classification obtained using program eQuakes shows aftershocks falling into distinct spatial groups. Thrusts dominate in the south (in the Sumatran domain), and normal faults dominate in the north (in the Andaman domain). Strike-slip faults are more evenly spread. They occur on the Sumatran wrench system, for example, but also on the Indian plate itself. Orientation groups readily emerge from such an analysis. Temporal variation in behaviour is immediately evident, changing after ˜12 months. Orientation groups in the first twelve months are consistent with margin perpendicular extension beneath the Andaman Sea (i.e. mode II megathrust behaviour) whereas afterward the pattern of deformation appears to have reverted to that expected in consequence of relative plate motion. In the first twelve months, strike-slip motion appears to have taken place on faults that are sub-parallel to spreading segments in the Andaman Sea. By early 2006 however normal fault clusters formed that showed ˜N-S extension across these spreading segments had resumed, while the overall density of aftershocks in the Andaman segment had considerably diminished. Throughout this entire period the Sumatran segment exhibited aftershock sequences consistent with ongoing Mode I megathrust behaviour. The Nicobar Swarm marks the transition from one sort of slab dynamics to the other. The earthquake swarm may have been

An Earthquake Swarm Search Implemented at Major Convergent Margins to Test for Associated Aseismic Slip

Science.gov (United States)

Holtkamp, S. G.; Pritchard, M. E.; Lohman, R. B.; Brudzinski, M. R.

2009-12-01

Recent geodetic analysis indicates earthquake swarms may be associated with slow slip such that earthquakes may only represent a fraction of the moment release. To investigate this potential relationship, we have developed a manual search approach to identify earthquake swarms from a seismicity catalog. Our technique is designed to be insensitive to spatial and temporal scales and the total number of events, as seismicity rates vary in different fault zones. Our first application of this technique on globally recorded earthquakes in South America detects 35 possible swarms of varying spatial scale, with 18 in the megathrust region and 8 along the volcanic arc. Three swarms in the vicinity of the arc appear to be triggered by the Mw=8.5 2001 Peru earthquake, and are examined for possible triggering mechanisms. Coulomb stress modeling suggests that static stress changes due to the earthquake are insufficient to trigger activity, so a dynamic or secondary triggering mechanism is more likely. Volcanic swarms are often associated with ground deformation, either associated with fluid movement (e.g. dike intrusion or chamber inflation or deflation) or fault movement, although these processes are sometimes difficult to differentiate. The only swarm along the arc with sufficient geodetic data that we can process and model is near Ticsani Volcano in Peru. In this case, a swarm of events southeast of the volcano precedes a more typical earthquake sequence beneath the volcano, and evidence for deformation is found in the location of the swarm, but there is no evidence for aseismic slip. Rather, we favor a model where the swarm is associated with deflation of a magma body to the southeast that triggered the earthquake sequence by promoting movement on a fault beneath Ticsani. Since swarms on the subduction interface may indicate aseismic moment release, with a direct impact on hazard, we examine potential relations between swarms and megathrust ruptures. We find evidence that

Towards a generic multi-agent engine for the simulation of spatial behavioural processes : MASQUE/SwarmCity

NARCIS (Netherlands)

Devisch, O.T.J.; Arentze, T.A.; Borgers, A.W.J.; Timmermans, H.J.P.; Leeuwen, van J.P.; Timmermans, H.J.P.

2004-01-01

SwarmCity is being developed as a micro-simulation model, simulating the location-choice behaviour of a population of households, retailers, firms, developers, etc. reacting to an urban plan. The focus of SwarmCity lies -in a first phase- on the decision-making procedures of households,

Design and analysis on sorting blade for automated size-based sorting device

Science.gov (United States)

Razali, Zol Bahri; Kader, Mohamed Mydin M. Abdul; Samsudin, Yasser Suhaimi; Daud, Mohd Hisam

2017-09-01

Nowadays rubbish separating or recycling is a main problem of nation, where peoples dumped their rubbish into dumpsite without caring the value of the rubbish if it can be recycled and reused. Thus the author proposed an automated segregating device, purposely to teach people to separate their rubbish and value the rubbish that can be reused. The automated size-based mechanical segregating device provides significant improvements in terms of efficiency and consistency in this segregating process. This device is designed to make recycling easier, user friendly, in the hope that more people will take responsibility if it is less of an expense of time and effort. This paper discussed about redesign a blade for the sorting device which is to develop an efficient automated mechanical sorting device for the similar material but in different size. The machine is able to identify the size of waste and it depends to the coil inside the container to separate it out. The detail design and methodology is described in detail in this paper.

Transport of Particle Swarms Through Fractures

Science.gov (United States)

Boomsma, E.; Pyrak-Nolte, L. J.

2011-12-01

slows the swarm, and a cohesive force that prevents swarm expansion and the corresponding decrease in particle density. For apertures >15mm, though the drag force is small, the loss of swarm cohesion dominates. In small apertures (<5mm), the drag from the wall dominates causing a loss in speed even though there is strong confinement. From a force-based particle interaction approach, the initial simulation did not capture the observed experimental behavior, i.e., the distinct peak in swarm velocities was not observed. For the funnel shaped aperture, the swarm was observed to bifurcate immediately upon reaching the intersection between the converging aperture and the uniform aperture portions of the fracture. Furthermore, converging apertures resulted in the deceleration of a swarm. Thus, the rate of transport of particle swarms is strongly affected by fracture aperture. Acknowledgment: The authors wish to acknowledge support of this work by the Geosciences Research Program, Office of Basic Energy Sciences US Department of Energy (DE-FG02-09ER16022).

A probability-based multi-cycle sorting method for 4D-MRI: A simulation study.

Science.gov (United States)

Liang, Xiao; Yin, Fang-Fang; Liu, Yilin; Cai, Jing

2016-12-01

To develop a novel probability-based sorting method capable of generating multiple breathing cycles of 4D-MRI images and to evaluate performance of this new method by comparing with conventional phase-based methods in terms of image quality and tumor motion measurement. Based on previous findings that breathing motion probability density function (PDF) of a single breathing cycle is dramatically different from true stabilized PDF that resulted from many breathing cycles, it is expected that a probability-based sorting method capable of generating multiple breathing cycles of 4D images may capture breathing variation information missing from conventional single-cycle sorting methods. The overall idea is to identify a few main breathing cycles (and their corresponding weightings) that can best represent the main breathing patterns of the patient and then reconstruct a set of 4D images for each of the identified main breathing cycles. This method is implemented in three steps: (1) The breathing signal is decomposed into individual breathing cycles, characterized by amplitude, and period; (2) individual breathing cycles are grouped based on amplitude and period to determine the main breathing cycles. If a group contains more than 10% of all breathing cycles in a breathing signal, it is determined as a main breathing pattern group and is represented by the average of individual breathing cycles in the group; (3) for each main breathing cycle, a set of 4D images is reconstructed using a result-driven sorting method adapted from our previous study. The probability-based sorting method was first tested on 26 patients' breathing signals to evaluate its feasibility of improving target motion PDF. The new method was subsequently tested for a sequential image acquisition scheme on the 4D digital extended cardiac torso (XCAT) phantom. Performance of the probability-based and conventional sorting methods was evaluated in terms of target volume precision and accuracy as measured

Applying Adaptive Swarm Intelligence Technology with Structuration in Web-Based Collaborative Learning

Science.gov (United States)

Huang, Yueh-Min; Liu, Chien-Hung

2009-01-01

One of the key challenges in the promotion of web-based learning is the development of effective collaborative learning environments. We posit that the structuration process strongly influences the effectiveness of technology used in web-based collaborative learning activities. In this paper, we propose an ant swarm collaborative learning (ASCL)…

Hierarchical Swarm Model: A New Approach to Optimization

Directory of Open Access Journals (Sweden)

Hanning Chen

2010-01-01

Full Text Available This paper presents a novel optimization model called hierarchical swarm optimization (HSO, which simulates the natural hierarchical complex system from where more complex intelligence can emerge for complex problems solving. This proposed model is intended to suggest ways that the performance of HSO-based algorithms on complex optimization problems can be significantly improved. This performance improvement is obtained by constructing the HSO hierarchies, which means that an agent in a higher level swarm can be composed of swarms of other agents from lower level and different swarms of different levels evolve on different spatiotemporal scale. A novel optimization algorithm (named PS2O, based on the HSO model, is instantiated and tested to illustrate the ideas of HSO model clearly. Experiments were conducted on a set of 17 benchmark optimization problems including both continuous and discrete cases. The results demonstrate remarkable performance of the PS2O algorithm on all chosen benchmark functions when compared to several successful swarm intelligence and evolutionary algorithms.

Multi-robot task allocation based on two dimensional artificial fish swarm algorithm

Science.gov (United States)

Zheng, Taixiong; Li, Xueqin; Yang, Liangyi

2007-12-01

The problem of task allocation for multiple robots is to allocate more relative-tasks to less relative-robots so as to minimize the processing time of these tasks. In order to get optimal multi-robot task allocation scheme, a twodimensional artificial swarm algorithm based approach is proposed in this paper. In this approach, the normal artificial fish is extended to be two dimension artificial fish. In the two dimension artificial fish, each vector of primary artificial fish is extended to be an m-dimensional vector. Thus, each vector can express a group of tasks. By redefining the distance between artificial fish and the center of artificial fish, the behavior of two dimension fish is designed and the task allocation algorithm based on two dimension artificial swarm algorithm is put forward. At last, the proposed algorithm is applied to the problem of multi-robot task allocation and comparer with GA and SA based algorithm is done. Simulation and compare result shows the proposed algorithm is effective.

Swarm controlled emergence for ant clustering

DEFF Research Database (Denmark)

Scheidler, Alexander; Merkle, Daniel; Middendorf, Martin

2013-01-01

.g. moving robots, and clustering algorithms. Design/methodology/approach: Different types of control agents for that ant clustering model are designed by introducing slight changes to the behavioural rules of the normal agents. The clustering behaviour of the resulting swarms is investigated by extensive...... for future research to investigate the application of the method in other swarm systems. Swarm controlled emergence might be applied to control emergent effects in computing systems that consist of many autonomous components which make decentralized decisions based on local information. Practical...... simulation studies. Findings: It is shown that complex behavior can emerge in systems with two types of agents (normal agents and control agents). For a particular behavior of the control agents, an interesting swarm size dependent effect was found. The behaviour prevents clustering when the number...

Novelty-driven Particle Swarm Optimization

DEFF Research Database (Denmark)

Galvao, Diana; Lehman, Joel Anthony; Urbano, Paulo

2015-01-01

Particle Swarm Optimization (PSO) is a well-known population-based optimization algorithm. Most often it is applied to optimize objective-based fitness functions that reward progress towards a desired objective or behavior. As a result, search increasingly focuses on higher-fitness areas. However......, in problems with many local optima, such focus often leads to premature convergence that precludes reaching the intended objective. To remedy this problem in certain types of domains, this paper introduces Novelty-driven Particle Swarm Optimization (NdPSO), which is motivated by the novelty search algorithm...

Optimal Time-Space Trade-Offs for Non-Comparison-Based Sorting

DEFF Research Database (Denmark)

Pagh, Rasmus; Pagter, Jacob Illeborg

2002-01-01

We study the problem of sorting n integers of w bits on a unit-cost RAM with word size w, and in particular consider the time-space trade-off (product of time and space in bits) for this problem. For comparison-based algorithms, the time-space complexity is known to be Θ(n2). A result of Beame...... shows that the lower bound also holds for non-comparison-based algorithms, but no algorithm has met this for time below the comparison-based Ω(nlgn) lower bound.We show that if sorting within some time bound &Ttilde; is possible, then time T = O(&Ttilde; + nlg* n) can be achieved with high probability...... using space S = O(n2/T + w), which is optimal. Given a deterministic priority queue using amortized time t(n) per operation and space nO(1), we provide a deterministic algorithm sorting in time T = O(n(t(n) + lg* n)) with S = O(n2/T + w). Both results require that w ≤ n1-Ω(1). Using existing priority...

Differential evolution enhanced with multiobjective sorting-based mutation operators.

Science.gov (United States)

Wang, Jiahai; Liao, Jianjun; Zhou, Ying; Cai, Yiqiao

2014-12-01

Differential evolution (DE) is a simple and powerful population-based evolutionary algorithm. The salient feature of DE lies in its mutation mechanism. Generally, the parents in the mutation operator of DE are randomly selected from the population. Hence, all vectors are equally likely to be selected as parents without selective pressure at all. Additionally, the diversity information is always ignored. In order to fully exploit the fitness and diversity information of the population, this paper presents a DE framework with multiobjective sorting-based mutation operator. In the proposed mutation operator, individuals in the current population are firstly sorted according to their fitness and diversity contribution by nondominated sorting. Then parents in the mutation operators are proportionally selected according to their rankings based on fitness and diversity, thus, the promising individuals with better fitness and diversity have more opportunity to be selected as parents. Since fitness and diversity information is simultaneously considered for parent selection, a good balance between exploration and exploitation can be achieved. The proposed operator is applied to original DE algorithms, as well as several advanced DE variants. Experimental results on 48 benchmark functions and 12 real-world application problems show that the proposed operator is an effective approach to enhance the performance of most DE algorithms studied.

Revisiting the South Atlantic Anomaly after 3 years of Swarm satellite mission

Science.gov (United States)

Pavón-Carrasco, F. Javier; Campuzano, Saioa A.; De Santis, Angelo

2017-04-01

Covering part of Southern America and the South Atlantic Ocean, the South Atlantic Anomaly (SAA) is nowadays one of the most important and largest features of the geomagnetic field at the Earth's surface. It is characterized by lower intensity values than expected for those geomagnetic latitudes. Thanks to the global geomagnetic models, the spatial and temporal geometry of the Earth's magnetic field can be defined at the core-mantle boundary, showing the origin of the SAA as a reversal polarity patch that is growing with a pronounced rate of -2.54ṡ105 nT per century and with western drift. Since the Swarm satellite mission of the European Space Agency was launched at the end of 2013, the three twin satellites are picking up the most accurate values of the geomagnetic field up to now. In this work, we use the satellite magnetic data from Swarm mission along with the observatory ground data of surrounding areas to evaluate the spatial and temporal evolution of the SAA during the Swarm-life.

A new ARMAX model based on evolutionary algorithm and particle swarm optimization for short-term load forecasting

International Nuclear Information System (INIS)

Wang, Bo; Tai, Neng-ling; Zhai, Hai-qing; Ye, Jian; Zhu, Jia-dong; Qi, Liang-bo

2008-01-01

In this paper, a new ARMAX model based on evolutionary algorithm and particle swarm optimization for short-term load forecasting is proposed. Auto-regressive (AR) and moving average (MA) with exogenous variables (ARMAX) has been widely applied in the load forecasting area. Because of the nonlinear characteristics of the power system loads, the forecasting function has many local optimal points. The traditional method based on gradient searching may be trapped in local optimal points and lead to high error. While, the hybrid method based on evolutionary algorithm and particle swarm optimization can solve this problem more efficiently than the traditional ways. It takes advantage of evolutionary strategy to speed up the convergence of particle swarm optimization (PSO), and applies the crossover operation of genetic algorithm to enhance the global search ability. The new ARMAX model for short-term load forecasting has been tested based on the load data of Eastern China location market, and the results indicate that the proposed approach has achieved good accuracy. (author)

Colour based sorting station with Matlab simulation

Directory of Open Access Journals (Sweden)

Constantin Victor

2017-01-01

Full Text Available The paper presents the design process and manufacturing elements of a colour-based sorting station. The system is comprised of a gravitational storage, which also contains the colour sensor. Parts are extracted using a linear pneumatic motor and are fed onto an electrically driven conveyor belt. Extraction of the parts is done at 4 points, using two pneumatic motors and a geared DC motor, while the 4th position is at the end of the belt. The mechanical parts of the system are manufactured using 3D printer technology, allowing for easy modification and adaption to the geometry of different parts. The paper shows all of the stages needed to design, optimize, test and implement the proposed solution. System optimization was performed using a graphical Matlab interface which also allows for sorting algorithm optimization.

Social networks and regional recruitment of foreign labour: Firm recruitment methods and spatial sorting in Denmark

DEFF Research Database (Denmark)

Schmidt, Torben Dall; Jensen, Peter Sandholt

2012-01-01

This paper tests the hypothesis that social networks are crucial for regional recruitment and inflows of foreign labour. New survey data on 971 firms located in Region Southern Denmark show that the predominant recruitment method of foreign labour was through networks. Danish municipal data from...... 1997–2006 furthermore reveal spatial sorting since initial shares of employees with a foreign background out of total regional employment predict foreign labour inflow rates to regional employment. Thus, social networks appear crucial for the recruitment and inflows of foreign labour, suggesting...

Perbandingan Kecepatan Gabungan Algoritma Quick Sort dan Merge Sort dengan Insertion Sort, Bubble Sort dan Selection Sort

OpenAIRE

Al Rivan, Muhammad Ezar

2017-01-01

Ordering is one of the process done before doing data processing. The sorting algorithm has its own strengths and weaknesses. By taking strengths of each algorithm then combined can be a better algorithm. Quick Sort and Merge Sort are algorithms that divide the data into parts and each part divide again into sub-section until one element. Usually one element join with others and then sorted by. In this experiment data divide into parts that have size not more than threshold. This part then so...

Biobotic insect swarm based sensor networks for search and rescue

Science.gov (United States)

Bozkurt, Alper; Lobaton, Edgar; Sichitiu, Mihail; Hedrick, Tyson; Latif, Tahmid; Dirafzoon, Alireza; Whitmire, Eric; Verderber, Alexander; Marin, Juan; Xiong, Hong

2014-06-01

The potential benefits of distributed robotics systems in applications requiring situational awareness, such as search-and-rescue in emergency situations, are indisputable. The efficiency of such systems requires robotic agents capable of coping with uncertain and dynamic environmental conditions. For example, after an earthquake, a tremendous effort is spent for days to reach to surviving victims where robotic swarms or other distributed robotic systems might play a great role in achieving this faster. However, current technology falls short of offering centimeter scale mobile agents that can function effectively under such conditions. Insects, the inspiration of many robotic swarms, exhibit an unmatched ability to navigate through such environments while successfully maintaining control and stability. We have benefitted from recent developments in neural engineering and neuromuscular stimulation research to fuse the locomotory advantages of insects with the latest developments in wireless networking technologies to enable biobotic insect agents to function as search-and-rescue agents. Our research efforts towards this goal include development of biobot electronic backpack technologies, establishment of biobot tracking testbeds to evaluate locomotion control efficiency, investigation of biobotic control strategies with Gromphadorhina portentosa cockroaches and Manduca sexta moths, establishment of a localization and communication infrastructure, modeling and controlling collective motion by learning deterministic and stochastic motion models, topological motion modeling based on these models, and the development of a swarm robotic platform to be used as a testbed for our algorithms.

Sorting out river channel patterns

NARCIS (Netherlands)

Kleinhans, M.G.

2010-01-01

Rivers self-organize their pattern/planform through feedbacks between bars, channels, floodplain and vegetation, which emerge as a result of the basic spatial sorting process of wash load sediment and bed sediment. The balance between floodplain formation and destruction determines the width and

PARTICLE SWARM OPTIMIZATION BASED OF THE MAXIMUM ...

African Journals Online (AJOL)

2010-06-30

Jun 30, 2010 ... Keywords: Particle Swarm Optimization (PSO), photovoltaic system, MPOP, ... systems from one hand and because of the instantaneous change of ..... Because of the P-V characteristics this heuristic method is used to seek ...

Self-sorting of guests and hard blocks in bisurea-based thermoplastic elastomers

NARCIS (Netherlands)

Botterhuis, N.E.; Karthikeyan, S.; Spiering, A.J.H.; Sijbesma, R.P.

2010-01-01

Self-sorting in thermoplastic elastomers was studied using bisurea-based thermoplastic elastomers (TPEs) which are known to form hard blocks via hierarchical aggregation of bisurea segments into ribbons and of ribbons into fibers. Self-sorting of different bisurea hard blocks in mixtures of polymers

Improved Sorting-Based Procedure for Integer Programming

DEFF Research Database (Denmark)

Dantchev, Stefan

2002-01-01

Recently, Cornuéjols and Dawande have considered a special class of 0-1 programs that turns out to be hard for existing IP solvers. One of them is a sorting-based algorithm, based on an idea of Wolsey. In this paper, we show how to improve both the running time and the space requirements...... of this algorithm. The drastic reduction of space needed allows us to solve much larger instances than was possible before using this technique....

A REVIEW OF SWARMING UNMANNED AERIAL VEHICLES

Directory of Open Access Journals (Sweden)

CORNEA Mihai

2016-11-01

Full Text Available This paper in if fact an overview of state of the art in mobile multi-robot systems as an initial part of our research in implementing a system based on swarm robotics concepts to be used in natural disaster search and rescue missions. The system is to be composed of a group of drones that can detect survivor mobile cell signals and exhibit some other features as well. This paper surveys the swarm robotics research landscape to provide a theoretical background to the implementation and help determine the techniques available to create the system. The Particle swarm optimization (PSO and Glowworm swarm optimization (GSO algorithms are briefly described and there is also insight into Bird flocking behavior and the model behind it

Stability-based sorting: The forgotten process behind (not only) biological evolution.

Science.gov (United States)

Toman, Jan; Flegr, Jaroslav

2017-12-21

Natural selection is considered to be the main process that drives biological evolution. It requires selected entities to originate dependent upon one another by the means of reproduction or copying, and for the progeny to inherit the qualities of their ancestors. However, natural selection is a manifestation of a more general persistence principle, whose temporal consequences we propose to name "stability-based sorting" (SBS). Sorting based on static stability, i.e., SBS in its strict sense and usual conception, favours characters that increase the persistence of their holders and act on all material and immaterial entities. Sorted entities could originate independently from each other, are not required to propagate and need not exhibit heredity. Natural selection is a specific form of SBS-sorting based on dynamic stability. It requires some form of heredity and is based on competition for the largest difference between the speed of generating its own copies and their expiration. SBS in its strict sense and selection thus have markedly different evolutionary consequences that are stressed in this paper. In contrast to selection, which is opportunistic, SBS is able to accumulate even momentarily detrimental characters that are advantageous for the long-term persistence of sorted entities. However, it lacks the amplification effect based on the preferential propagation of holders of advantageous characters. Thus, it works slower than selection and normally is unable to create complex adaptations. From a long-term perspective, SBS is a decisive force in evolution-especially macroevolution. SBS offers a new explanation for numerous evolutionary phenomena, including broad distribution and persistence of sexuality, altruistic behaviour, horizontal gene transfer, patterns of evolutionary stasis, planetary homeostasis, increasing ecosystem resistance to disturbances, and the universal decline of disparity in the evolution of metazoan lineages. SBS acts on all levels in

Light Focusing through Scattering Media by Particle Swarm Optimization

International Nuclear Information System (INIS)

Huang Hui-Ling; Chen Zi-Yang; Sun Cun-Zhi; Liu Ji-Lin; Pu Ji-Xiong

2015-01-01

We demonstrate light focusing through scattering media by introducing particle swarm optimization for modulating the phase wavefront. Light refocusing is simulated numerically based on the angular spectrum method and the circular Gaussian distribution model of the scattering media. Experimentally, a spatial light modulator is used to control the phase of incident light, so as to make the scattered light converge to a focus. The influence of divided segments of input light and the effect of the number of iterations on light intensity enhancement are investigated. Simulation results are found to be in good agreement with the theoretical analysis for light refocusing. (paper)

Color Feature-Based Object Tracking through Particle Swarm Optimization with Improved Inertia Weight.

Science.gov (United States)

Guo, Siqiu; Zhang, Tao; Song, Yulong; Qian, Feng

2018-04-23

This paper presents a particle swarm tracking algorithm with improved inertia weight based on color features. The weighted color histogram is used as the target feature to reduce the contribution of target edge pixels in the target feature, which makes the algorithm insensitive to the target non-rigid deformation, scale variation, and rotation. Meanwhile, the influence of partial obstruction on the description of target features is reduced. The particle swarm optimization algorithm can complete the multi-peak search, which can cope well with the object occlusion tracking problem. This means that the target is located precisely where the similarity function appears multi-peak. When the particle swarm optimization algorithm is applied to the object tracking, the inertia weight adjustment mechanism has some limitations. This paper presents an improved method. The concept of particle maturity is introduced to improve the inertia weight adjustment mechanism, which could adjust the inertia weight in time according to the different states of each particle in each generation. Experimental results show that our algorithm achieves state-of-the-art performance in a wide range of scenarios.

Sorting waves and associated eigenvalues

Science.gov (United States)

Carbonari, Costanza; Colombini, Marco; Solari, Luca

2017-04-01

The presence of mixed sediment always characterizes gravel bed rivers. Sorting processes take place during bed load transport of heterogeneous sediment mixtures. The two main elements necessary to the occurrence of sorting are the heterogeneous character of sediments and the presence of an active sediment transport. When these two key ingredients are simultaneously present, the segregation of bed material is consistently detected both in the field [7] and in laboratory [3] observations. In heterogeneous sediment transport, bed altimetric variations and sorting always coexist and both mechanisms are independently capable of driving the formation of morphological patterns. Indeed, consistent patterns of longitudinal and transverse sorting are identified almost ubiquitously. In some cases, such as bar formation [2] and channel bends [5], sorting acts as a stabilizing effect and therefore the dominant mechanism driving pattern formation is associated with bed altimetric variations. In other cases, such as longitudinal streaks, sorting enhances system instability and can therefore be considered the prevailing mechanism. Bedload sheets, first observed by Khunle and Southard [1], represent another classic example of a morphological pattern essentially triggered by sorting, as theoretical [4] and experimental [3] results suggested. These sorting waves cause strong spatial and temporal fluctuations of bedload transport rate typical observed in gravel bed rivers. The problem of bed load transport of a sediment mixture is formulated in the framework of a 1D linear stability analysis. The base state consists of a uniform flow in an infinitely wide channel with active bed load transport. The behaviour of the eigenvalues associated with fluid motion, bed evolution and sorting processes in the space of the significant flow and sediment parameters is analysed. A comparison is attempted with the results of the theoretical analysis of Seminara Colombini and Parker [4] and Stecca

Pressure Prediction of Coal Slurry Transportation Pipeline Based on Particle Swarm Optimization Kernel Function Extreme Learning Machine

Directory of Open Access Journals (Sweden)

Xue-cun Yang

2015-01-01

Full Text Available For coal slurry pipeline blockage prediction problem, through the analysis of actual scene, it is determined that the pressure prediction from each measuring point is the premise of pipeline blockage prediction. Kernel function of support vector machine is introduced into extreme learning machine, the parameters are optimized by particle swarm algorithm, and blockage prediction method based on particle swarm optimization kernel function extreme learning machine (PSOKELM is put forward. The actual test data from HuangLing coal gangue power plant are used for simulation experiments and compared with support vector machine prediction model optimized by particle swarm algorithm (PSOSVM and kernel function extreme learning machine prediction model (KELM. The results prove that mean square error (MSE for the prediction model based on PSOKELM is 0.0038 and the correlation coefficient is 0.9955, which is superior to prediction model based on PSOSVM in speed and accuracy and superior to KELM prediction model in accuracy.

A monolithic glass chip for active single-cell sorting based on mechanical phenotyping.

Science.gov (United States)

Faigle, Christoph; Lautenschläger, Franziska; Whyte, Graeme; Homewood, Philip; Martín-Badosa, Estela; Guck, Jochen

2015-03-07

The mechanical properties of biological cells have long been considered as inherent markers of biological function and disease. However, the screening and active sorting of heterogeneous populations based on serial single-cell mechanical measurements has not been demonstrated. Here we present a novel monolithic glass chip for combined fluorescence detection and mechanical phenotyping using an optical stretcher. A new design and manufacturing process, involving the bonding of two asymmetrically etched glass plates, combines exact optical fiber alignment, low laser damage threshold and high imaging quality with the possibility of several microfluidic inlet and outlet channels. We show the utility of such a custom-built optical stretcher glass chip by measuring and sorting single cells in a heterogeneous population based on their different mechanical properties and verify sorting accuracy by simultaneous fluorescence detection. This offers new possibilities of exact characterization and sorting of small populations based on rheological properties for biological and biomedical applications.

The Study on Food Sensory Evaluation based on Particle Swarm Optimization Algorithm

OpenAIRE

Hairong Wang; Huijuan Xu

2015-01-01

In this study, it explores the procedures and methods of the system for establishing food sensory evaluation based on particle swarm optimization algorithm, by means of explaining the interpretation of sensory evaluation and sensory analysis, combined with the applying situation of sensory evaluation in food industry.

The Optimal Wavelengths for Light Absorption Spectroscopy Measurements Based on Genetic Algorithm-Particle Swarm Optimization

Science.gov (United States)

Tang, Ge; Wei, Biao; Wu, Decao; Feng, Peng; Liu, Juan; Tang, Yuan; Xiong, Shuangfei; Zhang, Zheng

2018-03-01

To select the optimal wavelengths in the light extinction spectroscopy measurement, genetic algorithm-particle swarm optimization (GAPSO) based on genetic algorithm (GA) and particle swarm optimization (PSO) is adopted. The change of the optimal wavelength positions in different feature size parameters and distribution parameters is evaluated. Moreover, the Monte Carlo method based on random probability is used to identify the number of optimal wavelengths, and good inversion effects of the particle size distribution are obtained. The method proved to have the advantage of resisting noise. In order to verify the feasibility of the algorithm, spectra with bands ranging from 200 to 1000 nm are computed. Based on this, the measured data of standard particles are used to verify the algorithm.

Simulation Study of Swarm Intelligence Based on Life Evolution Behavior

OpenAIRE

Yanmin Liu; Ying Bi; Changling Sui; Yuanfeng Luo; Zhuanzhou Zhang; Rui Liu

2015-01-01

Swarm intelligence (SI) is a new evolutionary computation technology, and its performance efficacy is usually affected by each individual behavior in the swarm. According to the genetic and sociological theory, the life evolution behavior process is influenced by the external and internal factors, so the mechanisms of external and internal environment change must be analyzed and explored. Therefore, in this paper, we used the thought of the famous American genetic biologist Morgan, “life = DN...

MAGNAS - Magnetic Nanoprobe SWARM

DEFF Research Database (Denmark)

Lubberstedt, H.; Koebel, D.; Hansen, Flemming

2005-01-01

This paper presents the Magnetic Nano-Probe Swarm mission utilising a constellation of several swarms of nano-satellites in order to acquire simultaneous measurements of the geomagnetic field resolving the local field gradients. The space segment comprises of up to 4 S/C swarms each consisting...

Consensus-Based Sorting of Neuronal Spike Waveforms.

Science.gov (United States)

Fournier, Julien; Mueller, Christian M; Shein-Idelson, Mark; Hemberger, Mike; Laurent, Gilles

2016-01-01

Optimizing spike-sorting algorithms is difficult because sorted clusters can rarely be checked against independently obtained "ground truth" data. In most spike-sorting algorithms in use today, the optimality of a clustering solution is assessed relative to some assumption on the distribution of the spike shapes associated with a particular single unit (e.g., Gaussianity) and by visual inspection of the clustering solution followed by manual validation. When the spatiotemporal waveforms of spikes from different cells overlap, the decision as to whether two spikes should be assigned to the same source can be quite subjective, if it is not based on reliable quantitative measures. We propose a new approach, whereby spike clusters are identified from the most consensual partition across an ensemble of clustering solutions. Using the variability of the clustering solutions across successive iterations of the same clustering algorithm (template matching based on K-means clusters), we estimate the probability of spikes being clustered together and identify groups of spikes that are not statistically distinguishable from one another. Thus, we identify spikes that are most likely to be clustered together and therefore correspond to consistent spike clusters. This method has the potential advantage that it does not rely on any model of the spike shapes. It also provides estimates of the proportion of misclassified spikes for each of the identified clusters. We tested our algorithm on several datasets for which there exists a ground truth (simultaneous intracellular data), and show that it performs close to the optimum reached by a support vector machine trained on the ground truth. We also show that the estimated rate of misclassification matches the proportion of misclassified spikes measured from the ground truth data.

Optimal configuration of power grid sources based on optimal particle swarm algorithm

Science.gov (United States)

Wen, Yuanhua

2018-04-01

In order to optimize the distribution problem of power grid sources, an optimized particle swarm optimization algorithm is proposed. First, the concept of multi-objective optimization and the Pareto solution set are enumerated. Then, the performance of the classical genetic algorithm, the classical particle swarm optimization algorithm and the improved particle swarm optimization algorithm are analyzed. The three algorithms are simulated respectively. Compared with the test results of each algorithm, the superiority of the algorithm in convergence and optimization performance is proved, which lays the foundation for subsequent micro-grid power optimization configuration solution.

From organized internal traffic to collective navigation of bacterial swarms

International Nuclear Information System (INIS)

Ariel, Gil; Shklarsh, Adi; Kalisman, Oren; Ben-Jacob, Eshel; Ingham, Colin

2013-01-01

Bacterial swarming resulting in collective navigation over surfaces provides a valuable example of cooperative colonization of new territories. The social bacterium Paenibacillus vortex exhibits successful and diverse swarming strategies. When grown on hard agar surfaces with peptone, P. vortex develops complex colonies of vortices (rotating bacterial aggregates). In contrast, during growth on Mueller–Hinton broth gelled into a soft agar surface, a new strategy of multi-level organization is revealed: the colonies are organized into a special network of swarms (or ‘snakes’ of a fraction of millimeter in width) with intricate internal traffic. More specifically, cell movement is organized in two or three lanes of bacteria traveling between the back and the front of the swarm. This special form of cellular logistics suggests new methods in which bacteria can share resources and risk while searching for food or migrating into new territories. While the vortices-based organization on hard agar surfaces has been modeled before, here, we introduce a new multi-agent bacterial swarming model devised to capture the swarms-based organization on soft surfaces. We test two putative generic mechanisms that may underlie the observed swarming logistics: (i) chemo-activated taxis in response to chemical cues and (ii) special align-and-push interactions between the bacteria and the boundary of the layer of lubricant collectively generated by the swarming bacteria. Using realistic parameters, the model captures the observed phenomena with semi-quantitative agreement in terms of the velocity as well as the dynamics of the swarm and its envelope. This agreement implies that the bacteria interactions with the swarm boundary play a crucial role in mediating the interplay between the collective movement of the swarm and the internal traffic dynamics. (paper)

Assessing Human Judgment of Computationally Generated Swarming Behavior

Directory of Open Access Journals (Sweden)

John Harvey

2018-02-01

Full Text Available Computer-based swarm systems, aiming to replicate the flocking behavior of birds, were first introduced by Reynolds in 1987. In his initial work, Reynolds noted that while it was difficult to quantify the dynamics of the behavior from the model, observers of his model immediately recognized them as a representation of a natural flock. Considerable analysis has been conducted since then on quantifying the dynamics of flocking/swarming behavior. However, no systematic analysis has been conducted on human identification of swarming. In this paper, we assess subjects’ assessment of the behavior of a simplified version of Reynolds’ model. Factors that affect the identification of swarming are discussed and future applications of the resulting models are proposed. Differences in decision times for swarming-related questions asked during the study indicate that different brain mechanisms may be involved in different elements of the behavior assessment task. The relatively simple but finely tunable model used in this study provides a useful methodology for assessing individual human judgment of swarming behavior.

Particle Swarm Optimization Based on Local Attractors of Ordinary Differential Equation System

Directory of Open Access Journals (Sweden)

Wenyu Yang

2014-01-01

Full Text Available Particle swarm optimization (PSO is inspired by sociological behavior. In this paper, we interpret PSO as a finite difference scheme for solving a system of stochastic ordinary differential equations (SODE. In this framework, the position points of the swarm converge to an equilibrium point of the SODE and the local attractors, which are easily defined by the present position points, also converge to the global attractor. Inspired by this observation, we propose a class of modified PSO iteration methods (MPSO based on local attractors of the SODE. The idea of MPSO is to choose the next update state near the present local attractor, rather than the present position point as in the original PSO, according to a given probability density function. In particular, the quantum-behaved particle swarm optimization method turns out to be a special case of MPSO by taking a special probability density function. The MPSO methods with six different probability density functions are tested on a few benchmark problems. These MPSO methods behave differently for different problems. Thus, our framework not only gives an interpretation for the ordinary PSO but also, more importantly, provides a warehouse of PSO-like methods to choose from for solving different practical problems.

POLICE OFFICE MODEL IMPROVEMENT FOR SECURITY OF SWARM ROBOTIC SYSTEMS

Directory of Open Access Journals (Sweden)

I. A. Zikratov

2014-09-01

Full Text Available This paper focuses on aspects of information security for group of mobile robotic systems with swarm intellect. The ways for hidden attacks realization by the opposing party on swarm algorithm are discussed. We have fulfilled numerical modeling of potentially destructive information influence on the ant shortest path algorithm. We have demonstrated the consequences of attacks on the ant algorithm with different concentration in a swarm of subversive robots. Approaches are suggested for information security mechanisms in swarm robotic systems, based on the principles of centralized security management for mobile agents. We have developed the method of forming a self-organizing information security management system for robotic agents in swarm groups implementing POM (Police Office Model – a security model based on police offices, to provide information security in multi-agent systems. The method is based on the usage of police station network in the graph nodes, which have functions of identification and authentication of agents, identifying subversive robots by both their formal characteristics and their behavior in the swarm. We have suggested a list of software and hardware components for police stations, consisting of: communication channels between the robots in police office, nodes register, a database of robotic agents, a database of encryption and decryption module. We have suggested the variants of logic for the mechanism of information security in swarm systems with different temporary diagrams of data communication between police stations. We present comparative analysis of implementation of protected swarm systems depending on the functioning logic of police offices, integrated in swarm system. It is shown that the security model saves the ability to operate in noisy environments, when the duration of the interference is comparable to the time necessary for the agent to overcome the path between police stations.

Path Planning for Mobile Objects in Four-Dimension Based on Particle Swarm Optimization Method with Penalty Function

Directory of Open Access Journals (Sweden)

Yong Ma

2013-01-01

Full Text Available We present one algorithm based on particle swarm optimization (PSO with penalty function to determine the conflict-free path for mobile objects in four-dimension (three spatial and one-time dimensions with obstacles. The shortest path of the mobile object is set as goal function, which is constrained by conflict-free criterion, path smoothness, and velocity and acceleration requirements. This problem is formulated as a calculus of variation problem (CVP. With parametrization method, the CVP is converted to a time-varying nonlinear programming problem (TNLPP. Constraints of TNLPP are transformed to general TNLPP without any constraints through penalty functions. Then, by using a little calculations and applying the algorithm PSO, the solution of the CVP is consequently obtained. Approach efficiency is confirmed by numerical examples.

Aircraft noise, health, and residential sorting: evidence from two quasi-experiments.

Science.gov (United States)

Boes, Stefan; Nüesch, Stephan; Stillman, Steven

2013-09-01

We explore two unexpected changes in flight regulations to estimate the causal effect of aircraft noise on health. Detailed measures of noise are linked with longitudinal data on individual health outcomes based on the exact address information. Controlling for individual heterogeneity and spatial sorting into different neighborhoods, we find that aircraft noise significantly increases sleeping problems and headaches. Models that do not control for such heterogeneity and sorting substantially underestimate the negative health effects, which suggests that individuals self-select into residence based on their unobserved sensitivity to noise. Our study demonstrates that the combination of quasi-experimental variation and panel data is very powerful for identifying causal effects in epidemiological field studies. Copyright © 2013 John Wiley & Sons, Ltd.

Binar Sort: A Linear Generalized Sorting Algorithm

OpenAIRE

Gilreath, William F.

2008-01-01

Sorting is a common and ubiquitous activity for computers. It is not surprising that there exist a plethora of sorting algorithms. For all the sorting algorithms, it is an accepted performance limit that sorting algorithms are linearithmic or O(N lg N). The linearithmic lower bound in performance stems from the fact that the sorting algorithms use the ordering property of the data. The sorting algorithm uses comparison by the ordering property to arrange the data elements from an initial perm...

Swarm intelligence based on modified PSO algorithm for the optimization of axial-flow pump impeller

International Nuclear Information System (INIS)

Miao, Fuqing; Kim, Chol Min; Ahn, Seok Young; Park, Hong Seok

2015-01-01

This paper presents a multi-objective optimization of the impeller shape of an axial-flow pump based on the Modified particle swarm optimization (MPSO) algorithm. At first, an impeller shape was designed and used as a reference in the optimization process then NPSHr and η of the axial flow pump were numerically investigated by using the commercial software ANSYS with the design variables concerning hub angle β_h, chord angle β_c, cascade solidity of chord σ_c and maximum thickness of blade H. By using the Group method of data handling (GMDH) type neural networks in commercial software DTREG, the corresponding polynomial representation for NPSHr and η with respect to the design variables were obtained. A benchmark test was employed to evaluate the performance of the MPSO algorithm in comparison with other particle swarm algorithms. Later the MPSO approach was used for Pareto based optimization. Finally, the MPSO optimization result and CFD simulation result were compared in a re-evaluation process. By using swarm intelligence based on the modified PSO algorithm, better performance pump with higher efficiency and lower NPSHr could be obtained. This novel algorithm was successfully applied for the optimization of axial-flow pump impeller shape design

Swarm intelligence based on modified PSO algorithm for the optimization of axial-flow pump impeller

Energy Technology Data Exchange (ETDEWEB)

Miao, Fuqing; Kim, Chol Min; Ahn, Seok Young [Pusan National University, Busan (Korea, Republic of); Park, Hong Seok [Ulsan University, Ulsan (Korea, Republic of)

2015-11-15

This paper presents a multi-objective optimization of the impeller shape of an axial-flow pump based on the Modified particle swarm optimization (MPSO) algorithm. At first, an impeller shape was designed and used as a reference in the optimization process then NPSHr and η of the axial flow pump were numerically investigated by using the commercial software ANSYS with the design variables concerning hub angle β{sub h}, chord angle β{sub c}, cascade solidity of chord σ{sub c} and maximum thickness of blade H. By using the Group method of data handling (GMDH) type neural networks in commercial software DTREG, the corresponding polynomial representation for NPSHr and η with respect to the design variables were obtained. A benchmark test was employed to evaluate the performance of the MPSO algorithm in comparison with other particle swarm algorithms. Later the MPSO approach was used for Pareto based optimization. Finally, the MPSO optimization result and CFD simulation result were compared in a re-evaluation process. By using swarm intelligence based on the modified PSO algorithm, better performance pump with higher efficiency and lower NPSHr could be obtained. This novel algorithm was successfully applied for the optimization of axial-flow pump impeller shape design.

Optimal Allocation of Generalized Power Sources in Distribution Network Based on Multi-Objective Particle Swarm Optimization Algorithm

Directory of Open Access Journals (Sweden)

Li Ran

2017-01-01

Full Text Available Optimal allocation of generalized power sources in distribution network is researched. A simple index of voltage stability is put forward. Considering the investment and operation benefit, the stability of voltage and the pollution emissions of generalized power sources in distribution network, a multi-objective optimization planning model is established. A multi-objective particle swarm optimization algorithm is proposed to solve the optimal model. In order to improve the global search ability, the strategies of fast non-dominated sorting, elitism and crowding distance are adopted in this algorithm. Finally, tested the model and algorithm by IEEE-33 node system to find the best configuration of GP, the computed result shows that with the generalized power reasonable access to the active distribution network, the investment benefit and the voltage stability of the system is improved, and the proposed algorithm has better global search capability.

A Parallel Particle Swarm Optimizer

National Research Council Canada - National Science Library

Schutte, J. F; Fregly, B .J; Haftka, R. T; George, A. D

2003-01-01

.... Motivated by a computationally demanding biomechanical system identification problem, we introduce a parallel implementation of a stochastic population based global optimizer, the Particle Swarm...

The spatial structure of magnetospheric plasma disturbance estimated by using magnetic data obtained by SWARM satellites.

Science.gov (United States)

Yokoyama, Y.; Iyemori, T.; Aoyama, T.

2017-12-01

Field-aligned currents with various spatial scales flow into and out from high-latitude ionosphere. The magnetic fluctuations observed by LEO satellites along their orbits having period longer than a few seconds can be regarded as the manifestations of spatial structure of field aligned currents.This has been confirmed by using the initial orbital characteristics of 3 SWARM-satellites. From spectral analysis, we evaluated the spectral indices of these magnetic fluctuations and investigated their dependence on regions, such as magnetic latitude and MLT and so on. We found that the spectral indices take quite different values between the regions lower than the equatorward boundary of the auroral oval (around 63 degrees' in magnetic latitude) and the regions higher than that. On the other hands, we could not find the clear MLT dependence. In general, the FACs are believed to be generated in the magnetiospheric plasma sheet and boundary layer, and they flow along the field lines conserving their currents.The theory of FAC generation [e.g., Hasegawa and Sato ,1978] indicates that the FACs are strongly connected with magnetospheric plasma disturbances. Although the spectral indices above are these of spatial structures of the FACs over the ionosphere, by using the theoretical equation of FAC generation, we evaluate the spectral indices of magnetospheric plasma disturbance in FAC's generation regions. Furthermore, by projecting the area of fluctuations on the equatorial plane of magnetosphere (i.e. plasma sheet), we can estimate the spatial structure of magnetospheric plasma disturbance. In this presentation, we focus on the characteristics of disturbance in midnight region and discuss the relations to the substorm.

The Inertia Weight Updating Strategies in Particle Swarm Optimisation Based on the Beta Distribution

Directory of Open Access Journals (Sweden)

Petr Maca

2015-01-01

Full Text Available The presented paper deals with the comparison of selected random updating strategies of inertia weight in particle swarm optimisation. Six versions of particle swarm optimization were analysed on 28 benchmark functions, prepared for the Special Session on Real-Parameter Single Objective Optimisation at CEC2013. The random components of tested inertia weight were generated from Beta distribution with different values of shape parameters. The best analysed PSO version is the multiswarm PSO, which combines two strategies of updating the inertia weight. The first is driven by the temporally varying shape parameters, while the second is based on random control of shape parameters of Beta distribution.

Parameter Selection and Performance Comparison of Particle Swarm Optimization in Sensor Networks Localization.

Science.gov (United States)

Cui, Huanqing; Shu, Minglei; Song, Min; Wang, Yinglong

2017-03-01

Localization is a key technology in wireless sensor networks. Faced with the challenges of the sensors' memory, computational constraints, and limited energy, particle swarm optimization has been widely applied in the localization of wireless sensor networks, demonstrating better performance than other optimization methods. In particle swarm optimization-based localization algorithms, the variants and parameters should be chosen elaborately to achieve the best performance. However, there is a lack of guidance on how to choose these variants and parameters. Further, there is no comprehensive performance comparison among particle swarm optimization algorithms. The main contribution of this paper is three-fold. First, it surveys the popular particle swarm optimization variants and particle swarm optimization-based localization algorithms for wireless sensor networks. Secondly, it presents parameter selection of nine particle swarm optimization variants and six types of swarm topologies by extensive simulations. Thirdly, it comprehensively compares the performance of these algorithms. The results show that the particle swarm optimization with constriction coefficient using ring topology outperforms other variants and swarm topologies, and it performs better than the second-order cone programming algorithm.

Parameter Selection and Performance Comparison of Particle Swarm Optimization in Sensor Networks Localization

Directory of Open Access Journals (Sweden)

Huanqing Cui

2017-03-01

Full Text Available Localization is a key technology in wireless sensor networks. Faced with the challenges of the sensors’ memory, computational constraints, and limited energy, particle swarm optimization has been widely applied in the localization of wireless sensor networks, demonstrating better performance than other optimization methods. In particle swarm optimization-based localization algorithms, the variants and parameters should be chosen elaborately to achieve the best performance. However, there is a lack of guidance on how to choose these variants and parameters. Further, there is no comprehensive performance comparison among particle swarm optimization algorithms. The main contribution of this paper is three-fold. First, it surveys the popular particle swarm optimization variants and particle swarm optimization-based localization algorithms for wireless sensor networks. Secondly, it presents parameter selection of nine particle swarm optimization variants and six types of swarm topologies by extensive simulations. Thirdly, it comprehensively compares the performance of these algorithms. The results show that the particle swarm optimization with constriction coefficient using ring topology outperforms other variants and swarm topologies, and it performs better than the second-order cone programming algorithm.

Swarm robotics and minimalism

Science.gov (United States)

Sharkey, Amanda J. C.

2007-09-01

Swarm Robotics (SR) is closely related to Swarm Intelligence, and both were initially inspired by studies of social insects. Their guiding principles are based on their biological inspiration and take the form of an emphasis on decentralized local control and communication. Earlier studies went a step further in emphasizing the use of simple reactive robots that only communicate indirectly through the environment. More recently SR studies have moved beyond these constraints to explore the use of non-reactive robots that communicate directly, and that can learn and represent their environment. There is no clear agreement in the literature about how far such extensions of the original principles could go. Should there be any limitations on the individual abilities of the robots used in SR studies? Should knowledge of the capabilities of social insects lead to constraints on the capabilities of individual robots in SR studies? There is a lack of explicit discussion of such questions, and researchers have adopted a variety of constraints for a variety of reasons. A simple taxonomy of swarm robotics is presented here with the aim of addressing and clarifying these questions. The taxonomy distinguishes subareas of SR based on the emphases and justifications for minimalism and individual simplicity.

LazySorted: A Lazily, Partially Sorted Python List

Directory of Open Access Journals (Sweden)

Naftali Harris

2015-06-01

Full Text Available LazySorted is a Python C extension implementing a partially and lazily sorted list data structure. It solves a common problem faced by programmers, in which they need just part of a sorted list, like its middle element (the median, but sort the entire list to get it. LazySorted presents them with the abstraction that they are working with a fully sorted list, while actually only sorting the list partially with quicksort partitions to return the requested sub-elements. This enables programmers to use naive "sort first" algorithms but nonetheless attain linear run-times when possible. LazySorted may serve as a drop-in replacement for the built-in sorted function in most cases, and can sometimes achieve run-times more than 7 times faster.

Investigating Ground Swarm Robotics Using Agent Based Simulation

National Research Council Canada - National Science Library

Ho, Sze-Tek T

2006-01-01

The concept of employing ground swarm robotics to accomplish tasks has been proposed for future use in humanitarian de-mining, plume monitoring, searching for survivors in a disaster site, and other hazardous activities...

Field-aligned currents' scale analysis performed with the Swarm constellation

DEFF Research Database (Denmark)

Lühr, Hermann; Park, Jaeheung; Gjerløv, Jesper Wittendorff

2015-01-01

We present a statistical study of the temporal- and spatial-scale characteristics of different field-aligned current (FAC) types derived with the Swarm satellite formation. We divide FACs into two classes: small-scale, up to some 10 km, which are carried predominantly by kinetic Alfve´n waves...

Algorithm Sorts Groups Of Data

Science.gov (United States)

Evans, J. D.

1987-01-01

For efficient sorting, algorithm finds set containing minimum or maximum most significant data. Sets of data sorted as desired. Sorting process simplified by reduction of each multielement set of data to single representative number. First, each set of data expressed as polynomial with suitably chosen base, using elements of set as coefficients. Most significant element placed in term containing largest exponent. Base selected by examining range in value of data elements. Resulting series summed to yield single representative number. Numbers easily sorted, and each such number converted back to original set of data by successive division. Program written in BASIC.

Sex-sorting sperm using flow cytometry/cell sorting.

Science.gov (United States)

Garner, Duane L; Evans, K Michael; Seidel, George E

2013-01-01

The sex of mammalian offspring can be predetermined by flow sorting relatively pure living populations of X- and Y-chromosome-bearing sperm. This method is based on precise staining of the DNA of sperm with the nucleic acid-specific fluorophore, Hoechst 33342, to differentiate between the subpopulations of X- and Y-sperm. The fluorescently stained sperm are then sex-sorted using a specialized high speed sorter, MoFlo(®) SX XDP, and collected into biologically supportive media prior to reconcentration and cryopreservation in numbers adequate for use with artificial insemination for some species or for in vitro fertilization. Sperm sorting can provide subpopulations of X- or Y-bearing bovine sperm at rates in the 8,000 sperm/s range while maintaining; a purity of 90% such that it has been applied to cattle on a commercial basis. The sex of offspring has been predetermined in a wide variety of mammalian species including cattle, swine, horses, sheep, goats, dogs, cats, deer, elk, dolphins, water buffalo as well as in humans using flow cytometric sorting of X- and Y-sperm.

A Markov Chain Approach to Probabilistic Swarm Guidance

Science.gov (United States)

Acikmese, Behcet; Bayard, David S.

2012-01-01

This paper introduces a probabilistic guidance approach for the coordination of swarms of autonomous agents. The main idea is to drive the swarm to a prescribed density distribution in a prescribed region of the configuration space. In its simplest form, the probabilistic approach is completely decentralized and does not require communication or collabo- ration between agents. Agents make statistically independent probabilistic decisions based solely on their own state, that ultimately guides the swarm to the desired density distribution in the configuration space. In addition to being completely decentralized, the probabilistic guidance approach has a novel autonomous self-repair property: Once the desired swarm density distribution is attained, the agents automatically repair any damage to the distribution without collaborating and without any knowledge about the damage.

Quantitative analysis of distributed control paradigms of robot swarms

DEFF Research Database (Denmark)

Ngo, Trung Dung

2010-01-01

describe the physical and simulated robots, experiment scenario, and experiment setup. Third, we present our robot controllers based on behaviour based and neural network based paradigms. Fourth, we graphically show their experiment results and quantitatively analyse the results in comparison of the two......Given a task of designing controller for mobile robots in swarms, one might wonder which distributed control paradigms should be selected. Until now, paradigms of robot controllers have been within either behaviour based control or neural network based control, which have been recognized as two...... mainstreams of controller design for mobile robots. However, in swarm robotics, it is not clear how to determine control paradigms. In this paper we study the two control paradigms with various experiments of swarm aggregation. First, we introduce the two control paradigms for mobile robots. Second, we...

Drone Swarms

Science.gov (United States)

2017-05-25

Conversely, drone swarms have significant vulnerabilities and challenges, including electronic and cyber threats (hacking), legal and ethical ...Factors Affecting Success and selection in Goshawk Attacks on Woodpigeons,” Journal of Animal Ecology , Vol. 47, No. 2 (Jun., 1978), p 449-460 6 fish...organizational limitations, and ethical and legal constraints. This chapter answers what utility drone swarms bring to the military by examining

Light-Controlled Swarming and Assembly of Colloidal Particles

Directory of Open Access Journals (Sweden)

Jianhua Zhang

2018-02-01

Full Text Available Swarms and assemblies are ubiquitous in nature and they can perform complex collective behaviors and cooperative functions that they cannot accomplish individually. In response to light, some colloidal particles (CPs, including light active and passive CPs, can mimic their counterparts in nature and organize into complex structures that exhibit collective functions with remote controllability and high temporospatial precision. In this review, we firstly analyze the structural characteristics of swarms and assemblies of CPs and point out that light-controlled swarming and assembly of CPs are generally achieved by constructing light-responsive interactions between CPs. Then, we summarize in detail the recent advances in light-controlled swarming and assembly of CPs based on the interactions arisen from optical forces, photochemical reactions, photothermal effects, and photoisomerizations, as well as their potential applications. In the end, we also envision some challenges and future prospects of light-controlled swarming and assembly of CPs. With the increasing innovations in mechanisms and control strategies with easy operation, low cost, and arbitrary applicability, light-controlled swarming and assembly of CPs may be employed to manufacture programmable materials and reconfigurable robots for cooperative grasping, collective cargo transportation, and micro- and nanoengineering.

Ready, steady, SORT!

CERN Document Server

Laëtitia Pedroso

2010-01-01

The selective or ecological sorting of waste is already second nature to many of us and concerns us all. As the GS Department's new awareness-raising campaign reminds us, everything we do to sort waste contributes to preserving the environment.    Placemats printed on recycled paper using vegetable-based ink will soon be distributed in Restaurant No.1.   Environmental protection is never far from the headlines, and CERN has a responsibility to ensure that the 3000 tonnes and more of waste it produces every year are correctly and selectively sorted. Materials can be given a second life through recycling and re-use, thereby avoiding pollution from landfill sites and incineration plants and saving on processing costs. The GS Department is launching a new poster campaign designed to raise awareness of the importance of waste sorting and recycling. "After conducting a survey to find out whether members of the personnel were prepared to make an effort to sort a...

Evolution of Collective Behaviors for a Real Swarm of Aquatic Surface Robots.

Science.gov (United States)

Duarte, Miguel; Costa, Vasco; Gomes, Jorge; Rodrigues, Tiago; Silva, Fernando; Oliveira, Sancho Moura; Christensen, Anders Lyhne

2016-01-01

Swarm robotics is a promising approach for the coordination of large numbers of robots. While previous studies have shown that evolutionary robotics techniques can be applied to obtain robust and efficient self-organized behaviors for robot swarms, most studies have been conducted in simulation, and the few that have been conducted on real robots have been confined to laboratory environments. In this paper, we demonstrate for the first time a swarm robotics system with evolved control successfully operating in a real and uncontrolled environment. We evolve neural network-based controllers in simulation for canonical swarm robotics tasks, namely homing, dispersion, clustering, and monitoring. We then assess the performance of the controllers on a real swarm of up to ten aquatic surface robots. Our results show that the evolved controllers transfer successfully to real robots and achieve a performance similar to the performance obtained in simulation. We validate that the evolved controllers display key properties of swarm intelligence-based control, namely scalability, flexibility, and robustness on the real swarm. We conclude with a proof-of-concept experiment in which the swarm performs a complete environmental monitoring task by combining multiple evolved controllers.

Binary particle swarm optimization for frequency band selection in motor imagery based brain-computer interfaces.

Science.gov (United States)

Wei, Qingguo; Wei, Zhonghai

2015-01-01

A brain-computer interface (BCI) enables people suffering from affective neurological diseases to communicate with the external world. Common spatial pattern (CSP) is an effective algorithm for feature extraction in motor imagery based BCI systems. However, many studies have proved that the performance of CSP depends heavily on the frequency band of EEG signals used for the construction of covariance matrices. The use of different frequency bands to extract signal features may lead to different classification performances, which are determined by the discriminative and complementary information they contain. In this study, the broad frequency band (8-30 Hz) is divided into 10 sub-bands of band width 4 Hz and overlapping 2 Hz. Binary particle swarm optimization (BPSO) is used to find the best sub-band set to improve the performance of CSP and subsequent classification. Experimental results demonstrate that the proposed method achieved an average improvement of 6.91% in cross-validation accuracy when compared to broad band CSP.

A Novel Cluster Head Selection Algorithm Based on Fuzzy Clustering and Particle Swarm Optimization.

Science.gov (United States)

Ni, Qingjian; Pan, Qianqian; Du, Huimin; Cao, Cen; Zhai, Yuqing

2017-01-01

An important objective of wireless sensor network is to prolong the network life cycle, and topology control is of great significance for extending the network life cycle. Based on previous work, for cluster head selection in hierarchical topology control, we propose a solution based on fuzzy clustering preprocessing and particle swarm optimization. More specifically, first, fuzzy clustering algorithm is used to initial clustering for sensor nodes according to geographical locations, where a sensor node belongs to a cluster with a determined probability, and the number of initial clusters is analyzed and discussed. Furthermore, the fitness function is designed considering both the energy consumption and distance factors of wireless sensor network. Finally, the cluster head nodes in hierarchical topology are determined based on the improved particle swarm optimization. Experimental results show that, compared with traditional methods, the proposed method achieved the purpose of reducing the mortality rate of nodes and extending the network life cycle.

A persistent homology approach to collective behavior in insect swarms

Science.gov (United States)

Sinhuber, Michael; Ouellette, Nicholas T.

Various animals from birds and fish to insects tend to form aggregates, displaying self-organized collective swarming behavior. Due to their frequent occurrence in nature and their implications for engineered, collective systems, these systems have been investigated and modeled thoroughly for decades. Common approaches range from modeling them with coupled differential equations on the individual level up to continuum approaches. We present an alternative, topology-based approach for describing swarming behavior at the macroscale rather than the microscale. We study laboratory swarms of Chironomus riparius, a flying, non-biting midge. To obtain the time-resolved three-dimensional trajectories of individual insects, we use a multi-camera stereoimaging and particle-tracking setup. To investigate the swarming behavior in a topological sense, we employ a persistent homology approach to identify persisting structures and features in the insect swarm that elude a direct, ensemble-averaging approach. We are able to identify features of sub-clusters in the swarm that show behavior distinct from that of the remaining swarm members. The coexistence of sub-swarms with different features resembles some non-biological systems such as active colloids or even thermodynamic systems.

A degradation-based sorting method for lithium-ion battery reuse.

Directory of Open Access Journals (Sweden)

Hao Chen

Full Text Available In a world where millions of people are dependent on batteries to provide them with convenient and portable energy, battery recycling is of the utmost importance. In this paper, we developed a new method to sort 18650 Lithium-ion batteries in large quantities and in real time for harvesting used cells with enough capacity for battery reuse. Internal resistance and capacity tests were conducted as a basis for comparison with a novel degradation-based method based on X-ray radiographic scanning and digital image contrast computation. The test results indicate that the sorting accuracy of the test cells is about 79% and the execution time of our algorithm is at a level of 200 milliseconds, making our method a potential real-time solution for reusing the remaining capacity in good used cells.

Cooperative Search and Rescue with Artificial Fishes Based on Fish-Swarm Algorithm for Underwater Wireless Sensor Networks

Science.gov (United States)

Zhao, Wei; Tang, Zhenmin; Yang, Yuwang; Wang, Lei; Lan, Shaohua

2014-01-01

This paper presents a searching control approach for cooperating mobile sensor networks. We use a density function to represent the frequency of distress signals issued by victims. The mobile nodes' moving in mission space is similar to the behaviors of fish-swarm in water. So, we take the mobile node as artificial fish node and define its operations by a probabilistic model over a limited range. A fish-swarm based algorithm is designed requiring local information at each fish node and maximizing the joint detection probabilities of distress signals. Optimization of formation is also considered for the searching control approach and is optimized by fish-swarm algorithm. Simulation results include two schemes: preset route and random walks, and it is showed that the control scheme has adaptive and effective properties. PMID:24741341

Cooperative Search and Rescue with Artificial Fishes Based on Fish-Swarm Algorithm for Underwater Wireless Sensor Networks

Directory of Open Access Journals (Sweden)

Wei Zhao

2014-01-01

Full Text Available This paper presents a searching control approach for cooperating mobile sensor networks. We use a density function to represent the frequency of distress signals issued by victims. The mobile nodes’ moving in mission space is similar to the behaviors of fish-swarm in water. So, we take the mobile node as artificial fish node and define its operations by a probabilistic model over a limited range. A fish-swarm based algorithm is designed requiring local information at each fish node and maximizing the joint detection probabilities of distress signals. Optimization of formation is also considered for the searching control approach and is optimized by fish-swarm algorithm. Simulation results include two schemes: preset route and random walks, and it is showed that the control scheme has adaptive and effective properties.

Multispacecraft current estimates at swarm

DEFF Research Database (Denmark)

Dunlop, M. W.; Yang, Y.-Y.; Yang, J.-Y.

2015-01-01

During the first several months of the three-spacecraft Swarm mission all three spacecraft camerepeatedly into close alignment, providing an ideal opportunity for validating the proposed dual-spacecraftmethod for estimating current density from the Swarm magnetic field data. Two of the Swarm...

Guidance and control of swarms of spacecraft

Science.gov (United States)

Morgan, Daniel James

control uses a finite horizon to apply the most up-to-date control sequence while simultaneously calculating a new assignment and trajectory based on updated state information. Using a finite horizon allows collisions to only be considered between spacecraft that are near each other at the current time. This relaxes the all-to-all communication assumption so that only neighboring agents need to communicate. Experimental validation is done using the formation flying testbed. The swarm-reconfiguration algorithms are tested using multiple quadrotors. Experiments have been performed using sequential convex programming for offline trajectory planning, model predictive control and sequential convex programming for real-time trajectory generation, and the variable-swarm, distributed auction algorithm for optimal assignment. These experiments show that the swarm-reconfiguration algorithms can be implemented in real time using actual hardware. In general, this dissertation presents guidance and control algorithms that maintain and reconfigure swarms of spacecraft while maintaining the shape of the swarm, preventing collisions between the spacecraft, and minimizing the amount of propellant used.

Reconciling ocean mass content change based on direct and inverse approaches by utilizing data from GRACE, altimetry and Swarm

Science.gov (United States)

Rietbroek, R.; Uebbing, B.; Lück, C.; Kusche, J.

2017-12-01

Ocean mass content (OMC) change due to the melting of the ice-sheets in Greenland and Antarctica, melting of glaciers and changes in terrestrial hydrology is a major contributor to present-day sea level rise. Since 2002, the GRACE satellite mission serves as a valuable tool for directly measuring the variations in OMC. As GRACE has almost reached the end of its lifetime, efforts are being made to utilize the Swarm mission for the recovery of low degree time-variable gravity fields to bridge a possible gap until the GRACE-FO mission and to fill up periods where GRACE data was not existent. To this end we compute Swarm monthly normal equations and spherical harmonics that are found competitive to other solutions. In addition to directly measuring the OMC, combination of GRACE gravity data with altimetry data in a global inversion approach allows to separate the total sea level change into individual mass-driven and steric contributions. However, published estimates of OMC from the direct and inverse methods differ not only depending on the time window, but also are influenced by numerous post-processing choices. Here, we will look into sources of such differences between direct and inverse approaches and evaluate the capabilities of Swarm to derive OMC. Deriving time series of OMC requires several processing steps; choosing a GRACE (and altimetry) product, data coverage, masks and filters to be applied in either spatial or spectral domain, corrections related to spatial leakage, GIA and geocenter motion. In this study, we compare and quantify the effects of the different processing choices of the direct and inverse methods. Our preliminary results point to the GIA correction as the major source of difference between the two approaches.

Design of an Adaptive PID Neural Controller for Continuous Stirred Tank Reactor based on Particle Swarm Optimization

OpenAIRE

Khulood A. Dagher; Ahmed S. Al-Araji

2013-01-01

A particle swarm optimization algorithm and neural network like self-tuning PID controller for CSTR system is presented. The scheme of the discrete-time PID control structure is based on neural network and tuned the parameters of the PID controller by using a particle swarm optimization PSO technique as a simple and fast training algorithm. The proposed method has advantage that it is not necessary to use a combined structure of identification and decision because it used PSO. Simulation resu...

Continuous sorting of Brownian particles using coupled photophoresis and asymmetric potential cycling.

Science.gov (United States)

Ng, Tuck Wah; Neild, Adrian; Heeraman, Pascal

2008-03-15

Feasible sorters need to function rapidly and permit the input and delivery of particles continuously. Here, we describe a scheme that incorporates (i) restricted spatial input location and (ii) orthogonal sort and movement direction features. Sorting is achieved using an asymmetric potential that is cycled on and off, whereas movement is accomplished using photophoresis. Simulations with 0.2 and 0.5 microm diameter spherical particles indicate that sorting can commence quickly from a continuous stream. Procedures to optimize the sorting scheme are also described.

Agent based Particle Swarm Optimization for Load Frequency Control of Distribution Grid

DEFF Research Database (Denmark)

Cha, Seung-Tae; Saleem, Arshad; Wu, Qiuwei

2012-01-01

This paper presents a Particle Swarm Optimization (PSO) based on multi-agent controller. Real-time digital simulator (RTDS) is used for modelling the power system, while a PSO based multi-agent LFC algorithm is developed in JAVA for communicating with resource agents and determines the scenario...... to stabilize the frequency and voltage after the system enters into the islanding operation mode. The proposed algorithm is based on the formulation of an optimization problem using agent based PSO. The modified IEEE 9-bus system is employed to illustrate the performance of the proposed controller via RTDS...

Self-Organization in Aggregating Robot Swarms: A DW-KNN Topological Approach

KAUST Repository

Khaldi, Belkacem

2018-02-02

In certain swarm applications, where the inter-agent distance is not the only factor in the collective behaviours of the swarm, additional properties such as density could have a crucial effect. In this paper, we propose applying a Distance-Weighted K-Nearest Neighbouring (DW-KNN) topology to the behaviour of robot swarms performing self-organized aggregation, in combination with a virtual physics approach to keep the robots together. A distance-weighted function based on a Smoothed Particle Hydrodynamic (SPH) interpolation approach, which is used to evaluate the robot density in the swarm, is applied as the key factor for identifying the K-nearest neighbours taken into account when aggregating the robots. The intra virtual physical connectivity among these neighbours is achieved using a virtual viscoelastic-based proximity model. With the ARGoS based-simulator, we model and evaluate the proposed approach, showing various self-organized aggregations performed by a swarm of N foot-bot robots. Also, we compared the aggregation quality of DW-KNN aggregation approach to that of the conventional KNN approach and found better performance.

Cache-Aware and Cache-Oblivious Adaptive Sorting

DEFF Research Database (Denmark)

Brodal, Gerth Stølting; Fagerberg, Rolf; Moruz, Gabriel

2005-01-01

Two new adaptive sorting algorithms are introduced which perform an optimal number of comparisons with respect to the number of inversions in the input. The first algorithm is based on a new linear time reduction to (non-adaptive) sorting. The second algorithm is based on a new division protocol...... for the GenericSort algorithm by Estivill-Castro and Wood. From both algorithms we derive I/O-optimal cache-aware and cache-oblivious adaptive sorting algorithms. These are the first I/O-optimal adaptive sorting algorithms....

Time-delayed autosynchronous swarm control.

Science.gov (United States)

Biggs, James D; Bennet, Derek J; Dadzie, S Kokou

2012-01-01

In this paper a general Morse potential model of self-propelling particles is considered in the presence of a time-delayed term and a spring potential. It is shown that the emergent swarm behavior is dependent on the delay term and weights of the time-delayed function, which can be set to induce a stationary swarm, a rotating swarm with uniform translation, and a rotating swarm with a stationary center of mass. An analysis of the mean field equations shows that without a spring potential the motion of the center of mass is determined explicitly by a multivalued function. For a nonzero spring potential the swarm converges to a vortex formation about a stationary center of mass, except at discrete bifurcation points where the center of mass will periodically trace an ellipse. The analytical results defining the behavior of the center of mass are shown to correspond with the numerical swarm simulations.

Sorting Out Sorts

OpenAIRE

Jonathan B. Berk

1998-01-01

In this paper we analyze the theoretical implications of sorting data into groups and then running asset pricing tests within each group. We show that the way this procedure is implemented introduces a severe bias in favor of rejecting the model under consideration. By simply picking enough groups to sort into even the true asset pricing model can be shown to have no explanatory power within each group.

The Swarm Computing Approach to Business Intelligence

Directory of Open Access Journals (Sweden)

Schumann Andrew

2015-07-01

Full Text Available We have proposed to use some features of swarm behaviours in modelling business processes. Due to these features we deal with a propagation of business processes in all accessible directions. This propagation is involved into our formalization instead of communicating sequential processes. As a result, we have constructed a business process diagram language based on the swarm behavior and an extension of that language in the form of reflexive management language.

Agent-Based Simulation and Analysis of a Defensive UAV Swarm Against an Enemy UAV Swarm

Science.gov (United States)

2011-06-01

energy options” [10]. The research of swarm robotics derives much of its inspiration from natural systems. Social insects are known to coordinate their...Monterey, California 9. CPT. Francisco J. Hederra Direccion de Investigacion, Programas y Desarrollo de la Armada Armada de Chile CHILE 10. CAPT Jeffrey Kline, USN(ret.) Naval Postgraduate School Monterey, California 91

Swarm analysis by using transport equations

International Nuclear Information System (INIS)

Dote, Toshihiko.

1985-01-01

As the basis of weak ionization plasma phenomena, the motion, i.e. swarm, of charged particles in the gas is analyzed by use of the transport equations, from which basic nature of the swarm is discussed. The present report is an overview of the studies made in the past several years. Described are principally the most basic aspects concerning behaviors of the electrons and positive ions, that is, the basic equations and their significance, characteristics of the behaviors of the electron and positive ion swarms as revealed by solving the equations, and various characteristics of the swarm parameters. Contents are: Maxwell-Boltzmann's transport equations, behavior of the electron swarm, energy loss of the electrons, and behavior of the positive ion swarm. (Mori, K.)

A Framework for Constrained Optimization Problems Based on a Modified Particle Swarm Optimization

Directory of Open Access Journals (Sweden)

Biwei Tang

2016-01-01

Full Text Available This paper develops a particle swarm optimization (PSO based framework for constrained optimization problems (COPs. Aiming at enhancing the performance of PSO, a modified PSO algorithm, named SASPSO 2011, is proposed by adding a newly developed self-adaptive strategy to the standard particle swarm optimization 2011 (SPSO 2011 algorithm. Since the convergence of PSO is of great importance and significantly influences the performance of PSO, this paper first theoretically investigates the convergence of SASPSO 2011. Then, a parameter selection principle guaranteeing the convergence of SASPSO 2011 is provided. Subsequently, a SASPSO 2011-based framework is established to solve COPs. Attempting to increase the diversity of solutions and decrease optimization difficulties, the adaptive relaxation method, which is combined with the feasibility-based rule, is applied to handle constraints of COPs and evaluate candidate solutions in the developed framework. Finally, the proposed method is verified through 4 benchmark test functions and 2 real-world engineering problems against six PSO variants and some well-known methods proposed in the literature. Simulation results confirm that the proposed method is highly competitive in terms of the solution quality and can be considered as a vital alternative to solve COPs.

Virtual spring damper method for nonholonomic robotic swarm self-organization and leader following

Science.gov (United States)

Wiech, Jakub; Eremeyev, Victor A.; Giorgio, Ivan

2018-04-01

In this paper, we demonstrate a method for self-organization and leader following of nonholonomic robotic swarm based on spring damper mesh. By self-organization of swarm robots we mean the emergence of order in a swarm as the result of interactions among the single robots. In other words the self-organization of swarm robots mimics some natural behavior of social animals like ants among others. The dynamics of two-wheel robot is derived, and a relation between virtual forces and robot control inputs is defined in order to establish stable swarm formation. Two cases of swarm control are analyzed. In the first case the swarm cohesion is achieved by virtual spring damper mesh connecting nearest neighboring robots without designated leader. In the second case we introduce a swarm leader interacting with nearest and second neighbors allowing the swarm to follow the leader. The paper ends with numeric simulation for performance evaluation of the proposed control method.

Towards Realization of Intelligent Medical Treatment at Nanoscale by Artificial Microscopic Swarm Control Systems

Directory of Open Access Journals (Sweden)

Alireza Rowhanimanesh

2017-07-01

Full Text Available Background: In this paper, the novel concept of artificial microscopic swarm control systems is proposed as a promising approach towards realization of intelligent medical treatment at nanoscale. In this new paradigm, treatment is done autonomously at nanoscale within the patient’s body by the proposed swarm control systems.Methods: From control engineering perspective, medical treatment can be considered as a control problem, in which the ultimate goal is to find the best feasible way to change the state of diseased tissue from unhealthy to healthy in presence of uncertainty. Although a living tissue is a huge swarm of microscopic cells, nearly all of the common treatment methods are based on macroscopic centralized control paradigm. Inspired by natural microscopic swarm control systems such as nervous, endocrine and immune systems that work based on swarm control paradigm, medical treatment needs a paradigm shift from macroscopic centralized control to microscopic swarm control. An artificial microscopic swarm control system consists of a huge number of very simple autonomous microscopic agents that exploit swarm intelligence to realize sense, control (computing and actuation at nanoscale in local, distributed and decentralized manner. This control system can be designed based on mathematical analysis and computer simulation.Results: The proposed approach is used for treatment of atherosclerosis and cancer based on mathematical analysis and in-silico study.Conclusion: The notion of artificial microscopic swarm control systems opens new doors towards realization of autonomous and intelligent medical treatment at nanoscale within the patient’s body.

Verification of counting sort and radix sort

NARCIS (Netherlands)

C.P.T. de Gouw (Stijn); F.S. de Boer (Frank); J.C. Rot (Jurriaan)

2016-01-01

textabstractSorting is an important algorithmic task used in many applications. Two main aspects of sorting algorithms which have been studied extensively are complexity and correctness. [Foley and Hoare, 1971] published the first formal correctness proof of a sorting algorithm (Quicksort). While

Performance evaluation of PCA-based spike sorting algorithms.

Science.gov (United States)

Adamos, Dimitrios A; Kosmidis, Efstratios K; Theophilidis, George

2008-09-01

Deciphering the electrical activity of individual neurons from multi-unit noisy recordings is critical for understanding complex neural systems. A widely used spike sorting algorithm is being evaluated for single-electrode nerve trunk recordings. The algorithm is based on principal component analysis (PCA) for spike feature extraction. In the neuroscience literature it is generally assumed that the use of the first two or most commonly three principal components is sufficient. We estimate the optimum PCA-based feature space by evaluating the algorithm's performance on simulated series of action potentials. A number of modifications are made to the open source nev2lkit software to enable systematic investigation of the parameter space. We introduce a new metric to define clustering error considering over-clustering more favorable than under-clustering as proposed by experimentalists for our data. Both the program patch and the metric are available online. Correlated and white Gaussian noise processes are superimposed to account for biological and artificial jitter in the recordings. We report that the employment of more than three principal components is in general beneficial for all noise cases considered. Finally, we apply our results to experimental data and verify that the sorting process with four principal components is in agreement with a panel of electrophysiology experts.

Particle Swarms in Fractures: Open Versus Partially Closed Systems

Science.gov (United States)

Boomsma, E.; Pyrak-Nolte, L. J.

2014-12-01

drop or solid sphere, fracture boundaries do not exclusively decelerate swarm motion but instead produce enhanced swarm transport. Acknowledgments: This material is based upon work supported by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences, Geosciences Research Program under Award Number (DE-FG02-09ER16022).

Merging the fields of swarm robotics and new media: Perceiving swarm robotics as new media

Directory of Open Access Journals (Sweden)

Monika O. Ivanova

2014-06-01

Full Text Available The aim of this paper is to provide evidence that swarm robotic systems can be perceived as new media objects. A thorough description of the five principles of new media proposed by Lev Manovich in “The Language of New Media” is presented. This is complemented by a state of the art on swarm robotics with an in-depth comparison of the characteristics of both fields. Also presented are examples of swarm robotics used in new media installations in order to illustrate the cutting-edge applications of robotics and artificial intelligence achieved through the unity of bothfields. The hypothesis of this research is that a novel point of view would be introduced by examining the field of swarm robotics through the scope of new media, which would benefit thework of both new media and swarm robotic researchers.

Intelligent discrete particle swarm optimization for multiprocessor task scheduling problem

Directory of Open Access Journals (Sweden)

S Sarathambekai

2017-03-01

Full Text Available Discrete particle swarm optimization is one of the most recently developed population-based meta-heuristic optimization algorithm in swarm intelligence that can be used in any discrete optimization problems. This article presents a discrete particle swarm optimization algorithm to efficiently schedule the tasks in the heterogeneous multiprocessor systems. All the optimization algorithms share a common algorithmic step, namely population initialization. It plays a significant role because it can affect the convergence speed and also the quality of the final solution. The random initialization is the most commonly used method in majority of the evolutionary algorithms to generate solutions in the initial population. The initial good quality solutions can facilitate the algorithm to locate the optimal solution or else it may prevent the algorithm from finding the optimal solution. Intelligence should be incorporated to generate the initial population in order to avoid the premature convergence. This article presents a discrete particle swarm optimization algorithm, which incorporates opposition-based technique to generate initial population and greedy algorithm to balance the load of the processors. Make span, flow time, and reliability cost are three different measures used to evaluate the efficiency of the proposed discrete particle swarm optimization algorithm for scheduling independent tasks in distributed systems. Computational simulations are done based on a set of benchmark instances to assess the performance of the proposed algorithm.

Particle Swarm Optimization Based of the Maximum Photovoltaic ...

African Journals Online (AJOL)

Photovoltaic electricity is seen as an important source of renewable energy. The photovoltaic array is an unstable source of power since the peak power point depends on the temperature and the irradiation level. A maximum peak power point tracking is then necessary for maximum efficiency. In this work, a Particle Swarm ...

Short communication. Platform for bee-hives monitoring based on sound analysis. A perpetual warehouse for swarms daily activity

Energy Technology Data Exchange (ETDEWEB)

Atauri Mezquida, D.; Llorente Martinez, J.

2009-07-01

Bees and beekeeping are suffering a global crisis. Constant information on swarms conditions would be a key to study new diseases like colony collapse disorder and to develop new beekeeping tools to improve the hive management and make it more efficient. A platform for beehives monitoring is presented. It is based on the analysis of the colonies buzz which is registered by a bunch of sensors sending the data to a common database. Data obtained through sound processing shows plenty of patterns and tendency lines related to colonies activities and their conditions. It shows the potential of the sound as a swarm activity gauge. The goal of the platform is the possibility to store information about the swarms activity. The objective is to build a global net of monitored hives covering apiaries with different climates, razes and managements. (Author) 21 refs.

Bifurcating Particle Swarms in Smooth-Walled Fractures

Science.gov (United States)

Pyrak-Nolte, L. J.; Sun, H.

2010-12-01

Particle swarms can occur naturally or from industrial processes where small liquid drops containing thousands to millions of micron-size to colloidal-size particles are released over time from seepage or leaks into fractured rock. The behavior of these particle swarms as they fall under gravity are affected by particle interactions as well as interactions with the walls of the fractures. In this paper, we present experimental results on the effect of fractures on the cohesiveness of the swarm and the formation of bifurcation structures as they fall under gravity and interact with the fracture walls. A transparent cubic sample (100 mm x 100 mm x 100 mm) containing a synthetic fracture with uniform aperture distributions was optically imaged to quantify the effect of confinement within fractures on particle swarm formation, swarm velocity, and swarm geometry. A fracture with a uniform aperture distribution was fabricated from two polished rectangular prisms of acrylic. A series of experiments were performed to determine how swarm movement and geometry are affected as the walls of the fracture are brought closer together from 50 mm to 1 mm. During the experiments, the fracture was fully saturated with water. We created the swarms using two different particle sizes in dilute suspension (~ 1.0% by mass). The particles were 3 micron diameter fluorescent polymer beads and 25 micron diameter soda-lime glass beads. Experiments were performed using swarms that ranged in size from 5 µl to 60 µl. The swarm behavior was imaged using an optical fluorescent imaging system composed of a CCD camera illuminated by a 100 mW diode-pumped doubled YAG laser. As a swarm falls in an open-tank of water, it forms a torroidal shape that is stable as long as no ambient or background currents exist in the water tank. When a swarm is released into a fracture with an aperture less than 5 mm, the swarm forms the torroidal shape but it is distorted because of the presence of the walls. The

Multiobjective RFID Network Optimization Using Multiobjective Evolutionary and Swarm Intelligence Approaches

Directory of Open Access Journals (Sweden)

Hanning Chen

2014-01-01

Full Text Available The development of radio frequency identification (RFID technology generates the most challenging RFID network planning (RNP problem, which needs to be solved in order to operate the large-scale RFID network in an optimal fashion. RNP involves many objectives and constraints and has been proven to be a NP-hard multi-objective problem. The application of evolutionary algorithm (EA and swarm intelligence (SI for solving multiobjective RNP (MORNP has gained significant attention in the literature, but these algorithms always transform multiple objectives into a single objective by weighted coefficient approach. In this paper, we use multiobjective EA and SI algorithms to find all the Pareto optimal solutions and to achieve the optimal planning solutions by simultaneously optimizing four conflicting objectives in MORNP, instead of transforming multiobjective functions into a single objective function. The experiment presents an exhaustive comparison of three successful multiobjective EA and SI, namely, the recently developed multiobjective artificial bee colony algorithm (MOABC, the nondominated sorting genetic algorithm II (NSGA-II, and the multiobjective particle swarm optimization (MOPSO, on MORNP instances of different nature, namely, the two-objective and three-objective MORNP. Simulation results show that MOABC proves to be more superior for planning RFID networks than NSGA-II and MOPSO in terms of optimization accuracy and computation robustness.

A Novel Path Planning for Robots Based on Rapidly-Exploring Random Tree and Particle Swarm Optimizer Algorithm

Directory of Open Access Journals (Sweden)

Zhou Feng

2013-09-01

Full Text Available A based on Rapidly-exploring Random Tree(RRT and Particle Swarm Optimizer (PSO for path planning of the robot is proposed.First the grid method is built to describe the working space of the mobile robot,then the Rapidly-exploring Random Tree algorithm is used to obtain the global navigation path,and the Particle Swarm Optimizer algorithm is adopted to get the better path.Computer experiment results demonstrate that this novel algorithm can plan an optimal path rapidly in a cluttered environment.The successful obstacle avoidance is achieved,and the model is robust and performs reliably.

Swarm Satellites : Design, Characteristics and Applications

NARCIS (Netherlands)

Engelen, S.

2016-01-01

Satellite swarms are a novelty, yet promise to deliver unprecedented robustness and data-collection efficiency. They are so new in fact that even the definition of what a satellite swarm is is disputable, and consequently, the term "swarm" is used for practically any type of distributed space

Joint global optimization of tomographic data based on particle swarm optimization and decision theory

Science.gov (United States)

Paasche, H.; Tronicke, J.

2012-04-01

In many near surface geophysical applications multiple tomographic data sets are routinely acquired to explore subsurface structures and parameters. Linking the model generation process of multi-method geophysical data sets can significantly reduce ambiguities in geophysical data analysis and model interpretation. Most geophysical inversion approaches rely on local search optimization methods used to find an optimal model in the vicinity of a user-given starting model. The final solution may critically depend on the initial model. Alternatively, global optimization (GO) methods have been used to invert geophysical data. They explore the solution space in more detail and determine the optimal model independently from the starting model. Additionally, they can be used to find sets of optimal models allowing a further analysis of model parameter uncertainties. Here we employ particle swarm optimization (PSO) to realize the global optimization of tomographic data. PSO is an emergent methods based on swarm intelligence characterized by fast and robust convergence towards optimal solutions. The fundamental principle of PSO is inspired by nature, since the algorithm mimics the behavior of a flock of birds searching food in a search space. In PSO, a number of particles cruise a multi-dimensional solution space striving to find optimal model solutions explaining the acquired data. The particles communicate their positions and success and direct their movement according to the position of the currently most successful particle of the swarm. The success of a particle, i.e. the quality of the currently found model by a particle, must be uniquely quantifiable to identify the swarm leader. When jointly inverting disparate data sets, the optimization solution has to satisfy multiple optimization objectives, at least one for each data set. Unique determination of the most successful particle currently leading the swarm is not possible. Instead, only statements about the Pareto

The Swarm Magnetometry Package

DEFF Research Database (Denmark)

Merayo, José M.G.; Jørgensen, John Leif; Friis-Christensen, Eigil

2008-01-01

The Swarm mission under the ESA's Living Planet Programme is planned for launch in 2010 and consists of a constellation of three satellites at LEO. The prime objective of Swarm is to measure the geomagnetic field with unprecedented accuracy in space and time. The magnetometry package consists...

Osmotic pressure in a bacterial swarm.

Science.gov (United States)

Ping, Liyan; Wu, Yilin; Hosu, Basarab G; Tang, Jay X; Berg, Howard C

2014-08-19

Using Escherichia coli as a model organism, we studied how water is recruited by a bacterial swarm. A previous analysis of trajectories of small air bubbles revealed a stream of fluid flowing in a clockwise direction ahead of the swarm. A companion study suggested that water moves out of the agar into the swarm in a narrow region centered ∼ 30 μm from the leading edge of the swarm and then back into the agar (at a smaller rate) in a region centered ∼ 120 μm back from the leading edge. Presumably, these flows are driven by changes in osmolarity. Here, we utilized green/red fluorescent liposomes as reporters of osmolarity to verify this hypothesis. The stream of fluid that flows in front of the swarm contains osmolytes. Two distinct regions are observed inside the swarm near its leading edge: an outer high-osmolarity band (∼ 30 mOsm higher than the agar baseline) and an inner low-osmolarity band (isotonic or slightly hypotonic to the agar baseline). This profile supports the fluid-flow model derived from the drift of air bubbles and provides new (to our knowledge) insights into water maintenance in bacterial swarms. High osmotic pressure at the leading edge of the swarm extracts water from the underlying agar and promotes motility. The osmolyte is of high molecular weight and probably is lipopolysaccharide. Copyright © 2014 Biophysical Society. Published by Elsevier Inc. All rights reserved.

Biogeography-based particle swarm optimization with fuzzy elitism and its applications to constrained engineering problems

Science.gov (United States)

Guo, Weian; Li, Wuzhao; Zhang, Qun; Wang, Lei; Wu, Qidi; Ren, Hongliang

2014-11-01

In evolutionary algorithms, elites are crucial to maintain good features in solutions. However, too many elites can make the evolutionary process stagnate and cannot enhance the performance. This article employs particle swarm optimization (PSO) and biogeography-based optimization (BBO) to propose a hybrid algorithm termed biogeography-based particle swarm optimization (BPSO) which could make a large number of elites effective in searching optima. In this algorithm, the whole population is split into several subgroups; BBO is employed to search within each subgroup and PSO for the global search. Since not all the population is used in PSO, this structure overcomes the premature convergence in the original PSO. Time complexity analysis shows that the novel algorithm does not increase the time consumption. Fourteen numerical benchmarks and four engineering problems with constraints are used to test the BPSO. To better deal with constraints, a fuzzy strategy for the number of elites is investigated. The simulation results validate the feasibility and effectiveness of the proposed algorithm.

LinkMind: link optimization in swarming mobile sensor networks.

Science.gov (United States)

Ngo, Trung Dung

2011-01-01

A swarming mobile sensor network is comprised of a swarm of wirelessly connected mobile robots equipped with various sensors. Such a network can be applied in an uncertain environment for services such as cooperative navigation and exploration, object identification and information gathering. One of the most advantageous properties of the swarming wireless sensor network is that mobile nodes can work cooperatively to organize an ad-hoc network and optimize the network link capacity to maximize the transmission of gathered data from a source to a target. This paper describes a new method of link optimization of swarming mobile sensor networks. The new method is based on combination of the artificial potential force guaranteeing connectivities of the mobile sensor nodes and the max-flow min-cut theorem of graph theory ensuring optimization of the network link capacity. The developed algorithm is demonstrated and evaluated in simulation.

LinkMind: Link Optimization in Swarming Mobile Sensor Networks

Directory of Open Access Journals (Sweden)

Trung Dung Ngo

2011-08-01

Full Text Available A swarming mobile sensor network is comprised of a swarm of wirelessly connected mobile robots equipped with various sensors. Such a network can be applied in an uncertain environment for services such as cooperative navigation and exploration, object identification and information gathering. One of the most advantageous properties of the swarming wireless sensor network is that mobile nodes can work cooperatively to organize an ad-hoc network and optimize the network link capacity to maximize the transmission of gathered data from a source to a target. This paper describes a new method of link optimization of swarming mobile sensor networks. The new method is based on combination of the artificial potential force guaranteeing connectivities of the mobile sensor nodes and the max-flow min-cut theorem of graph theory ensuring optimization of the network link capacity. The developed algorithm is demonstrated and evaluated in simulation.

Fourier transform and particle swarm optimization based modified LQR algorithm for mitigation of vibrations using magnetorheological dampers

Science.gov (United States)

Kumar, Gaurav; Kumar, Ashok

2017-11-01

Structural control has gained significant attention in recent times. The standalone issue of power requirement during an earthquake has already been solved up to a large extent by designing semi-active control systems using conventional linear quadratic control theory, and many other intelligent control algorithms such as fuzzy controllers, artificial neural networks, etc. In conventional linear-quadratic regulator (LQR) theory, it is customary to note that the values of the design parameters are decided at the time of designing the controller and cannot be subsequently altered. During an earthquake event, the response of the structure may increase or decrease, depending the quasi-resonance occurring between the structure and the earthquake. In this case, it is essential to modify the value of the design parameters of the conventional LQR controller to obtain optimum control force to mitigate the vibrations due to the earthquake. A few studies have been done to sort out this issue but in all these studies it was necessary to maintain a database of the earthquake. To solve this problem and to find the optimized design parameters of the LQR controller in real time, a fast Fourier transform and particle swarm optimization based modified linear quadratic regulator method is presented here. This method comprises four different algorithms: particle swarm optimization (PSO), the fast Fourier transform (FFT), clipped control algorithm and the LQR. The FFT helps to obtain the dominant frequency for every time window. PSO finds the optimum gain matrix through the real-time update of the weighting matrix R, thereby, dispensing with the experimentation. The clipped control law is employed to match the magnetorheological (MR) damper force with the desired force given by the controller. The modified Bouc-Wen phenomenological model is taken to recognize the nonlinearities in the MR damper. The assessment of the advised method is done by simulation of a three-story structure

An External Archive-Guided Multiobjective Particle Swarm Optimization Algorithm.

Science.gov (United States)

Zhu, Qingling; Lin, Qiuzhen; Chen, Weineng; Wong, Ka-Chun; Coello Coello, Carlos A; Li, Jianqiang; Chen, Jianyong; Zhang, Jun

2017-09-01

The selection of swarm leaders (i.e., the personal best and global best), is important in the design of a multiobjective particle swarm optimization (MOPSO) algorithm. Such leaders are expected to effectively guide the swarm to approach the true Pareto optimal front. In this paper, we present a novel external archive-guided MOPSO algorithm (AgMOPSO), where the leaders for velocity update are all selected from the external archive. In our algorithm, multiobjective optimization problems (MOPs) are transformed into a set of subproblems using a decomposition approach, and then each particle is assigned accordingly to optimize each subproblem. A novel archive-guided velocity update method is designed to guide the swarm for exploration, and the external archive is also evolved using an immune-based evolutionary strategy. These proposed approaches speed up the convergence of AgMOPSO. The experimental results fully demonstrate the superiority of our proposed AgMOPSO in solving most of the test problems adopted, in terms of two commonly used performance measures. Moreover, the effectiveness of our proposed archive-guided velocity update method and immune-based evolutionary strategy is also experimentally validated on more than 30 test MOPs.

Application of visible spectroscopy in waste sorting

Science.gov (United States)

Spiga, Philippe; Bourely, Antoine

2011-10-01

Today, waste recycling, (bottles, papers...), is a mechanical operation: the waste are crushed, fused and agglomerated in order to obtain new manufactured products (e.g. new bottles, clothes ...). The plastics recycling is the main application in the color sorting process. The colorless plastics recovered are more valuable than the colored plastics. Other emergent applications are in the paper sorting, where the main goal is to sort dyed paper from white papers. Up to now, Pellenc Selective Technologies has manufactured color sorting machines based on RGB cameras. Three dimensions (red, green and blue) are no longer sufficient to detect low quantities of dye in the considered waste. In order to increase the efficiency of the color detection, a new sorting machine, based on visible spectroscopy, has been developed. This paper presents the principles of the two approaches and their difference in terms of sorting performance, making visible spectroscopy a clear winner.

Particle swarm optimization for automatic creation of complex graphic characters

International Nuclear Information System (INIS)

Fister, Iztok; Perc, Matjaž; Ljubič, Karin; Kamal, Salahuddin M.; Iglesias, Andres; Fister, Iztok

2015-01-01

Nature-inspired algorithms are a very promising tool for solving the hardest problems in computer sciences and mathematics. These algorithms are typically inspired by the fascinating behavior at display in biological systems, such as bee swarms or fish schools. So far, these algorithms have been applied in many practical applications. In this paper, we present a simple particle swarm optimization, which allows automatic creation of complex two-dimensional graphic characters. The method involves constructing the base characters, optimizing the modifications of the base characters with the particle swarm optimization algorithm, and finally generating the graphic characters from the solution. We demonstrate the effectiveness of our approach with the creation of simple snowman, but we also outline in detail how more complex characters can be created

Multiscale Modelling and Analysis of Collective Decision Making in Swarm Robotics

Science.gov (United States)

Vigelius, Matthias; Meyer, Bernd; Pascoe, Geoffrey

2014-01-01

We present a unified approach to describing certain types of collective decision making in swarm robotics that bridges from a microscopic individual-based description to aggregate properties. Our approach encompasses robot swarm experiments, microscopic and probabilistic macroscopic-discrete simulations as well as an analytic mathematical model. Following up on previous work, we identify the symmetry parameter, a measure of the progress of the swarm towards a decision, as a fundamental integrated swarm property and formulate its time evolution as a continuous-time Markov process. Contrary to previous work, which justified this approach only empirically and a posteriori, we justify it from first principles and derive hard limits on the parameter regime in which it is applicable. PMID:25369026

Multiscale modelling and analysis of collective decision making in swarm robotics.

Science.gov (United States)

Vigelius, Matthias; Meyer, Bernd; Pascoe, Geoffrey

2014-01-01

We present a unified approach to describing certain types of collective decision making in swarm robotics that bridges from a microscopic individual-based description to aggregate properties. Our approach encompasses robot swarm experiments, microscopic and probabilistic macroscopic-discrete simulations as well as an analytic mathematical model. Following up on previous work, we identify the symmetry parameter, a measure of the progress of the swarm towards a decision, as a fundamental integrated swarm property and formulate its time evolution as a continuous-time Markov process. Contrary to previous work, which justified this approach only empirically and a posteriori, we justify it from first principles and derive hard limits on the parameter regime in which it is applicable.

Perbandingan Bubble Sort dengan Insertion Sort pada Bahasa Pemrograman C dan Fortran

OpenAIRE

Reina, Reina; Gautama, Josef Bernadi

2013-01-01

Sorting is a basic algorithm studied by students of computer science major. Sorting algorithm is the basis of other algorithms such as searching algorithm, pattern matching algorithm. Bubble sort is a popular basic sorting algorithm due to its easiness to be implemented. Besides bubble sort, there is insertion sort. It is lesspopular than bubble sort because it has more difficult algorithm. This paper discusses about process time between insertion sort and bubble sort with two kinds of data. ...

Chaotically encoded particle swarm optimization algorithm and its applications

International Nuclear Information System (INIS)

Alatas, Bilal; Akin, Erhan

2009-01-01

This paper proposes a novel particle swarm optimization (PSO) algorithm, chaotically encoded particle swarm optimization algorithm (CENPSOA), based on the notion of chaos numbers that have been recently proposed for a novel meaning to numbers. In this paper, various chaos arithmetic and evaluation measures that can be used in CENPSOA have been described. Furthermore, CENPSOA has been designed to be effectively utilized in data mining applications.

A Survey of Formal Methods for Intelligent Swarms

Science.gov (United States)

Truszkowski, Walt; Rash, James; Hinchey, Mike; Rouff, Chrustopher A.

2004-01-01

cutting edge in system correctness, and requires higher levels of assurance than other (traditional) missions that use a single or small number of spacecraft that are deterministic in nature and have near continuous communication access. One of the highest possible levels of assurance comes from the application of formal methods. Formal methods are mathematics-based tools and techniques for specifying and verifying (software and hardware) systems. They are particularly useful for specifying complex parallel systems, such as exemplified by the ANTS mission, where the entire system is difficult for a single person to fully understand, a problem that is multiplied with multiple developers. Once written, a formal specification can be used to prove properties of a system (e.g., the underlying system will go from one state to another or not into a specific state) and check for particular types of errors (e.g., race or livelock conditions). A formal specification can also be used as input to a model checker for further validation. This report gives the results of a survey of formal methods techniques for verification and validation of space missions that use swarm technology. Multiple formal methods were evaluated to determine their effectiveness in modeling and assuring the behavior of swarms of spacecraft using the ANTS mission as an example system. This report is the first result of the project to determine formal approaches that are promising for formally specifying swarm-based systems. From this survey, the most promising approaches were selected and are discussed relative to their possible application to the ANTS mission. Future work will include the application of an integrated approach, based on the selected approaches identified in this report, to the formal specification of the ANTS mission.

IAP-Based Cell Sorting Results in Homogeneous Transplantable Dopaminergic Precursor Cells Derived from Human Pluripotent Stem Cells

Directory of Open Access Journals (Sweden)

Daniela Lehnen

2017-10-01

Full Text Available Human pluripotent stem cell (hPSC-derived mesencephalic dopaminergic (mesDA neurons can relieve motor deficits in animal models of Parkinson's disease (PD. Clinical translation of differentiation protocols requires standardization of production procedures, and surface-marker-based cell sorting is considered instrumental for reproducible generation of defined cell products. Here, we demonstrate that integrin-associated protein (IAP is a cell surface marker suitable for enrichment of hPSC-derived mesDA progenitor cells. Immunomagnetically sorted IAP+ mesDA progenitors showed increased expression of ventral midbrain floor plate markers, lacked expression of pluripotency markers, and differentiated into mature dopaminergic (DA neurons in vitro. Intrastriatal transplantation of IAP+ cells sorted at day 16 of differentiation in a rat model of PD resulted in functional recovery. Grafts from sorted IAP+ mesDA progenitors were more homogeneous in size and DA neuron density. Thus, we suggest IAP-based sorting for reproducible prospective enrichment of mesDA progenitor cells in clinical cell replacement strategies.

The 2015 Fillmore earthquake swarm and possible crustal deformation mechanisms near the bottom of the eastern Ventura Basin, California

Science.gov (United States)

Hauksson, Egill; Andrews, Jennifer; Plesch, Andreas; Shaw, John H.; Shelly, David R.

2016-01-01

The 2015 Fillmore swarm occurred about 6 km west of the city of Fillmore in Ventura, California, and was located beneath the eastern part of the actively subsiding Ventura basin at depths from 11.8 to 13.8 km, similar to two previous swarms in the area. Template‐matching event detection showed that it started on 5 July 2015 at 2:21 UTC with an M∼1.0 earthquake. The swarm exhibited unusual episodic spatial and temporal migrations and unusual diversity in the nodal planes of the focal mechanisms as compared to the simple hypocenter‐defined plane. It was also noteworthy because it consisted of >1400 events of M≥0.0, with M 2.8 being the largest event. We suggest that fluids released by metamorphic dehydration processes, migration of fluids along a detachment zone, and cascading asperity failures caused this prolific earthquake swarm, but other mechanisms (such as simple mainshock–aftershock stress triggering or a regional aseismic creep event) are less likely. Dilatant strengthening may be a mechanism that causes the temporal decay of the swarm as pore‐pressure drop increased the effective normal stress, and counteracted the instability driving the swarm.

Dome growth behavior at Soufriere Hills Volcano, Montserrat, revealed by relocation of volcanic event swarms, 1995-1996

Science.gov (United States)

Rowe, C.A.; Thurber, C.H.; White, R.A.

2004-01-01

We have relocated a subset of events from the digital waveform catalogue of ???17,000 volcanic microearthquakes recorded between July 1995 and February 1996 at Soufriere Hills Volcano (SHV), Montserrat, using a cross-correlation-based phase repicking technique with a joint location method. Hypocenters were estimated for 3914 earthquakes having five or more corrected P-wave picks. The seismic source region collapsed to a volume of ???1 km3 from an initial ???100 km3. Relocated events represent 36 swarms, each containing nearly identical waveforms, having source dimensions of 10 to 100 m in diameter and spatial separations on the order of 500 m or less. Each swarm occurred over a span of several hours to a few days.Triggered data appear to miss between 65% and 98% of the events that occur within these swarms, based on review of helicorder records. Visual estimates of summit dome growth show a rough correspondence between episodes of intense swarming and increases in extruded magma, although dome observations are too sparse to make a direct comparison for this time period. The limited depth range over which dome-growth-related events occur is consistent with a dynamic model of cyclic plug extrusion behavior in the shallow conduit, governed by magma supply rate, overpressure buildup and physical properties of the magma and conduit geometry. Seismic sources may occur in locally overpressured regions that result from microlite formation in a zone of rapid decompression; we propose that this zone exists in the vicinity of a detachment plane associated with the cyclic plug extrusion. ?? 2004 Elsevier B.V. All rights reserved.

Chaotic particle swarm optimization for economic dispatch considering the generator constraints

International Nuclear Information System (INIS)

Cai, Jiejin; Ma, Xiaoqian; Li, Lixiang; Haipeng, Peng

2007-01-01

Chaotic particle swarm optimization (CPSO) methods are optimization approaches based on the proposed particle swarm optimization (PSO) with adaptive inertia weight factor (AIWF) and chaotic local search (CLS). In this paper, two CPSO methods based on the logistic equation and the Tent equation are presented to solve economic dispatch (ED) problems with generator constraints and applied in two power system cases. Compared with the traditional PSO method, the convergence iterative numbers of the CPSO methods are reduced, and the solutions generation costs decrease around 5 $/h in the six unit system and 24 $/h in the 15 unit system. The simulation results show that the CPSO methods have good convergence property. The generation costs of the CPSO methods are lower than those of the traditional particle swarm optimization algorithm, and hence, CPSO methods can result in great economic effect. For economic dispatch problems, the CPSO methods are more feasible and more effective alternative approaches than the traditional particle swarm optimization algorithm

Swarm.

Science.gov (United States)

Petersen, Hugh

2002-01-01

Describes an eighth grade art project for which students created bug swarms on scratchboard. Explains that the project also teaches students about design principles, such as balance. Discusses how the students created their drawings. (CMK)

Neuronal spike sorting based on radial basis function neural networks

Directory of Open Access Journals (Sweden)

Taghavi Kani M

2011-02-01

Full Text Available "nBackground: Studying the behavior of a society of neurons, extracting the communication mechanisms of brain with other tissues, finding treatment for some nervous system diseases and designing neuroprosthetic devices, require an algorithm to sort neuralspikes automatically. However, sorting neural spikes is a challenging task because of the low signal to noise ratio (SNR of the spikes. The main purpose of this study was to design an automatic algorithm for classifying neuronal spikes that are emitted from a specific region of the nervous system."n "nMethods: The spike sorting process usually consists of three stages: detection, feature extraction and sorting. We initially used signal statistics to detect neural spikes. Then, we chose a limited number of typical spikes as features and finally used them to train a radial basis function (RBF neural network to sort the spikes. In most spike sorting devices, these signals are not linearly discriminative. In order to solve this problem, the aforesaid RBF neural network was used."n "nResults: After the learning process, our proposed algorithm classified any arbitrary spike. The obtained results showed that even though the proposed Radial Basis Spike Sorter (RBSS reached to the same error as the previous methods, however, the computational costs were much lower compared to other algorithms. Moreover, the competitive points of the proposed algorithm were its good speed and low computational complexity."n "nConclusion: Regarding the results of this study, the proposed algorithm seems to serve the purpose of procedures that require real-time processing and spike sorting.

The Swarm Satellite Constellation Application and Research Facility (SCARF) and Swarm data products

DEFF Research Database (Denmark)

Olsen, Nils; Friis-Christensen, Eigil; Floberghagen, R.

2013-01-01

Swarm, a three-satellite constellation to study the dynamics of the Earth's magnetic field and its interactions with the Earth system, is expected to be launched in late 2013. The objective of the Swarm mission is to provide the best ever survey of the geomagnetic field and its temporal evolution...

Design and control of swarm dynamics

CERN Document Server

Bouffanais, Roland

2016-01-01

The book is about the key elements required for designing, building and controlling effective artificial swarms comprised of multiple moving physical agents. Therefore this book presents the fundamentals of each of those key elements in the particular frame of dynamic swarming, specifically exposing the profound connections between these elements and establish some general design principles for swarming behaviors. This scientific endeavor requires an inter-disciplinary approach: biomimetic inspiration from ethology and ecology, study of social information flow, analysis of temporal and adaptive signaling network of interaction, considerations of control of networked real-time systems, and lastly, elements of complex adaptive dynamical systems. This book offers a completely new perspective on the scientific understanding of dynamic collective behaviors thanks to its multi-disciplinary approach and its focus on artificial swarm of physical agents. Two of the key problems in understanding the emergence of swarm ...

Energy group structure determination using particle swarm optimization

International Nuclear Information System (INIS)

Yi, Ce; Sjoden, Glenn

2013-01-01

Highlights: ► Particle swarm optimization is applied to determine broad group structure. ► A graph representation of the broad group structure problem is introduced. ► The approach is tested on a fuel-pin model. - Abstract: Multi-group theory is widely applied for the energy domain discretization when solving the Linear Boltzmann Equation. To reduce the computational cost, fine group cross libraries are often down-sampled into broad group cross section libraries. Cross section data collapsing generally involves two steps: Firstly, the broad group structure has to be determined; secondly, a weighting scheme is used to evaluate the broad cross section library based on the fine group cross section data and the broad group structure. A common scheme is to average the fine group cross section weighted by the fine group flux. Cross section collapsing techniques have been intensively researched. However, most studies use a pre-determined group structure, open based on experience, to divide the neutron energy spectrum into thermal, epi-thermal, fast, etc. energy range. In this paper, a swarm intelligence algorithm, particle swarm optimization (PSO), is applied to optimize the broad group structure. A graph representation of the broad group structure determination problem is introduced. And the swarm intelligence algorithm is used to solve the graph model. The effectiveness of the approach is demonstrated using a fuel-pin model

Scouts behave as streakers in honeybee swarms

Science.gov (United States)

Greggers, Uwe; Schöning, Caspar; Degen, Jacqueline; Menzel, Randolf

2013-08-01

Harmonic radar tracking was used to record the flights of scout bees during takeoff and initial flight path of two honeybee swarms. One swarm remained intact and performed a full flight to a destination beyond the range of the harmonic radar, while a second swarm disintegrated within the range of the radar and most of the bees returned to the queen. The initial stretch of the full flight is characterized by accelerating speed, whereas the disintegrating swarm flew steadily at low speed. The two scouts in the swarm displaying full flight performed characteristic flight maneuvers. They flew at high speed when traveling in the direction of their destination and slowed down or returned over short stretches at low speed. Scouts in the disintegrating swarm did not exhibit the same kind of characteristic flight performance. Our data support the streaker bee hypothesis proposing that scout bees guide the swarm by traveling at high speed in the direction of the new nest site for short stretches of flight and slowing down when reversing flight direction.

Perbandingan Bubble Sort dengan Insertion Sort pada Bahasa Pemrograman C dan Fortran

Directory of Open Access Journals (Sweden)

Reina Reina

2013-12-01

Full Text Available Sorting is a basic algorithm studied by students of computer science major. Sorting algorithm is the basis of other algorithms such as searching algorithm, pattern matching algorithm. Bubble sort is a popular basic sorting algorithm due to its easiness to be implemented. Besides bubble sort, there is insertion sort. It is lesspopular than bubble sort because it has more difficult algorithm. This paper discusses about process time between insertion sort and bubble sort with two kinds of data. First is randomized data, and the second is data of descending list. Comparison of process time has been done in two kinds of programming language that is C programming language and FORTRAN programming language. The result shows that bubble sort needs more time than insertion sort does.

PhySortR: a fast, flexible tool for sorting phylogenetic trees in R.

Science.gov (United States)

Stephens, Timothy G; Bhattacharya, Debashish; Ragan, Mark A; Chan, Cheong Xin

2016-01-01

A frequent bottleneck in interpreting phylogenomic output is the need to screen often thousands of trees for features of interest, particularly robust clades of specific taxa, as evidence of monophyletic relationship and/or reticulated evolution. Here we present PhySortR, a fast, flexible R package for classifying phylogenetic trees. Unlike existing utilities, PhySortR allows for identification of both exclusive and non-exclusive clades uniting the target taxa based on tip labels (i.e., leaves) on a tree, with customisable options to assess clades within the context of the whole tree. Using simulated and empirical datasets, we demonstrate the potential and scalability of PhySortR in analysis of thousands of phylogenetic trees without a priori assumption of tree-rooting, and in yielding readily interpretable trees that unambiguously satisfy the query. PhySortR is a command-line tool that is freely available and easily automatable.

Size-based cell sorting with a resistive pulse sensor and an electromagnetic pump in a microfluidic chip.

Science.gov (United States)

Song, Yongxin; Li, Mengqi; Pan, Xinxiang; Wang, Qi; Li, Dongqing

2015-02-01

An electrokinetic microfluidic chip is developed to detect and sort target cells by size from human blood samples. Target-cell detection is achieved by a differential resistive pulse sensor (RPS) based on the size difference between the target cell and other cells. Once a target cell is detected, the detected RPS signal will automatically actuate an electromagnetic pump built in a microchannel to push the target cell into a collecting channel. This method was applied to automatically detect and sort A549 cells and T-lymphocytes from a peripheral fingertip blood sample. The viability of A549 cells sorted in the collecting well was verified by Hoechst33342 and propidium iodide staining. The results show that as many as 100 target cells per minute can be sorted out from the sample solution and thus is particularly suitable for sorting very rare target cells, such as circulating tumor cells. The actuation of the electromagnetic valve has no influence on RPS cell detection and the consequent cell-sorting process. The viability of the collected A549 cell is not impacted by the applied electric field when the cell passes the RPS detection area. The device described in this article is simple, automatic, and label-free and has wide applications in size-based rare target cell sorting for medical diagnostics. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Spiking Neural Networks Based on OxRAM Synapses for Real-Time Unsupervised Spike Sorting.

Science.gov (United States)

Werner, Thilo; Vianello, Elisa; Bichler, Olivier; Garbin, Daniele; Cattaert, Daniel; Yvert, Blaise; De Salvo, Barbara; Perniola, Luca

2016-01-01

In this paper, we present an alternative approach to perform spike sorting of complex brain signals based on spiking neural networks (SNN). The proposed architecture is suitable for hardware implementation by using resistive random access memory (RRAM) technology for the implementation of synapses whose low latency (spike sorting. This offers promising advantages to conventional spike sorting techniques for brain-computer interfaces (BCI) and neural prosthesis applications. Moreover, the ultra-low power consumption of the RRAM synapses of the spiking neural network (nW range) may enable the design of autonomous implantable devices for rehabilitation purposes. We demonstrate an original methodology to use Oxide based RRAM (OxRAM) as easy to program and low energy (Spike Timing Dependent Plasticity. Real spiking data have been recorded both intra- and extracellularly from an in-vitro preparation of the Crayfish sensory-motor system and used for validation of the proposed OxRAM based SNN. This artificial SNN is able to identify, learn, recognize and distinguish between different spike shapes in the input signal with a recognition rate about 90% without any supervision.

Optimization of multi-objective micro-grid based on improved particle swarm optimization algorithm

Science.gov (United States)

Zhang, Jian; Gan, Yang

2018-04-01

The paper presents a multi-objective optimal configuration model for independent micro-grid with the aim of economy and environmental protection. The Pareto solution set can be obtained by solving the multi-objective optimization configuration model of micro-grid with the improved particle swarm algorithm. The feasibility of the improved particle swarm optimization algorithm for multi-objective model is verified, which provides an important reference for multi-objective optimization of independent micro-grid.

Dynamic scaling in natural swarms

Science.gov (United States)

Cavagna, Andrea; Conti, Daniele; Creato, Chiara; Del Castello, Lorenzo; Giardina, Irene; Grigera, Tomas S.; Melillo, Stefania; Parisi, Leonardo; Viale, Massimiliano

2017-09-01

Collective behaviour in biological systems presents theoretical challenges beyond the borders of classical statistical physics. The lack of concepts such as scaling and renormalization is particularly problematic, as it forces us to negotiate details whose relevance is often hard to assess. In an attempt to improve this situation, we present here experimental evidence of the emergence of dynamic scaling laws in natural swarms of midges. We find that spatio-temporal correlation functions in different swarms can be rescaled by using a single characteristic time, which grows with the correlation length with a dynamical critical exponent z ~ 1, a value not found in any other standard statistical model. To check whether out-of-equilibrium effects may be responsible for this anomalous exponent, we run simulations of the simplest model of self-propelled particles and find z ~ 2, suggesting that natural swarms belong to a novel dynamic universality class. This conclusion is strengthened by experimental evidence of the presence of non-dissipative modes in the relaxation, indicating that previously overlooked inertial effects are needed to describe swarm dynamics. The absence of a purely dissipative regime suggests that natural swarms undergo a near-critical censorship of hydrodynamics.

Seismicity-based estimation of the driving fluid pressure in the case of swarm activity in Western Bohemia

Science.gov (United States)

Hainzl, S.; Fischer, T.; Dahm, T.

2012-10-01

Two recent major swarms in Western Bohemia occurred in the years 2000 and 2008 within almost the same portion of a fault close to Novy Kostel. Previous analysis of the year 2000 earthquake swarm revealed that fluid intrusion seemed to initiate the activity whereas stress redistribution by the individual swarm earthquakes played a major role in the further swarm evolution. Here we analyse the new swarm, which occurred in the year 2008, with regard to its correlation to the previous swarm as well its spatiotemporal migration patterns. We find that (i) the main part of the year 2008 activity ruptured fault patches adjacent to the main activity of the swarm 2000, but that also (ii) a significant overlap exists where earthquakes occurred in patches in which stress had been already released by precursory events; (iii) the activity shows a clear migration which can be described by a 1-D (in up-dip direction) diffusion process; (iv) the migration pattern can be equally well explained by a hydrofracture growth, which additionally explains the faster migration in up-dip compared to the down-dip direction as well as the maximum up-dip extension of the activity. We use these observations to estimate the underlying fluid pressure change in two different ways: First, we calculate the stress changes induced by precursory events at the location of each swarm earthquake assuming that observed stress deficits had to be compensated by pore pressure increases; and secondly, we estimate the fluid overpressure by fitting a hydrofracture model to the asymmetric seismicity patterns. Both independent methods indicate that the fluid pressure increase was initially up to 30 MPa.

Swarm Science objectives and challenges

DEFF Research Database (Denmark)

Friis-Christensen, Eigil; Lühr, Hermann; Hulot, Gauthier

Swarm is the fifth Earth Explorer mission in ESA’s Living Planet Programme to be launched in 2009. The objective of the Swarm mission is to provide the best ever survey of the geomagnetic field and its temporal evolution. The innovative constellation concept and a unique set of dedicated instrume......Swarm is the fifth Earth Explorer mission in ESA’s Living Planet Programme to be launched in 2009. The objective of the Swarm mission is to provide the best ever survey of the geomagnetic field and its temporal evolution. The innovative constellation concept and a unique set of dedicated...... instruments will provide the necessary observations that are required to separate and model the various sources of the geomagnetic field. This will provide new insights into the Earth system by improving our understanding of the Earth’s interior and Sun-Earth connection processes....

Multiswarm comprehensive learning particle swarm optimization for solving multiobjective optimization problems.

Science.gov (United States)

Yu, Xiang; Zhang, Xueqing

2017-01-01

Comprehensive learning particle swarm optimization (CLPSO) is a powerful state-of-the-art single-objective metaheuristic. Extending from CLPSO, this paper proposes multiswarm CLPSO (MSCLPSO) for multiobjective optimization. MSCLPSO involves multiple swarms, with each swarm associated with a separate original objective. Each particle's personal best position is determined just according to the corresponding single objective. Elitists are stored externally. MSCLPSO differs from existing multiobjective particle swarm optimizers in three aspects. First, each swarm focuses on optimizing the associated objective using CLPSO, without learning from the elitists or any other swarm. Second, mutation is applied to the elitists and the mutation strategy appropriately exploits the personal best positions and elitists. Third, a modified differential evolution (DE) strategy is applied to some extreme and least crowded elitists. The DE strategy updates an elitist based on the differences of the elitists. The personal best positions carry useful information about the Pareto set, and the mutation and DE strategies help MSCLPSO discover the true Pareto front. Experiments conducted on various benchmark problems demonstrate that MSCLPSO can find nondominated solutions distributed reasonably over the true Pareto front in a single run.

Merging the fields of swarm robotics and new media: Perceiving swarm robotics as new media

OpenAIRE

Monika O. Ivanova; Micael S. Couceiro; Fernando M. L. Martins

2014-01-01

The aim of this paper is to provide evidence that swarm robotic systems can be perceived as new media objects. A thorough description of the five principles of new media proposed by Lev Manovich in “The Language of New Media” is presented. This is complemented by a state of the art on swarm robotics with an in-depth comparison of the characteristics of both fields. Also presented are examples of swarm robotics used in new media installations in order to illustrate the cuttin...

Using pico-LCoS SLMs for high speed cell sorting

DEFF Research Database (Denmark)

Bañas, Andrew Rafael; Aabo, Thomas; Palima, Darwin

2012-01-01

We propose the use of consumer pico projectors as cost effective spatial light modulators in cell sorting applications. The matched filtering Generalized Phase Contrast (mGPC) beam shaping method is used to produce high intensity optical spots for trapping and catapulting cells. A pico projector......’s liquid crystal on silicon (LCoS) chip was used as a binary phase spatial light modulator (SLM) wherein correlation target patterns are addressed. Experiments using the binary LCoS phase SLM with a fabricated Pyrex matched filter demonstrate the generation of intense optical spots that can potentially...... be used for cell sorting. Numerical studies also show mGPC’s robustness to phase aberrations in the LCoS device, and its ability to outperform a top hat beam with the same power....

IAP-Based Cell Sorting Results in Homogeneous Transplantable Dopaminergic Precursor Cells Derived from Human Pluripotent Stem Cells.

Science.gov (United States)

Lehnen, Daniela; Barral, Serena; Cardoso, Tiago; Grealish, Shane; Heuer, Andreas; Smiyakin, Andrej; Kirkeby, Agnete; Kollet, Jutta; Cremer, Harold; Parmar, Malin; Bosio, Andreas; Knöbel, Sebastian

2017-10-10

Human pluripotent stem cell (hPSC)-derived mesencephalic dopaminergic (mesDA) neurons can relieve motor deficits in animal models of Parkinson's disease (PD). Clinical translation of differentiation protocols requires standardization of production procedures, and surface-marker-based cell sorting is considered instrumental for reproducible generation of defined cell products. Here, we demonstrate that integrin-associated protein (IAP) is a cell surface marker suitable for enrichment of hPSC-derived mesDA progenitor cells. Immunomagnetically sorted IAP + mesDA progenitors showed increased expression of ventral midbrain floor plate markers, lacked expression of pluripotency markers, and differentiated into mature dopaminergic (DA) neurons in vitro. Intrastriatal transplantation of IAP + cells sorted at day 16 of differentiation in a rat model of PD resulted in functional recovery. Grafts from sorted IAP + mesDA progenitors were more homogeneous in size and DA neuron density. Thus, we suggest IAP-based sorting for reproducible prospective enrichment of mesDA progenitor cells in clinical cell replacement strategies. Copyright © 2017 Miltenyi Biotec GmbH. Published by Elsevier Inc. All rights reserved.

Robust design of broadband EUV multilayer beam splitters based on particle swarm optimization

International Nuclear Information System (INIS)

Jiang, Hui; Michette, Alan G.

2013-01-01

A robust design idea for broadband EUV multilayer beam splitters is introduced that achieves the aim of decreasing the influence of layer thickness errors on optical performances. Such beam splitters can be used in interferometry to determine the quality of EUVL masks by comparing with a reference multilayer. In the optimization, particle swarm techniques were used for the first time in such designs. Compared to conventional genetic algorithms, particle swarm optimization has stronger ergodicity, simpler processing and faster convergence

Collective motion of a class of social foraging swarms

International Nuclear Information System (INIS)

Liu Bo; Chu Tianguang; Wang Long; Wang Zhanfeng

2008-01-01

This paper considers a class of social foraging swarms with a nutrient profile (or an attractant/repellent) and an attraction-repulsion coupling function, which is chosen to guarantee collision avoidance between individuals. The paper also studies non-identical interaction ability or efficiency among different swarm individuals for different profiles. The swarm behavior is a result of a balance between inter-individual interplays as well as the interplays of the swarm individuals (agents) with their environment. It is proved that the individuals of a quasi-reciprocal swarm will aggregate and eventually form a cohesive cluster of finite size for different profiles. It is also shown that the swarm system is completely stable, that is, every solution converges to the set of equilibrium points of the system. Moreover, all the swarm individuals will converge to more favorable areas of the profile under certain conditions. For general non-reciprocal swarms, numerical simulations show that more complex self-organized rotation may occur in the swarms

Swarm algorithms with chaotic jumps for optimization of multimodal functions

Science.gov (United States)

Krohling, Renato A.; Mendel, Eduardo; Campos, Mauro

2011-11-01

In this article, the use of some well-known versions of particle swarm optimization (PSO) namely the canonical PSO, the bare bones PSO (BBPSO) and the fully informed particle swarm (FIPS) is investigated on multimodal optimization problems. A hybrid approach which consists of swarm algorithms combined with a jump strategy in order to escape from local optima is developed and tested. The jump strategy is based on the chaotic logistic map. The hybrid algorithm was tested for all three versions of PSO and simulation results show that the addition of the jump strategy improves the performance of swarm algorithms for most of the investigated optimization problems. Comparison with the off-the-shelf PSO with local topology (l best model) has also been performed and indicates the superior performance of the standard PSO with chaotic jump over the standard both using local topology (l best model).

Noise-robust unsupervised spike sorting based on discriminative subspace learning with outlier handling.

Science.gov (United States)

Keshtkaran, Mohammad Reza; Yang, Zhi

2017-06-01

Spike sorting is a fundamental preprocessing step for many neuroscience studies which rely on the analysis of spike trains. Most of the feature extraction and dimensionality reduction techniques that have been used for spike sorting give a projection subspace which is not necessarily the most discriminative one. Therefore, the clusters which appear inherently separable in some discriminative subspace may overlap if projected using conventional feature extraction approaches leading to a poor sorting accuracy especially when the noise level is high. In this paper, we propose a noise-robust and unsupervised spike sorting algorithm based on learning discriminative spike features for clustering. The proposed algorithm uses discriminative subspace learning to extract low dimensional and most discriminative features from the spike waveforms and perform clustering with automatic detection of the number of the clusters. The core part of the algorithm involves iterative subspace selection using linear discriminant analysis and clustering using Gaussian mixture model with outlier detection. A statistical test in the discriminative subspace is proposed to automatically detect the number of the clusters. Comparative results on publicly available simulated and real in vivo datasets demonstrate that our algorithm achieves substantially improved cluster distinction leading to higher sorting accuracy and more reliable detection of clusters which are highly overlapping and not detectable using conventional feature extraction techniques such as principal component analysis or wavelets. By providing more accurate information about the activity of more number of individual neurons with high robustness to neural noise and outliers, the proposed unsupervised spike sorting algorithm facilitates more detailed and accurate analysis of single- and multi-unit activities in neuroscience and brain machine interface studies.

Noise-robust unsupervised spike sorting based on discriminative subspace learning with outlier handling

Science.gov (United States)

Keshtkaran, Mohammad Reza; Yang, Zhi

2017-06-01

Objective. Spike sorting is a fundamental preprocessing step for many neuroscience studies which rely on the analysis of spike trains. Most of the feature extraction and dimensionality reduction techniques that have been used for spike sorting give a projection subspace which is not necessarily the most discriminative one. Therefore, the clusters which appear inherently separable in some discriminative subspace may overlap if projected using conventional feature extraction approaches leading to a poor sorting accuracy especially when the noise level is high. In this paper, we propose a noise-robust and unsupervised spike sorting algorithm based on learning discriminative spike features for clustering. Approach. The proposed algorithm uses discriminative subspace learning to extract low dimensional and most discriminative features from the spike waveforms and perform clustering with automatic detection of the number of the clusters. The core part of the algorithm involves iterative subspace selection using linear discriminant analysis and clustering using Gaussian mixture model with outlier detection. A statistical test in the discriminative subspace is proposed to automatically detect the number of the clusters. Main results. Comparative results on publicly available simulated and real in vivo datasets demonstrate that our algorithm achieves substantially improved cluster distinction leading to higher sorting accuracy and more reliable detection of clusters which are highly overlapping and not detectable using conventional feature extraction techniques such as principal component analysis or wavelets. Significance. By providing more accurate information about the activity of more number of individual neurons with high robustness to neural noise and outliers, the proposed unsupervised spike sorting algorithm facilitates more detailed and accurate analysis of single- and multi-unit activities in neuroscience and brain machine interface studies.

Teleoperated robotic sorting system

Science.gov (United States)

Roos, Charles E.; Sommer, Edward J.; Parrish, Robert H.; Russell, James R.

2000-01-01

A method and apparatus are disclosed for classifying materials utilizing a computerized touch sensitive screen or other computerized pointing device for operator identification and electronic marking of spatial coordinates of materials to be extracted. An operator positioned at a computerized touch sensitive screen views electronic images of the mixture of materials to be sorted as they are conveyed past a sensor array which transmits sequences of images of the mixture either directly or through a computer to the touch sensitive display screen. The operator manually "touches" objects displayed on the screen to be extracted from the mixture thereby registering the spatial coordinates of the objects within the computer. The computer then tracks the registered objects as they are conveyed and directs automated devices including mechanical means such as air jets, robotic arms, or other mechanical diverters to extract the registered objects.

SWARM-BOT: Pattern Formation in a Swarm of Self-Assembling Mobile Robots

OpenAIRE

El Kamel, A.; Mellouli, K.; Borne, P.; Sahin, E.; Labella, T.H.; Trianni, V.; Deneubourg, J.-L.; Rasse, P.; Floreano, D.; Gambardella, L.M.; Mondada, F.; Nolfi, S.; Dorigo, M.

2002-01-01

In this paper we introduce a new robotic system, called swarm-bot. The system consists of a swarm of mobile robots with the ability to connect to/disconnect from each other to self-assemble into different kinds of structures. First, we describe our vision and the goals of the project. Then we present preliminary results on the formation of patterns obtained from a grid-world simulation of the system.

The Fate of Colloidal Swarms in Fractures

Science.gov (United States)

Pyrak-Nolte, L. J.; Olander, M. K.

2009-12-01

In the next 10-20 years, nano- and micro-sensor engineering will advance to the stage where sensor swarms could be deployed in the subsurface to probe rock formations and the fluids contained in them. Sensor swarms are groups of nano- or micro- sensors that are maintained as a coherent group to enable either sensor-to-sensor communication and/or coherent transmission of information as a group. The ability to maintain a swarm of sensors depends on the complexity of the flow paths in the rock, on the size and shape of the sensors and on the chemical interaction among the sensors, fluids, and rock surfaces. In this study, we investigate the effect of fracture aperture and fluid currents on the formation, evolution and break-up of colloidal swarms under gravity. Transparent cubic samples (100 mm x 100 mm x 100 mm) containing synthetic fractures with uniform and non-uniform aperture distributions were used to quantify the effect of aperture on swarm formation, swarm velocity, and swarm geometry using optical imaging. A fracture with a uniform aperture distribution was fabricated from two polished rectangular prisms of acrylic. A fracture with a non-uniform aperture distribution was created with a polished rectangular acrylic prism and an acrylic replica of an induced fracture surface from a carbonate rock. A series of experiments were performed to determine how swarm movement and geometry are affected as the walls of the fracture are brought closer together from 50 mm to 1 mm. During the experiments, the fracture was fully saturated with water. We created the swarms using two different particle sizes in dilute suspension (~ 1.0% by mass) . The particles were 3 micron diameter fluorescent polymer beads and 25 micron diameter soda-lime glass beads. The swarm behavior was imaged using an optical fluorescent imaging system composed of a CCD camera illuminated by a 100 mW diode-pumped doubled YAG laser. A swam was created when approximately 0.01 g drop of the suspension was

Big Five Measurement via Q-Sort

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Chris D. Fluckinger

2014-08-01

Full Text Available Socially desirable responding presents a difficult challenge in measuring personality. I tested whether a partially ipsative measure—a normatively scored Q-sort containing traditional Big Five items—would produce personality scores indicative of less socially desirable responding compared with Likert-based measures. Across both instructions to respond honestly and in the context of applying for a job, the Q-sort produced lower mean scores, lower intercorrelations between dimensions, and similar validity in predicting supervisor performance ratings to Likert. In addition, the Q-sort produced a more orthogonal structure (but not fully orthogonal when modeled at the latent level. These results indicate that the Q-sort method did constrain socially desirable responding. Researchers and practitioners should consider Big Five measurement via Q-sort for contexts in which high socially desirable responding is expected.

Elitism set based particle swarm optimization and its application

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Yanxia Sun

2017-01-01

Full Text Available Topology plays an important role for Particle Swarm Optimization (PSO to achieve good optimization performance. It is difficult to find one topology structure for the particles to achieve better optimization performance than the others since the optimization performance not only depends on the searching abilities of the particles, also depends on the type of the optimization problems. Three elitist set based PSO algorithm without using explicit topology structure is proposed in this paper. An elitist set, which is based on the individual best experience, is used to communicate among the particles. Moreover, to avoid the premature of the particles, different statistical methods have been used in these three proposed methods. The performance of the proposed PSOs is compared with the results of the standard PSO 2011 and several PSO with different topologies, and the simulation results and comparisons demonstrate that the proposed PSO with adaptive probabilistic preference can achieve good optimization performance.

Improving Vector Evaluated Particle Swarm Optimisation by incorporating nondominated solutions.

Science.gov (United States)

Lim, Kian Sheng; Ibrahim, Zuwairie; Buyamin, Salinda; Ahmad, Anita; Naim, Faradila; Ghazali, Kamarul Hawari; Mokhtar, Norrima

2013-01-01

The Vector Evaluated Particle Swarm Optimisation algorithm is widely used to solve multiobjective optimisation problems. This algorithm optimises one objective using a swarm of particles where their movements are guided by the best solution found by another swarm. However, the best solution of a swarm is only updated when a newly generated solution has better fitness than the best solution at the objective function optimised by that swarm, yielding poor solutions for the multiobjective optimisation problems. Thus, an improved Vector Evaluated Particle Swarm Optimisation algorithm is introduced by incorporating the nondominated solutions as the guidance for a swarm rather than using the best solution from another swarm. In this paper, the performance of improved Vector Evaluated Particle Swarm Optimisation algorithm is investigated using performance measures such as the number of nondominated solutions found, the generational distance, the spread, and the hypervolume. The results suggest that the improved Vector Evaluated Particle Swarm Optimisation algorithm has impressive performance compared with the conventional Vector Evaluated Particle Swarm Optimisation algorithm.

Pareto-Ranking Based Quantum-Behaved Particle Swarm Optimization for Multiobjective Optimization

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Na Tian

2015-01-01

Full Text Available A study on pareto-ranking based quantum-behaved particle swarm optimization (QPSO for multiobjective optimization problems is presented in this paper. During the iteration, an external repository is maintained to remember the nondominated solutions, from which the global best position is chosen. The comparison between different elitist selection strategies (preference order, sigma value, and random selection is performed on four benchmark functions and two metrics. The results demonstrate that QPSO with preference order has comparative performance with sigma value according to different number of objectives. Finally, QPSO with sigma value is applied to solve multiobjective flexible job-shop scheduling problems.

Alarm systems detect volcanic tremor and earthquake swarms during Redoubt eruption, 2009

Science.gov (United States)

Thompson, G.; West, M. E.

2009-12-01

We ran two alarm algorithms on real-time data from Redoubt volcano during the 2009 crisis. The first algorithm was designed to detect escalations in continuous seismicity (tremor). This is implemented within an application called IceWeb which computes reduced displacement, and produces plots of reduced displacement and spectrograms linked to the Alaska Volcano Observatory internal webpage every 10 minutes. Reduced displacement is a measure of the amplitude of volcanic tremor, and is computed by applying a geometrical spreading correction to a displacement seismogram. When the reduced displacement at multiple stations exceeds pre-defined thresholds and there has been a factor of 3 increase in reduced displacement over the previous hour, a tremor alarm is declared. The second algorithm was to designed to detect earthquake swarms. The mean and median event rates are computed every 5 minutes based on the last hour of data from a real-time event catalog. By comparing these with thresholds, three swarm alarm conditions can be declared: a new swarm, an escalation in a swarm, and the end of a swarm. The end of swarm alarm is important as it may mark a transition from swarm to continuous tremor. Alarms from both systems were dispatched using a generic alarm management system which implements a call-down list, allowing observatory scientists to be called in sequence until someone acknowledged the alarm via a confirmation web page. The results of this simple approach are encouraging. The tremor alarm algorithm detected 26 of the 27 explosive eruptions that occurred from 23 March - 4 April. The swarm alarm algorithm detected all five of the main volcanic earthquake swarm episodes which occurred during the Redoubt crisis on 26-27 February, 21-23 March, 26 March, 2-4 April and 3-7 May. The end-of-swarm alarms on 23 March and 4 April were particularly helpful as they were caused by transitions from swarm to tremor shortly preceding explosive eruptions; transitions which were

Particle swarm genetic algorithm and its application

International Nuclear Information System (INIS)

Liu Chengxiang; Yan Changxiang; Wang Jianjun; Liu Zhenhai

2012-01-01

To solve the problems of slow convergence speed and tendency to fall into the local optimum of the standard particle swarm optimization while dealing with nonlinear constraint optimization problem, a particle swarm genetic algorithm is designed. The proposed algorithm adopts feasibility principle handles constraint conditions and avoids the difficulty of penalty function method in selecting punishment factor, generates initial feasible group randomly, which accelerates particle swarm convergence speed, and introduces genetic algorithm crossover and mutation strategy to avoid particle swarm falls into the local optimum Through the optimization calculation of the typical test functions, the results show that particle swarm genetic algorithm has better optimized performance. The algorithm is applied in nuclear power plant optimization, and the optimization results are significantly. (authors)

A lithospheric magnetic field model derived from the Swarm satellite magnetic field measurements

Science.gov (United States)

Hulot, G.; Thebault, E.; Vigneron, P.

2015-12-01

The Swarm constellation of satellites was launched in November 2013 and has since then delivered high quality scalar and vector magnetic field measurements. A consortium of several research institutions was selected by the European Space Agency (ESA) to provide a number of scientific products which will be made available to the scientific community. Within this framework, specific tools were tailor-made to better extract the magnetic signal emanating from Earth's the lithospheric. These tools rely on the scalar gradient measured by the lower pair of Swarm satellites and rely on a regional modeling scheme that is more sensitive to small spatial scales and weak signals than the standard spherical harmonic modeling. In this presentation, we report on various activities related to data analysis and processing. We assess the efficiency of this dedicated chain for modeling the lithospheric magnetic field using more than one year of measurements, and finally discuss refinements that are continuously implemented in order to further improve the robustness and the spatial resolution of the lithospheric field model.

Transport of Particle Swarms Through Variable Aperture Fractures

Science.gov (United States)

Boomsma, E.; Pyrak-Nolte, L. J.

2012-12-01

Particle transport through fractured rock is a key concern with the increased use of micro- and nano-size particles in consumer products as well as from other activities in the sub- and near surface (e.g. mining, industrial waste, hydraulic fracturing, etc.). While particle transport is often studied as the transport of emulsions or dispersions, particles may also enter the subsurface from leaks or seepage that lead to particle swarms. Swarms are drop-like collections of millions of colloidal-sized particles that exhibit a number of unique characteristics when compared to dispersions and emulsions. Any contaminant or engineered particle that forms a swarm can be transported farther, faster, and more cohesively in fractures than would be expected from a traditional dispersion model. In this study, the effects of several variable aperture fractures on colloidal swarm cohesiveness and evolution were studied as a swarm fell under gravity and interacted with the fracture walls. Transparent acrylic was used to fabricate synthetic fracture samples with (1) a uniform aperture, (2) a converging region followed by a uniform region (funnel shaped), (3) a uniform region followed by a diverging region (inverted funnel), and (4) a cast of a an induced fracture from a carbonate rock. All of the samples consisted of two blocks that measured 100 x 100 x 50 mm. The minimum separation between these blocks determined the nominal aperture (0.5 mm to 20 mm). During experiments a fracture was fully submerged in water and swarms were released into it. The swarms consisted of a dilute suspension of 3 micron polystyrene fluorescent beads (1% by mass) with an initial volume of 5μL. The swarms were illuminated with a green (525 nm) LED array and imaged optically with a CCD camera. The variation in fracture aperture controlled swarm behavior. Diverging apertures caused a sudden loss of confinement that resulted in a rapid change in the swarm's shape as well as a sharp increase in its velocity

Swarm Optimization-Based Magnetometer Calibration for Personal Handheld Devices

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Naser El-Sheimy

2012-09-01

Full Text Available Inertial Navigation Systems (INS consist of accelerometers, gyroscopes and a processor that generates position and orientation solutions by integrating the specific forces and rotation rates. In addition to the accelerometers and gyroscopes, magnetometers can be used to derive the user heading based on Earth’s magnetic field. Unfortunately, the measurements of the magnetic field obtained with low cost sensors are usually corrupted by several errors, including manufacturing defects and external electro-magnetic fields. Consequently, proper calibration of the magnetometer is required to achieve high accuracy heading measurements. In this paper, a Particle Swarm Optimization (PSO-based calibration algorithm is presented to estimate the values of the bias and scale factor of low cost magnetometers. The main advantage of this technique is the use of the artificial intelligence which does not need any error modeling or awareness of the nonlinearity. Furthermore, the proposed algorithm can help in the development of Pedestrian Navigation Devices (PNDs when combined with inertial sensors and GPS/Wi-Fi for indoor navigation and Location Based Services (LBS applications.

Design and realization of sort manipulator of crystal-angle sort machine

Science.gov (United States)

Wang, Ming-shun; Chen, Shu-ping; Guan, Shou-ping; Zhang, Yao-wei

2005-12-01

It is a current tendency of development in automation technology to replace manpower with manipulators in working places where dangerous, harmful, heavy or repetitive work is involved. The sort manipulator is installed in a crystal-angle sort machine to take the place of manpower, and engaged in unloading and sorting work. It is the outcome of combing together mechanism, electric transmission, and pneumatic element and micro-controller control. The step motor makes the sort manipulator operate precisely. The pneumatic elements make the sort manipulator be cleverer. Micro-controller's software bestows some simple artificial intelligence on the sort manipulator, so that it can precisely repeat its unloading and sorting work. The combination of manipulator's zero position and step motor counting control puts an end to accumulating error in long time operation. A sort manipulator's design in the practice engineering has been proved to be correct and reliable.

Particle swarm approach based on quantum mechanics and harmonic oscillator potential well for economic load dispatch with valve-point effects

International Nuclear Information System (INIS)

Santos Coelho, Leandro dos; Mariani, Viviana Cocco

2008-01-01

Particle swarm optimization (PSO) algorithm is population-based heuristic global search algorithm inspired by social behavior patterns of organisms that live and interact within large groups. The PSO is based on researches on swarms such as fish schooling and bird flocking. Inspired by the classical PSO method and quantum mechanics theories, this work presents a quantum-inspired version of the PSO (QPSO) using the harmonic oscillator potential well (HQPSO) to solve economic dispatch problems. A 13-units test system with incremental fuel cost function that takes into account the valve-point loading effects is used to illustrate the effectiveness of the proposed HQPSO method compared with the simulation results based on the classical PSO, the QPSO, and other optimization algorithms reported in the literature

Particle swarm approach based on quantum mechanics and harmonic oscillator potential well for economic load dispatch with valve-point effects

Energy Technology Data Exchange (ETDEWEB)

dos Santos Coelho, Leandro [Pontifical Catholic University of Parana, PUCPR Industrial and Systems Engineering Graduate Program, PPGEPS, Imaculada Conceicao, 1155, Zip code 80215-901, Curitiba, PR (Brazil); Mariani, Viviana Cocco [Pontifical Catholic University of Parana, PUCPR Mechanical Engineering Graduate Program, PPGEM, Imaculada Conceicao, 1155, Zip code 80215-901, Curitiba, PR (Brazil)

2008-11-15

Particle swarm optimization (PSO) algorithm is population-based heuristic global search algorithm inspired by social behavior patterns of organisms that live and interact within large groups. The PSO is based on researches on swarms such as fish schooling and bird flocking. Inspired by the classical PSO method and quantum mechanics theories, this work presents a quantum-inspired version of the PSO (QPSO) using the harmonic oscillator potential well (HQPSO) to solve economic dispatch problems. A 13-units test system with incremental fuel cost function that takes into account the valve-point loading effects is used to illustrate the effectiveness of the proposed HQPSO method compared with the simulation results based on the classical PSO, the QPSO, and other optimization algorithms reported in the literature. (author)

Particle swarm approach based on quantum mechanics and harmonic oscillator potential well for economic load dispatch with valve-point effects

Energy Technology Data Exchange (ETDEWEB)

Santos Coelho, Leandro dos [Pontifical Catholic University of Parana, PUCPR Industrial and Systems Engineering Graduate Program, PPGEPS, Imaculada Conceicao, 1155, Zip code 80215-901, Curitiba, PR (Brazil)], E-mail: leandro.coelho@pucpr.br; Mariani, Viviana Cocco [Pontifical Catholic University of Parana, PUCPR Mechanical Engineering Graduate Program, PPGEM, Imaculada Conceicao, 1155, Zip code 80215-901, Curitiba, PR (Brazil)], E-mail: viviana.mariani@pucpr.br

2008-11-15

Particle swarm optimization (PSO) algorithm is population-based heuristic global search algorithm inspired by social behavior patterns of organisms that live and interact within large groups. The PSO is based on researches on swarms such as fish schooling and bird flocking. Inspired by the classical PSO method and quantum mechanics theories, this work presents a quantum-inspired version of the PSO (QPSO) using the harmonic oscillator potential well (HQPSO) to solve economic dispatch problems. A 13-units test system with incremental fuel cost function that takes into account the valve-point loading effects is used to illustrate the effectiveness of the proposed HQPSO method compared with the simulation results based on the classical PSO, the QPSO, and other optimization algorithms reported in the literature.

Gene expression in Pseudomonas aeruginosa swarming motility

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Déziel Eric

2010-10-01

Full Text Available Abstract Background The bacterium Pseudomonas aeruginosa is capable of three types of motilities: swimming, twitching and swarming. The latter is characterized by a fast and coordinated group movement over a semi-solid surface resulting from intercellular interactions and morphological differentiation. A striking feature of swarming motility is the complex fractal-like patterns displayed by migrating bacteria while they move away from their inoculation point. This type of group behaviour is still poorly understood and its characterization provides important information on bacterial structured communities such as biofilms. Using GeneChip® Affymetrix microarrays, we obtained the transcriptomic profiles of both bacterial populations located at the tip of migrating tendrils and swarm center of swarming colonies and compared these profiles to that of a bacterial control population grown on the same media but solidified to not allow swarming motility. Results Microarray raw data were corrected for background noise with the RMA algorithm and quantile normalized. Differentially expressed genes between the three conditions were selected using a threshold of 1.5 log2-fold, which gave a total of 378 selected genes (6.3% of the predicted open reading frames of strain PA14. Major shifts in gene expression patterns are observed in each growth conditions, highlighting the presence of distinct bacterial subpopulations within a swarming colony (tendril tips vs. swarm center. Unexpectedly, microarrays expression data reveal that a minority of genes are up-regulated in tendril tip populations. Among them, we found energy metabolism, ribosomal protein and transport of small molecules related genes. On the other hand, many well-known virulence factors genes were globally repressed in tendril tip cells. Swarm center cells are distinct and appear to be under oxidative and copper stress responses. Conclusions Results reported in this study show that, as opposed to

Towards CHAOS-5 - How can Swarm contribute?

DEFF Research Database (Denmark)

Finlay, Chris; Olsen, Nils; Tøffner-Clausen, Lars

2014-01-01

The launch of ESA's satellite trio Swarm in November 2013 opens an exciting new chapter in the observation and monitoring of Earth's magnetic field from space. We report preliminary results from an extension of the CHAOS series of geomagnetic field models to include both scalar and vector field...... observations from the three Swarm satellites, along with the most recent quasi-definitive ground observatory data. The fit of this new update CHAOS field model to the Swarm observations will be presented in detail providing useful insight the initial Swarm data. Enhancements of the CHAOS modelling scheme...

Sambot II: A self-assembly modular swarm robot

Science.gov (United States)

Zhang, Yuchao; Wei, Hongxing; Yang, Bo; Jiang, Cancan

2018-04-01

The new generation of self-assembly modular swarm robot Sambot II, based on the original generation of self-assembly modular swarm robot Sambot, adopting laser and camera module for information collecting, is introduced in this manuscript. The visual control algorithm of Sambot II is detailed and feasibility of the algorithm is verified by the laser and camera experiments. At the end of this manuscript, autonomous docking experiments of two Sambot II robots are presented. The results of experiments are showed and analyzed to verify the feasibility of whole scheme of Sambot II.

Application of Swarm Intelligence Based Routingprotocols for Wireless Adhoc Sensor Network

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Mrutyunjaya PANDA

2011-07-01

Full Text Available The enormous growth of wireless sensor network (WSN research has opined challenges about their ease in implementation and performance evaluation. Efficient swarm intelligence based routing protocols that can be used to obtain the application specific service guarantee are the key design issues in designing a WSN model. In this paper, an experimental testbed is designed with 100 sensor nodes deployed in a dense environment to address the scalability and performance issues of WSN. In this paper, we use Flooded Piggyback (FP and SC-MCBR ant colony based routing along with AODV and MCBR Tree in order to design an efficient WSN model. Finally, simulation results are presented with various performance measures to understand the efficacy of the proposed WSN design.

Design and control of multifunctional sorting and training platform based on PLC control

Science.gov (United States)

Wan, Hongqiang; Ge, Shuai; Han, Peiying; Li, Fancong; Zhang, Simiao

2018-05-01

Electromechanical integration, as a multi-disciplinary subject, has been paid much attention by universities and is widely used in the automation production of enterprises. Aiming at the problem of the lack of control among enterprises and the lack of training among colleges and universities, this paper presents a design of multifunctional sorting training platform based on PLC control. Firstly, the structure of the platform is determined and three-dimensional modeling is done. Then design the platform's aerodynamic control and electrical control. Finally, realize the platform sorting function through PLC programming and configuration software development. The training platform can be used to design the practical training experiment, which has a strong advance and pertinence in the electromechanical integration teaching. At the same time, the platform makes full use of modular thinking to make the sorting modules more flexible. Compared with the traditional training platform, its teaching effect is more significant.

Complex emergent dynamics of anisotropic swarms: Convergence vs oscillation

International Nuclear Information System (INIS)

Chu Tianguang; Wang Long; Chen Tongwen; Mu Shumei

2006-01-01

This paper considers an anisotropic swarm model with a simple attraction and repulsion function. It is shown that the members of a reciprocal swarm will aggregate and eventually form a cohesive cluster of finite size around the swarm center. Moreover, the swarm system is also completely stable, i.e., every solution converges to the set of equilibrium points of the system. These results are also valid for a class of non-reciprocal swarms under the detailed balance condition on coupling weights. For general non-reciprocal swarms, numerical simulations are worked out to demonstrate more complex oscillatory motions in the systems. The study provides further insight into the effect of the interaction pattern on the collective behavior of a swarm system

Improving Vector Evaluated Particle Swarm Optimisation by Incorporating Nondominated Solutions

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Kian Sheng Lim

2013-01-01

Full Text Available The Vector Evaluated Particle Swarm Optimisation algorithm is widely used to solve multiobjective optimisation problems. This algorithm optimises one objective using a swarm of particles where their movements are guided by the best solution found by another swarm. However, the best solution of a swarm is only updated when a newly generated solution has better fitness than the best solution at the objective function optimised by that swarm, yielding poor solutions for the multiobjective optimisation problems. Thus, an improved Vector Evaluated Particle Swarm Optimisation algorithm is introduced by incorporating the nondominated solutions as the guidance for a swarm rather than using the best solution from another swarm. In this paper, the performance of improved Vector Evaluated Particle Swarm Optimisation algorithm is investigated using performance measures such as the number of nondominated solutions found, the generational distance, the spread, and the hypervolume. The results suggest that the improved Vector Evaluated Particle Swarm Optimisation algorithm has impressive performance compared with the conventional Vector Evaluated Particle Swarm Optimisation algorithm.

Swarm: ESA's Magnetic Field Mission

Science.gov (United States)

Plank, G.; Floberghagen, R.; Menard, Y.; Haagmans, R.

2013-12-01

Swarm is the fifth Earth Explorer mission in ESA's Living Planet Programme, and is scheduled for launch in fall 2013. The objective of the Swarm mission is to provide the best-ever survey of the geomagnetic field and its temporal evolution using a constellation of three identical satellites. The mission shall deliver data that allow access to new insights into the Earth system by improved scientific understanding of the Earth's interior and near-Earth electromagnetic environment. After launch and triple satellite release at an initial altitude of about 490 km, a pair of the satellites will fly side-by-side with slowly decaying altitude, while the third satellite will be lifted to 530 km to complete the Swarm constellation. High-precision and high-resolution measurements of the strength, direction and variation of the magnetic field, complemented by precise navigation, accelerometer and electric field measurements, will provide the observations required to separate and model various sources of the geomagnetic field and near-Earth current systems. The mission science goals are to provide a unique view into Earth's core dynamics, mantle conductivity, crustal magnetisation, ionospheric and magnetospheric current systems and upper atmosphere dynamics - ranging from understanding the geodynamo to contributing to space weather. The scientific objectives and results from recent scientific studies will be presented. In addition the current status of the project, which is presently in the final stage of the development phase, will be addressed. A consortium of European scientific institutes is developing a distributed processing system to produce geophysical (Level 2) data products for the Swarm user community. The setup of the Swarm ground segment and the contents of the data products will be addressed. In case the Swarm satellites are already in orbit, a summary of the on-going mission operations activities will be given. More information on Swarm can be found at www.esa.int/esaLP/LPswarm.html.

Algorithmic requirements for swarm intelligence in differently coupled collective systems

International Nuclear Information System (INIS)

Stradner, Jürgen; Thenius, Ronald; Zahadat, Payam; Hamann, Heiko; Crailsheim, Karl; Schmickl, Thomas

2013-01-01

Swarm systems are based on intermediate connectivity between individuals and dynamic neighborhoods. In natural swarms self-organizing principles bring their agents to that favorable level of connectivity. They serve as interesting sources of inspiration for control algorithms in swarm robotics on the one hand, and in modular robotics on the other hand. In this paper we demonstrate and compare a set of bio-inspired algorithms that are used to control the collective behavior of swarms and modular systems: BEECLUST, AHHS (hormone controllers), FGRN (fractal genetic regulatory networks), and VE (virtual embryogenesis). We demonstrate how such bio-inspired control paradigms bring their host systems to a level of intermediate connectivity, what delivers sufficient robustness to these systems for collective decentralized control. In parallel, these algorithms allow sufficient volatility of shared information within these systems to help preventing local optima and deadlock situations, this way keeping those systems flexible and adaptive in dynamic non-deterministic environments

Estimation of Valve Stiction Using Particle Swarm Optimization

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S. Sivagamasundari

2011-06-01

Full Text Available This paper presents a procedure for quantifying valve stiction in control loops based on particle swarm optimization. Measurements of the Process Variable (PV and Controller Output (OP are used to estimate the parameters of a Hammerstein system, consisting of connection of a non linear control valve stiction model and a linear process model. The parameters of the Hammerstein model are estimated using particle swarm optimization, from the input-output data by minimizing the error between the true model output and the identified model output. Using particle swarm optimization, Hammerstein models with known nonlinear structure and unknown parameters can be identified. A cost-effective optimization technique is adopted to find the best valve stiction models representing a more realistic valve behavior in the oscillating loop. Simulation and practical laboratory control system results are included, which demonstrates the effectiveness and robustness of the identification scheme.

Self-regulating and self-evolving particle swarm optimizer

Science.gov (United States)

Wang, Hui-Min; Qiao, Zhao-Wei; Xia, Chang-Liang; Li, Liang-Yu

2015-01-01

In this article, a novel self-regulating and self-evolving particle swarm optimizer (SSPSO) is proposed. Learning from the idea of direction reversal, self-regulating behaviour is a modified position update rule for particles, according to which the algorithm improves the best position to accelerate convergence in situations where the traditional update rule does not work. Borrowing the idea of mutation from evolutionary computation, self-evolving behaviour acts on the current best particle in the swarm to prevent the algorithm from prematurely converging. The performance of SSPSO and four other improved particle swarm optimizers is numerically evaluated by unimodal, multimodal and rotated multimodal benchmark functions. The effectiveness of SSPSO in solving real-world problems is shown by the magnetic optimization of a Halbach-based permanent magnet machine. The results show that SSPSO has good convergence performance and high reliability, and is well matched to actual problems.

IDENTIFICATION OF EARTHQUAKE AFTERSHOCK AND SWARM SEQUENCES IN THE BAIKAL RIFT ZONE

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N. A. Radziminovich

2013-01-01

Full Text Available The catalog of earthquakes (КR³6.6 which occurred in the Baikal rift zone (BRZ was declastered, and the results are presented in the article. Aftershocks of seismic events (КR³12.5 were determined by the software developed by V.B. Smirnov (Lomonosov Moscow State University with application of the algorithm co-authored by G.M. Molchan and O.E. Dmitrieva. To ensure proper control of the software application, aftershocks were also selected manually. The results of declustering show that aftershocks of the earthquakes (КR³12.5 account for about 25 per cent of all seismic events in the regional catalog. Aftershocks accompanied 90 per cent of all the earthquakes considered as main shocks. Besides, earthquake swarms, including events with КR³11, were identified. The results of this study show that, in the BRZ, the swarms and strong events with aftershocks are not spatially separated, and this conclusion differs from the views of the previous studies that reviewed data from a shorter observation period. Moreover, it is noted that the swarms may consist of several main shocks accompanied by aftershocks. The data accumulated over the last fifty years of instrumental observations support the conclusion made earlier that the swarms in BRZ occur mainly in the north-eastward direction from Lake Baikal and also confirm the trend of a small number of aftershocks accompanying earthquakes in the south-western part of the Baikal rift zone.

Infrared and visible image fusion using discrete cosine transform and swarm intelligence for surveillance applications

Science.gov (United States)

Paramanandham, Nirmala; Rajendiran, Kishore

2018-01-01

A novel image fusion technique is presented for integrating infrared and visible images. Integration of images from the same or various sensing modalities can deliver the required information that cannot be delivered by viewing the sensor outputs individually and consecutively. In this paper, a swarm intelligence based image fusion technique using discrete cosine transform (DCT) domain is proposed for surveillance application which integrates the infrared image with the visible image for generating a single informative fused image. Particle swarm optimization (PSO) is used in the fusion process for obtaining the optimized weighting factor. These optimized weighting factors are used for fusing the DCT coefficients of visible and infrared images. Inverse DCT is applied for obtaining the initial fused image. An enhanced fused image is obtained through adaptive histogram equalization for a better visual understanding and target detection. The proposed framework is evaluated using quantitative metrics such as standard deviation, spatial frequency, entropy and mean gradient. The experimental results demonstrate the outperformance of the proposed algorithm over many other state- of- the- art techniques reported in literature.

Surface acoustic wave actuated cell sorting (SAWACS).

Science.gov (United States)

Franke, T; Braunmüller, S; Schmid, L; Wixforth, A; Weitz, D A

2010-03-21

We describe a novel microfluidic cell sorter which operates in continuous flow at high sorting rates. The device is based on a surface acoustic wave cell-sorting scheme and combines many advantages of fluorescence activated cell sorting (FACS) and fluorescence activated droplet sorting (FADS) in microfluidic channels. It is fully integrated on a PDMS device, and allows fast electronic control of cell diversion. We direct cells by acoustic streaming excited by a surface acoustic wave which deflects the fluid independently of the contrast in material properties of deflected objects and the continuous phase; thus the device underlying principle works without additional enhancement of the sorting by prior labelling of the cells with responsive markers such as magnetic or polarizable beads. Single cells are sorted directly from bulk media at rates as fast as several kHz without prior encapsulation into liquid droplet compartments as in traditional FACS. We have successfully directed HaCaT cells (human keratinocytes), fibroblasts from mice and MV3 melanoma cells. The low shear forces of this sorting method ensure that cells survive after sorting.

Swarm Robotics with Circular Formation Motion Including Obstacles Avoidance

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Nabil M. Hewahi

2017-07-01

Full Text Available The robots science has been developed over the past few years, where robots have become used to accomplish difficult, repetitive or accurate tasks, which are very hard for humans to carry out. In this paper, we propose an algorithm to control the motion of a swarm of robots and make them able to avoid obstacles. The proposed solution is based on forming the robots in circular fashion. A group set of robots consists of multiple groups of robots, each group of robots consists of robots forming a circular shape and each group set is a circular form of robots. The proposed algorithm is concerned with first locating the randomly generated robots in groups and secondly with the swarm robot motion and finally with the swarm obstacle avoidance and swarm reorganization after crossing the obstacle. The proposed algorithm has been simulated with five different obstacles with various numbers of randomly generated robots. The results show that the swarm in the circular form can deal with the obstacles very effectively by passing the obstacles smoothly. The proposed algorithm has been compared with flocking algorithm and it is shown that the circular formation algorithm does not need extensive computation after obstacle avoidance whereas the flocking algorithm needs extensive computation. In addition, the circular formation algorithm maintains every robot in its group after avoiding the obstacles whereas with flocking algorithm does not.

Parallel sorting algorithms

CERN Document Server

Akl, Selim G

1985-01-01

Parallel Sorting Algorithms explains how to use parallel algorithms to sort a sequence of items on a variety of parallel computers. The book reviews the sorting problem, the parallel models of computation, parallel algorithms, and the lower bounds on the parallel sorting problems. The text also presents twenty different algorithms, such as linear arrays, mesh-connected computers, cube-connected computers. Another example where algorithm can be applied is on the shared-memory SIMD (single instruction stream multiple data stream) computers in which the whole sequence to be sorted can fit in the

Spike sorting using locality preserving projection with gap statistics and landmark-based spectral clustering.

Science.gov (United States)

Nguyen, Thanh; Khosravi, Abbas; Creighton, Douglas; Nahavandi, Saeid

2014-12-30

Understanding neural functions requires knowledge from analysing electrophysiological data. The process of assigning spikes of a multichannel signal into clusters, called spike sorting, is one of the important problems in such analysis. There have been various automated spike sorting techniques with both advantages and disadvantages regarding accuracy and computational costs. Therefore, developing spike sorting methods that are highly accurate and computationally inexpensive is always a challenge in the biomedical engineering practice. An automatic unsupervised spike sorting method is proposed in this paper. The method uses features extracted by the locality preserving projection (LPP) algorithm. These features afterwards serve as inputs for the landmark-based spectral clustering (LSC) method. Gap statistics (GS) is employed to evaluate the number of clusters before the LSC can be performed. The proposed LPP-LSC is highly accurate and computationally inexpensive spike sorting approach. LPP spike features are very discriminative; thereby boost the performance of clustering methods. Furthermore, the LSC method exhibits its efficiency when integrated with the cluster evaluator GS. The proposed method's accuracy is approximately 13% superior to that of the benchmark combination between wavelet transformation and superparamagnetic clustering (WT-SPC). Additionally, LPP-LSC computing time is six times less than that of the WT-SPC. LPP-LSC obviously demonstrates a win-win spike sorting solution meeting both accuracy and computational cost criteria. LPP and LSC are linear algorithms that help reduce computational burden and thus their combination can be applied into real-time spike analysis. Copyright © 2014 Elsevier B.V. All rights reserved.

Particle Swarm Social Adaptive Model for Multi-Agent Based Insurgency Warfare Simulation

Energy Technology Data Exchange (ETDEWEB)

Cui, Xiaohui [ORNL; Potok, Thomas E [ORNL

2009-12-01

To better understand insurgent activities and asymmetric warfare, a social adaptive model for modeling multiple insurgent groups attacking multiple military and civilian targets is proposed and investigated. This report presents a pilot study using the particle swarm modeling, a widely used non-linear optimal tool to model the emergence of insurgency campaign. The objective of this research is to apply the particle swarm metaphor as a model of insurgent social adaptation for the dynamically changing environment and to provide insight and understanding of insurgency warfare. Our results show that unified leadership, strategic planning, and effective communication between insurgent groups are not the necessary requirements for insurgents to efficiently attain their objective.

Particle Swarm Imaging (PSIM). A swarming algorithm for the reporting of robust, optimal measurement uncertainties

International Nuclear Information System (INIS)

Parvin, Dan; Clarke, Sean

2015-01-01

Particle Swarm Imaging (PSIM) overcomes some of the challenges associated with the accurate declaration of measurement uncertainties of radionuclide inventories within waste items when the distribution of activity is unknown. Implementation requires minimal equipment, making use of gamma‑ray measurements taken from different locations around the waste item, using only a single electrically cooled HRGS gamma‑ray detector for objects up to a UK ISO freight container in size. The PSIM technique is a computational method that iteratively ‘homes‑in’ on the true location of activity concentrations in waste items. PSIM differs from conventional assay techniques by allowing only viable solutions - that is those that could actually give rise to the measured data - to be considered. Thus PSIM avoids the drawback of conventional analyses, namely, the adoption of unrealistic assumptions about the activity distribution that inevitably leads to the declaration of pessimistic (and in some cases optimistic) activity estimates and uncertainties. PSIM applies an optimisation technique based upon ‘particle swarming’ methods to determine a set of candidate solutions within a ‘search space’ defined by the interior volume of a waste item. The positions and activities of the swarm are used in conjunction with a mathematical model to simulate the measurement response for the current swarm location. The swarm is iteratively updated (with modified positions and activities) until a match with sufficient quality is obtained between the simulated and actual measurement data. This process is repeated to build up a distribution of candidate solutions, which is subsequently analysed to calculate a measurement result and uncertainty along with a visual image of the activity distribution. The application of ‘swarming’ computational methods to non‑destructive assay (NDA) measurements is considered novel and this paper is intended to introduce the PSIM concept and provide

Predator confusion is sufficient to evolve swarming behaviour

OpenAIRE

Olson, Randal S.; Hintze, Arend; Dyer, Fred C.; Knoester, David B.; Adami, Christoph

2013-01-01

Swarming behaviours in animals have been extensively studied owing to their implications for the evolution of cooperation, social cognition and predator–prey dynamics. An important goal of these studies is discerning which evolutionary pressures favour the formation of swarms. One hypothesis is that swarms arise because the presence of multiple moving prey in swarms causes confusion for attacking predators, but it remains unclear how important this selective force is. Using an evolutionary mo...

Predator confusion is sufficient to evolve swarming behavior

OpenAIRE

Olson, Randal S.; Hintze, Arend; Dyer, Fred C.; Knoester, David B.; Adami, Christoph

2012-01-01

Swarming behaviors in animals have been extensively studied due to their implications for the evolution of cooperation, social cognition, and predator-prey dynamics. An important goal of these studies is discerning which evolutionary pressures favor the formation of swarms. One hypothesis is that swarms arise because the presence of multiple moving prey in swarms causes confusion for attacking predators, but it remains unclear how important this selective force is. Using an evolutionary model...

A fully automated non-external marker 4D-CT sorting algorithm using a serial cine scanning protocol.

Science.gov (United States)

Carnes, Greg; Gaede, Stewart; Yu, Edward; Van Dyk, Jake; Battista, Jerry; Lee, Ting-Yim

2009-04-07

Current 4D-CT methods require external marker data to retrospectively sort image data and generate CT volumes. In this work we develop an automated 4D-CT sorting algorithm that performs without the aid of data collected from an external respiratory surrogate. The sorting algorithm requires an overlapping cine scan protocol. The overlapping protocol provides a spatial link between couch positions. Beginning with a starting scan position, images from the adjacent scan position (which spatial match the starting scan position) are selected by maximizing the normalized cross correlation (NCC) of the images at the overlapping slice position. The process was continued by 'daisy chaining' all couch positions using the selected images until an entire 3D volume was produced. The algorithm produced 16 phase volumes to complete a 4D-CT dataset. Additional 4D-CT datasets were also produced using external marker amplitude and phase angle sorting methods. The image quality of the volumes produced by the different methods was quantified by calculating the mean difference of the sorted overlapping slices from adjacent couch positions. The NCC sorted images showed a significant decrease in the mean difference (p < 0.01) for the five patients.

A minimal model of predator-swarm interactions.

Science.gov (United States)

Chen, Yuxin; Kolokolnikov, Theodore

2014-05-06

We propose a minimal model of predator-swarm interactions which captures many of the essential dynamics observed in nature. Different outcomes are observed depending on the predator strength. For a 'weak' predator, the swarm is able to escape the predator completely. As the strength is increased, the predator is able to catch up with the swarm as a whole, but the individual prey is able to escape by 'confusing' the predator: the prey forms a ring with the predator at the centre. For higher predator strength, complex chasing dynamics are observed which can become chaotic. For even higher strength, the predator is able to successfully capture the prey. Our model is simple enough to be amenable to a full mathematical analysis, which is used to predict the shape of the swarm as well as the resulting predator-prey dynamics as a function of model parameters. We show that, as the predator strength is increased, there is a transition (owing to a Hopf bifurcation) from confusion state to chasing dynamics, and we compute the threshold analytically. Our analysis indicates that the swarming behaviour is not helpful in avoiding the predator, suggesting that there are other reasons why the species may swarm. The complex shape of the swarm in our model during the chasing dynamics is similar to the shape of a flock of sheep avoiding a shepherd.

Identifying Interacting Genetic Variations by Fish-Swarm Logic Regression

Science.gov (United States)

Yang, Aiyuan; Yan, Chunxia; Zhu, Feng; Zhao, Zhongmeng; Cao, Zhi

2013-01-01

Understanding associations between genotypes and complex traits is a fundamental problem in human genetics. A major open problem in mapping phenotypes is that of identifying a set of interacting genetic variants, which might contribute to complex traits. Logic regression (LR) is a powerful multivariant association tool. Several LR-based approaches have been successfully applied to different datasets. However, these approaches are not adequate with regard to accuracy and efficiency. In this paper, we propose a new LR-based approach, called fish-swarm logic regression (FSLR), which improves the logic regression process by incorporating swarm optimization. In our approach, a school of fish agents are conducted in parallel. Each fish agent holds a regression model, while the school searches for better models through various preset behaviors. A swarm algorithm improves the accuracy and the efficiency by speeding up the convergence and preventing it from dropping into local optimums. We apply our approach on a real screening dataset and a series of simulation scenarios. Compared to three existing LR-based approaches, our approach outperforms them by having lower type I and type II error rates, being able to identify more preset causal sites, and performing at faster speeds. PMID:23984382

Identifying Interacting Genetic Variations by Fish-Swarm Logic Regression

Directory of Open Access Journals (Sweden)

Xuanping Zhang

2013-01-01

Full Text Available Understanding associations between genotypes and complex traits is a fundamental problem in human genetics. A major open problem in mapping phenotypes is that of identifying a set of interacting genetic variants, which might contribute to complex traits. Logic regression (LR is a powerful multivariant association tool. Several LR-based approaches have been successfully applied to different datasets. However, these approaches are not adequate with regard to accuracy and efficiency. In this paper, we propose a new LR-based approach, called fish-swarm logic regression (FSLR, which improves the logic regression process by incorporating swarm optimization. In our approach, a school of fish agents are conducted in parallel. Each fish agent holds a regression model, while the school searches for better models through various preset behaviors. A swarm algorithm improves the accuracy and the efficiency by speeding up the convergence and preventing it from dropping into local optimums. We apply our approach on a real screening dataset and a series of simulation scenarios. Compared to three existing LR-based approaches, our approach outperforms them by having lower type I and type II error rates, being able to identify more preset causal sites, and performing at faster speeds.

Particle swarm optimization based feature enhancement and feature selection for improved emotion recognition in speech and glottal signals.

Science.gov (United States)

Muthusamy, Hariharan; Polat, Kemal; Yaacob, Sazali

2015-01-01

In the recent years, many research works have been published using speech related features for speech emotion recognition, however, recent studies show that there is a strong correlation between emotional states and glottal features. In this work, Mel-frequency cepstralcoefficients (MFCCs), linear predictive cepstral coefficients (LPCCs), perceptual linear predictive (PLP) features, gammatone filter outputs, timbral texture features, stationary wavelet transform based timbral texture features and relative wavelet packet energy and entropy features were extracted from the emotional speech (ES) signals and its glottal waveforms(GW). Particle swarm optimization based clustering (PSOC) and wrapper based particle swarm optimization (WPSO) were proposed to enhance the discerning ability of the features and to select the discriminating features respectively. Three different emotional speech databases were utilized to gauge the proposed method. Extreme learning machine (ELM) was employed to classify the different types of emotions. Different experiments were conducted and the results show that the proposed method significantly improves the speech emotion recognition performance compared to previous works published in the literature.

Investigation of Evolutionary Pheromone Communication Based on External Measurement and Emergence of Swarm Intelligence

OpenAIRE

川村, 秀憲; 山本, 雅人; 大内, 東

2001-01-01

In this paper, we focus on the emergence phenomenon related with artificial pheromone communication and swarm intelligence among many agents in Ants War environment, in which two colonies of artificial ant agents compete for the limited number of food items in order to survive in evolutionary process. The purpose of this research is to clarify the emerging process of communication and the relationship between communication and swarm intelligence. For investigation of communication, we introdu...

Application of radix sorting in high energy physics experiment

International Nuclear Information System (INIS)

Chen Xuan; Gu Minhao; Zhu Kejun

2012-01-01

In the high energy physics experiments, there are always requirements to sort the large scale of experiment data. To meet the demand, this paper introduces one radix sorting algorithms, whose sub-sort is counting sorting and time complex is O (n), based on the characteristic of high energy physics experiment data that is marked by time stamp. This paper gives the description, analysis, implementation and experimental result of the sorting algorithms. (authors)

Size-based sorting of micro-particles using microbubble streaming

Science.gov (United States)

Wang, Cheng; Jalikop, Shreyas; Hilgenfeldt, Sascha

2009-11-01

Oscillating microbubbles driven by ultrasound have shown great potential in microfluidic applications, such as transporting particles and promoting mixing [1-3]. The oscillations generate secondary steady streaming that can also trap particles. We use the streaming to develop a method of sorting particles of different sizes in an initially well-mixed solution. The solution is fed into a channel consisting of bubbles placed periodically along a side wall. When the bubbles are excited by an ultrasound piezo-electric transducer to produce steady streaming, the flow field is altered by the presence of the particles. This effect is dependent on particle size and results in size-based sorting of the particles. The effectiveness of the separation depends on the dimensions of the bubbles and particles as well as on the ultrasound frequency. Our experimental studies are aimed at a better understanding of the design and control of effective microfluidic separating devices. Ref: [1] P. Marmottant and S. Hilgenfeldt, Nature 423, 153 (2003). [2] P. Marmottant and S. Hilgenfeldt, Proc. Natl. Acad. Science USA, 101, 9523 (2004). [3] P. Marmottant, J.-P. Raven, H. Gardeniers, J. G. Bomer, and S. Hilgenfeldt, J. Fluid Mech., vol.568, 109 (2006).

Multi-Working Modes Product-Color Planning Based on Evolutionary Algorithms and Swarm Intelligence

Directory of Open Access Journals (Sweden)

Man Ding

2010-01-01

Full Text Available In order to assist designer in color planning during product development, a novel synthesized evaluation method is presented to evaluate color-combination schemes of multi-working modes products (MMPs. The proposed evaluation method considers color-combination images in different working modes as evaluating attributes, to which the corresponding weights are assigned for synthesized evaluation. Then a mathematical model is developed to search for optimal color-combination schemes of MMP based on the proposed evaluation method and two powerful search techniques known as Evolution Algorithms (EAs and Swarm Intelligence (SI. In the experiments, we present a comparative study for two EAs, namely, Genetic Algorithm (GA and Difference Evolution (DE, and one SI algorithm, namely, Particle Swarm Optimization (PSO, on searching for color-combination schemes of MMP problem. All of the algorithms are evaluated against a test scenario, namely, an Arm-type aerial work platform, which has two working modes. The results show that the DE obtains the superior solution than the other two algorithms for color-combination scheme searching problem in terms of optimization accuracy and computation robustness. Simulation results demonstrate that the proposed method is feasible and efficient.

A measurement-based fault detection approach applied to monitor robots swarm

KAUST Repository

Khaldi, Belkacem; Harrou, Fouzi; Sun, Ying; Cherif, Foudil

2017-01-01

present an innovative data-driven fault detection method for monitoring robots swarm. The method combines the flexibility of principal component analysis (PCA) models and the greater sensitivity of the exponentially-weighted moving average control chart

CellSort: a support vector machine tool for optimizing fluorescence-activated cell sorting and reducing experimental effort.

Science.gov (United States)

Yu, Jessica S; Pertusi, Dante A; Adeniran, Adebola V; Tyo, Keith E J

2017-03-15

High throughput screening by fluorescence activated cell sorting (FACS) is a common task in protein engineering and directed evolution. It can also be a rate-limiting step if high false positive or negative rates necessitate multiple rounds of enrichment. Current FACS software requires the user to define sorting gates by intuition and is practically limited to two dimensions. In cases when multiple rounds of enrichment are required, the software cannot forecast the enrichment effort required. We have developed CellSort, a support vector machine (SVM) algorithm that identifies optimal sorting gates based on machine learning using positive and negative control populations. CellSort can take advantage of more than two dimensions to enhance the ability to distinguish between populations. We also present a Bayesian approach to predict the number of sorting rounds required to enrich a population from a given library size. This Bayesian approach allowed us to determine strategies for biasing the sorting gates in order to reduce the required number of enrichment rounds. This algorithm should be generally useful for improve sorting outcomes and reducing effort when using FACS. Source code available at http://tyolab.northwestern.edu/tools/ . k-tyo@northwestern.edu. Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

Controlling factors on earthquake swarms associated with magmatic intrusions; constraints from Iceland

Science.gov (United States)

Pedersen, R.; Sigmundsson, F.; Einarsson, P.; Brandsdottir, B.; Arnadottir, T.

2005-12-01

Intrusion of magma into the Earth's crust is frequently associated with seismic activity, often occurring as distinct earthquake swarms. Understanding the nature of these swarms is important for evaluating crisis situations in volcanic areas. However, there often seem to be little correlation between the amount of seismic energy release, the spatial extent of the volume of rock affected by the stress perturbations, and the volume of magma on the move, which complicates the immediate risk evaluation. A number of factors may influence the evolution of a magmatically induced seismic swarm and the resulting seismic energy release. A number of factors need to be evaluated in each individual case. These are, in random order: the crustal thickness, presence/absence of a crustal magma chamber, geothermal gradient, magmatic flow rate/stressing rate, intrusion volume, depth of intrusion, tectonic setting of the intruded area, regional stresses and tectonic history. Based on three case studies, where seismic swarm activities have been confirmed through deformation measurements to be related to magmatic movements, we attempt to evaluate the relative importance of the assumed controlling factors. All case examples are located within Iceland, but in different tectonic settings. 1. The Hengill triple junction, situated where two extensional plate boundaries join a transform zone. The area experienced a period of unusually persistent earthquake activity from 1994 to 1999, contemporaneously with ground uplift at a rate of 1-2 cm/yr. The uplift was modeled as a response to magma injection at about 7 km depth. 2. The Eyjafjallajokull volcano, situated in a volcanic flank zone where extensional fractures are only poorly developed. Two minor seismic swarms, in 1994 and 1999; were associated with a cumulative surface uplift of more than 35 cm. The two uplift events were modeled as sill intrusions at depths of 4.5 to 6.5 km. 3. The Krafla rift segment, forming part of an extensional

An Unsupervised Online Spike-Sorting Framework.

Science.gov (United States)

Knieling, Simeon; Sridharan, Kousik S; Belardinelli, Paolo; Naros, Georgios; Weiss, Daniel; Mormann, Florian; Gharabaghi, Alireza

2016-08-01

Extracellular neuronal microelectrode recordings can include action potentials from multiple neurons. To separate spikes from different neurons, they can be sorted according to their shape, a procedure referred to as spike-sorting. Several algorithms have been reported to solve this task. However, when clustering outcomes are unsatisfactory, most of them are difficult to adjust to achieve the desired results. We present an online spike-sorting framework that uses feature normalization and weighting to maximize the distinctiveness between different spike shapes. Furthermore, multiple criteria are applied to either facilitate or prevent cluster fusion, thereby enabling experimenters to fine-tune the sorting process. We compare our method to established unsupervised offline (Wave_Clus (WC)) and online (OSort (OS)) algorithms by examining their performance in sorting various test datasets using two different scoring systems (AMI and the Adamos metric). Furthermore, we evaluate sorting capabilities on intra-operative recordings using established quality metrics. Compared to WC and OS, our algorithm achieved comparable or higher scores on average and produced more convincing sorting results for intra-operative datasets. Thus, the presented framework is suitable for both online and offline analysis and could substantially improve the quality of microelectrode-based data evaluation for research and clinical application.

A Diversity-Guided Particle Swarm Optimizer - the ARPSO

DEFF Research Database (Denmark)

Vesterstrøm, Jacob Svaneborg; Riget, Jacques

2002-01-01

The particle swarm optimization (PSO) algorithm is a new population based search strat- egy, which has exhibited good performance on well-known numerical test problems. How- ever, on strongly multi-modal test problems the PSO tends to suffer from premature convergence. This is due to a decrease...... that the ARPSO prevents premature convergence to a high degree, but still keeps a rapid convergence like the basic PSO. Thus, it clearly outperforms the basic PSO as well as the implemented GA in multi-modal optimization. Keywords Particle Swarm Optimization, Diversity-Guided Search 1 Introduction The PSO model...

Two-sorted Point-Interval Temporal Logics

DEFF Research Database (Denmark)

Balbiani, Philippe; Goranko, Valentin; Sciavicco, Guido

2011-01-01

There are two natural and well-studied approaches to temporal ontology and reasoning: point-based and interval-based. Usually, interval-based temporal reasoning deals with points as particular, duration-less intervals. Here we develop explicitly two-sorted point-interval temporal logical framework...... whereby time instants (points) and time periods (intervals) are considered on a par, and the perspective can shift between them within the formal discourse. We focus on fragments involving only modal operators that correspond to the inter-sort relations between points and intervals. We analyze...

Modeling and Flocking Consensus Analysis for Large-Scale UAV Swarms

Directory of Open Access Journals (Sweden)

Li Bing

2013-01-01

Full Text Available Recently, distributed coordination control of the unmanned aerial vehicle (UAV swarms has been a particularly active topic in intelligent system field. In this paper, through understanding the emergent mechanism of the complex system, further research on the flocking and the dynamic characteristic of UAV swarms will be given. Firstly, this paper analyzes the current researches and existent problems of UAV swarms. Afterwards, by the theory of stochastic process and supplemented variables, a differential-integral model is established, converting the system model into Volterra integral equation. The existence and uniqueness of the solution of the system are discussed. Then the flocking control law is given based on artificial potential with system consensus. At last, we analyze the stability of the proposed flocking control algorithm based on the Lyapunov approach and prove that the system in a limited time can converge to the consensus direction of the velocity. Simulation results are provided to verify the conclusion.

Hybrid chaotic ant swarm optimization

International Nuclear Information System (INIS)

Li Yuying; Wen Qiaoyan; Li Lixiang; Peng Haipeng

2009-01-01

Chaotic ant swarm optimization (CASO) is a powerful chaos search algorithm that is used to find the global optimum solution in search space. However, the CASO algorithm has some disadvantages, such as lower solution precision and longer computational time, when solving complex optimization problems. To resolve these problems, an improved CASO, called hybrid chaotic swarm optimization (HCASO), is proposed in this paper. The new algorithm introduces preselection operator and discrete recombination operator into the CASO; meanwhile it replaces the best position found by own and its neighbors' ants with the best position found by preselection operator and discrete recombination operator in evolution equation. Through testing five benchmark functions with large dimensionality, the experimental results show the new method enhances the solution accuracy and stability greatly, as well as reduces the computational time and computer memory significantly when compared to the CASO. In addition, we observe the results can become better with swarm size increasing from the sensitivity study to swarm size. And we gain some relations between problem dimensions and swam size according to scalability study.

A modified multi-objective particle swarm optimization approach and its application to the design of a deepwater composite riser

Science.gov (United States)

Zheng, Y.; Chen, J.

2017-09-01

A modified multi-objective particle swarm optimization method is proposed for obtaining Pareto-optimal solutions effectively. Different from traditional multi-objective particle swarm optimization methods, Kriging meta-models and the trapezoid index are introduced and integrated with the traditional one. Kriging meta-models are built to match expensive or black-box functions. By applying Kriging meta-models, function evaluation numbers are decreased and the boundary Pareto-optimal solutions are identified rapidly. For bi-objective optimization problems, the trapezoid index is calculated as the sum of the trapezoid's area formed by the Pareto-optimal solutions and one objective axis. It can serve as a measure whether the Pareto-optimal solutions converge to the Pareto front. Illustrative examples indicate that to obtain Pareto-optimal solutions, the method proposed needs fewer function evaluations than the traditional multi-objective particle swarm optimization method and the non-dominated sorting genetic algorithm II method, and both the accuracy and the computational efficiency are improved. The proposed method is also applied to the design of a deepwater composite riser example in which the structural performances are calculated by numerical analysis. The design aim was to enhance the tension strength and minimize the cost. Under the buckling constraint, the optimal trade-off of tensile strength and material volume is obtained. The results demonstrated that the proposed method can effectively deal with multi-objective optimizations with black-box functions.

Seismotectonic significance of the 2008–2010 Walloon Brabant seismic swarm in the Brabant Massif, Belgium

Science.gov (United States)

Van Noten, Koen; Lecocq, Thomas; Shah, Anjana K.; Camelbeeck, Thierry

2015-01-01

Between 12 July 2008 and 18 January 2010 a seismic swarm occurred close to the town of Court-Saint-Etienne, 20 km SE of Brussels (Belgium). The Belgian network and a temporary seismic network covering the epicentral area established a seismic catalogue in which magnitude varies between ML -0.7 and ML 3.2. Based on waveform cross-correlation of co-located earthquakes, the spatial distribution of the hypocentre locations was improved considerably and shows a dense cluster displaying a 200 m-wide, 1.5-km long, NW-SE oriented fault structure at a depth range between 5 and 7 km, located in the Cambrian basement rocks of the Lower Palaeozoic Anglo-Brabant Massif. Waveform comparison of the largest events of the 2008–2010 swarm with an ML 4.0 event that occurred during swarm activity between 1953 and 1957 in the same region shows similar P- and S-wave arrivals at the Belgian Uccle seismic station. The geometry depicted by the hypocentral distribution is consistent with a nearly vertical, left-lateral strike-slip fault taking place in a current local WNW–ESE oriented local maximum horizontal stress field. To determine a relevant tectonic structure, a systematic matched filtering approach of aeromagnetic data, which can approximately locate isolated anomalies associated with hypocentral depths, has been applied. Matched filtering shows that the 2008–2010 seismic swarm occurred along a limited-sized fault which is situated in slaty, low-magnetic rocks of the Mousty Formation. The fault is bordered at both ends with obliquely oriented magnetic gradients. Whereas the NW end of the fault is structurally controlled, its SE end is controlled by a magnetic gradient representing an early-orogenic detachment fault separating the low-magnetic slaty Mousty Formation from the high-magnetic Tubize Formation. The seismic swarm is therefore interpreted as a sinistral reactivation of an inherited NW–SE oriented isolated fault in a weakened crust within the Cambrian core of

Short-Term Wind Power Forecasting Using the Enhanced Particle Swarm Optimization Based Hybrid Method

OpenAIRE

Wen-Yeau Chang

2013-01-01

High penetration of wind power in the electricity system provides many challenges to power system operators, mainly due to the unpredictability and variability of wind power generation. Although wind energy may not be dispatched, an accurate forecasting method of wind speed and power generation can help power system operators reduce the risk of an unreliable electricity supply. This paper proposes an enhanced particle swarm optimization (EPSO) based hybrid forecasting method for short-term wi...

Enhanced fuzzy-connective-based hierarchical aggregation network using particle swarm optimization

Science.gov (United States)

Wang, Fang-Fang; Su, Chao-Ton

2014-11-01

The fuzzy-connective-based aggregation network is similar to the human decision-making process. It is capable of aggregating and propagating degrees of satisfaction of a set of criteria in a hierarchical manner. Its interpreting ability and transparency make it especially desirable. To enhance its effectiveness and further applicability, a learning approach is successfully developed based on particle swarm optimization to determine the weights and parameters of the connectives in the network. By experimenting on eight datasets with different characteristics and conducting further statistical tests, it has been found to outperform the gradient- and genetic algorithm-based learning approaches proposed in the literature; furthermore, it is capable of generating more accurate estimates. The present approach retains the original benefits of fuzzy-connective-based aggregation networks and is widely applicable. The characteristics of the learning approaches are also discussed and summarized, providing better understanding of the similarities and differences among these three approaches.

Improving the Bin Packing Heuristic through Grammatical Evolution Based on Swarm Intelligence

Directory of Open Access Journals (Sweden)

Marco Aurelio Sotelo-Figueroa

2014-01-01

Full Text Available In recent years Grammatical Evolution (GE has been used as a representation of Genetic Programming (GP which has been applied to many optimization problems such as symbolic regression, classification, Boolean functions, constructed problems, and algorithmic problems. GE can use a diversity of searching strategies including Swarm Intelligence (SI. Particle Swarm Optimisation (PSO is an algorithm of SI that has two main problems: premature convergence and poor diversity. Particle Evolutionary Swarm Optimization (PESO is a recent and novel algorithm which is also part of SI. PESO uses two perturbations to avoid PSO’s problems. In this paper we propose using PESO and PSO in the frame of GE as strategies to generate heuristics that solve the Bin Packing Problem (BPP; it is possible however to apply this methodology to other kinds of problems using another Grammar designed for that problem. A comparison between PESO, PSO, and BPP’s heuristics is performed through the nonparametric Friedman test. The main contribution of this paper is proposing a Grammar to generate online and offline heuristics depending on the test instance trying to improve the heuristics generated by other grammars and humans; it also proposes a way to implement different algorithms as search strategies in GE like PESO to obtain better results than those obtained by PSO.

DNA-assisted swarm control in a biomolecular motor system.

Science.gov (United States)

Keya, Jakia Jannat; Suzuki, Ryuhei; Kabir, Arif Md Rashedul; Inoue, Daisuke; Asanuma, Hiroyuki; Sada, Kazuki; Hess, Henry; Kuzuya, Akinori; Kakugo, Akira

2018-01-31

In nature, swarming behavior has evolved repeatedly among motile organisms because it confers a variety of beneficial emergent properties. These include improved information gathering, protection from predators, and resource utilization. Some organisms, e.g., locusts, switch between solitary and swarm behavior in response to external stimuli. Aspects of swarming behavior have been demonstrated for motile supramolecular systems composed of biomolecular motors and cytoskeletal filaments, where cross-linkers induce large scale organization. The capabilities of such supramolecular systems may be further extended if the swarming behavior can be programmed and controlled. Here, we demonstrate that the swarming of DNA-functionalized microtubules (MTs) propelled by surface-adhered kinesin motors can be programmed and reversibly regulated by DNA signals. Emergent swarm behavior, such as translational and circular motion, can be selected by tuning the MT stiffness. Photoresponsive DNA containing azobenzene groups enables switching between solitary and swarm behavior in response to stimulation with visible or ultraviolet light.

Automatic spike sorting using tuning information.

Science.gov (United States)

Ventura, Valérie

2009-09-01

Current spike sorting methods focus on clustering neurons' characteristic spike waveforms. The resulting spike-sorted data are typically used to estimate how covariates of interest modulate the firing rates of neurons. However, when these covariates do modulate the firing rates, they provide information about spikes' identities, which thus far have been ignored for the purpose of spike sorting. This letter describes a novel approach to spike sorting, which incorporates both waveform information and tuning information obtained from the modulation of firing rates. Because it efficiently uses all the available information, this spike sorter yields lower spike misclassification rates than traditional automatic spike sorters. This theoretical result is verified empirically on several examples. The proposed method does not require additional assumptions; only its implementation is different. It essentially consists of performing spike sorting and tuning estimation simultaneously rather than sequentially, as is currently done. We used an expectation-maximization maximum likelihood algorithm to implement the new spike sorter. We present the general form of this algorithm and provide a detailed implementable version under the assumptions that neurons are independent and spike according to Poisson processes. Finally, we uncover a systematic flaw of spike sorting based on waveform information only.

Adaptive Remote-Sensing Techniques Implementing Swarms of Mobile Agents

Energy Technology Data Exchange (ETDEWEB)

Cameron, S.M.; Loubriel, G.M.; Rbinett, R.D. III; Stantz, K.M.; Trahan, M.W.; Wagner, J.S.

1999-04-01

This paper focuses on our recent work at Sandia National Laboratories toward engineering a physics-based swarm of mobile vehicles for distributed sensing applications. Our goal is to coordinate a sensor array that optimizes sensor coverage and multivariate signal analysis by implementing artificial intelligence and evolutionary computational techniques. These intelligent control systems integrate both globally operating decision-making systems and locally cooperative information-sharing modes using genetically-trained neural networks. Once trained, neural networks have the ability to enhance real-time operational responses to dynamical environments, such as obstacle avoidance, responding to prevailing wind patterns, and overcoming other natural obscurants or interferences (jammers). The swarm realizes a collective set of sensor neurons with simple properties incorporating interactions based on basic community rules (potential fields) and complex interconnecting functions based on various neural network architectures, Therefore, the swarm is capable of redundant heterogeneous measurements which furnishes an additional degree of robustness and fault tolerance not afforded by conventional systems, while accomplishing such cognitive tasks as generalization, error correction, pattern recognition, and sensor fission. The robotic platforms could be equipped with specialized sensor devices including transmit/receive dipole antennas, chemical or biological sniffers in combination with recognition analysis tools, communication modulators, and laser diodes. Our group has been studying the collective behavior of an autonomous, multi-agent system applied to emerging threat applications. To accomplish such tasks, research in the fields of robotics, sensor technology, and swarms are being conducted within an integrated program. Mission scenarios under consideration include ground penetrating impulse radar (GPR) for detection of under-ground structures, airborne systems, and plume

Particle Swarm Optimization and harmony search based clustering and routing in Wireless Sensor Networks

Directory of Open Access Journals (Sweden)

Veena Anand

2017-01-01

Full Text Available Wireless Sensor Networks (WSN has the disadvantage of limited and non-rechargeable energy resource in WSN creates a challenge and led to development of various clustering and routing algorithms. The paper proposes an approach for improving network lifetime by using Particle swarm optimization based clustering and Harmony Search based routing in WSN. So in this paper, global optimal cluster head are selected and Gateway nodes are introduced to decrease the energy consumption of the CH while sending aggregated data to the Base station (BS. Next, the harmony search algorithm based Local Search strategy finds best routing path for gateway nodes to the Base Station. Finally, the proposed algorithm is presented.

Monitoring a robot swarm using a data-driven fault detection approach

KAUST Repository

Khaldi, Belkacem

2017-06-30

Using swarm robotics system, with one or more faulty robots, to accomplish specific tasks may lead to degradation in performances complying with the target requirements. In such circumstances, robot swarms require continuous monitoring to detect abnormal events and to sustain normal operations. In this paper, an innovative exogenous fault detection method for monitoring robots swarm is presented. The method merges the flexibility of principal component analysis (PCA) models and the greater sensitivity of the exponentially-weighted moving average (EWMA) and cumulative sum (CUSUM) control charts to insidious changes. The method is tested and evaluated on a swarm of simulated foot-bot robots performing a circle formation task, via the viscoelastic control model. We illustrate through simulated data collected from the ARGoS simulator that a significant improvement in fault detection can be obtained by using the proposed method where compared to the conventional PCA-based methods (i.e., T2 and Q).

A Localization Method for Underwater Wireless Sensor Networks Based on Mobility Prediction and Particle Swarm Optimization Algorithms

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Ying Zhang

2016-02-01

Full Text Available Due to their special environment, Underwater Wireless Sensor Networks (UWSNs are usually deployed over a large sea area and the nodes are usually floating. This results in a lower beacon node distribution density, a longer time for localization, and more energy consumption. Currently most of the localization algorithms in this field do not pay enough consideration on the mobility of the nodes. In this paper, by analyzing the mobility patterns of water near the seashore, a localization method for UWSNs based on a Mobility Prediction and a Particle Swarm Optimization algorithm (MP-PSO is proposed. In this method, the range-based PSO algorithm is used to locate the beacon nodes, and their velocities can be calculated. The velocity of an unknown node is calculated by using the spatial correlation of underwater object’s mobility, and then their locations can be predicted. The range-based PSO algorithm may cause considerable energy consumption and its computation complexity is a little bit high, nevertheless the number of beacon nodes is relatively smaller, so the calculation for the large number of unknown nodes is succinct, and this method can obviously decrease the energy consumption and time cost of localizing these mobile nodes. The simulation results indicate that this method has higher localization accuracy and better localization coverage rate compared with some other widely used localization methods in this field.

A Localization Method for Underwater Wireless Sensor Networks Based on Mobility Prediction and Particle Swarm Optimization Algorithms.

Science.gov (United States)

Zhang, Ying; Liang, Jixing; Jiang, Shengming; Chen, Wei

2016-02-06

Due to their special environment, Underwater Wireless Sensor Networks (UWSNs) are usually deployed over a large sea area and the nodes are usually floating. This results in a lower beacon node distribution density, a longer time for localization, and more energy consumption. Currently most of the localization algorithms in this field do not pay enough consideration on the mobility of the nodes. In this paper, by analyzing the mobility patterns of water near the seashore, a localization method for UWSNs based on a Mobility Prediction and a Particle Swarm Optimization algorithm (MP-PSO) is proposed. In this method, the range-based PSO algorithm is used to locate the beacon nodes, and their velocities can be calculated. The velocity of an unknown node is calculated by using the spatial correlation of underwater object's mobility, and then their locations can be predicted. The range-based PSO algorithm may cause considerable energy consumption and its computation complexity is a little bit high, nevertheless the number of beacon nodes is relatively smaller, so the calculation for the large number of unknown nodes is succinct, and this method can obviously decrease the energy consumption and time cost of localizing these mobile nodes. The simulation results indicate that this method has higher localization accuracy and better localization coverage rate compared with some other widely used localization methods in this field.

Heterogeneous architecture to process swarm optimization algorithms

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Maria A. Dávila-Guzmán

2014-01-01

Full Text Available Since few years ago, the parallel processing has been embedded in personal computers by including co-processing units as the graphics processing units resulting in a heterogeneous platform. This paper presents the implementation of swarm algorithms on this platform to solve several functions from optimization problems, where they highlight their inherent parallel processing and distributed control features. In the swarm algorithms, each individual and dimension problem are parallelized by the granularity of the processing system which also offer low communication latency between individuals through the embedded processing. To evaluate the potential of swarm algorithms on graphics processing units we have implemented two of them: the particle swarm optimization algorithm and the bacterial foraging optimization algorithm. The algorithms’ performance is measured using the acceleration where they are contrasted between a typical sequential processing platform and the NVIDIA GeForce GTX480 heterogeneous platform; the results show that the particle swarm algorithm obtained up to 36.82x and the bacterial foraging swarm algorithm obtained up to 9.26x. Finally, the effect to increase the size of the population is evaluated where we show both the dispersion and the quality of the solutions are decreased despite of high acceleration performance since the initial distribution of the individuals can converge to local optimal solution.

ESA Swarm Mission - Level 1b Products

Science.gov (United States)

Tøffner-Clausen, Lars; Floberghagen, Rune; Mecozzi, Riccardo; Menard, Yvon

2014-05-01

Swarm, a three-satellite constellation to study the dynamics of the Earth's magnetic field and its interactions with the Earth system, has been launched in November 2013. The objective of the Swarm mission is to provide the best ever survey of the geomagnetic field and its temporal evolution, which will bring new insights into the Earth system by improving our understanding of the Earth's interior and environment. The Level 1b Products of the Swarm mission contain time-series of the quality screened, calibrated, corrected, and fully geo-localized measurements of the magnetic field intensity, the magnetic field vector (provided in both instrument and Earth-fixed frames), the plasma density, temperature, and velocity. Additionally, quality screened and pre-calibrated measurements of the nongravitational accelerations are provided. Geo-localization is performed by 24- channel GPS receivers and by means of unique, three head Advanced Stellar Compasses for high-precision satellite attitude information. The Swarm Level 1b data will be provided in daily products separately for each of the three Swarm spacecrafts. This poster will present detailed lists of the contents of the Swarm Level 1b Products and brief descriptions of the processing algorithms used in the generation of these data.

Power System Stabilizer Design Based on a Particle Swarm Optimization Multiobjective Function Implemented Under Graphical Interface

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Ghouraf Djamel Eddine

2016-05-01

Full Text Available Power system stability considered a necessary condition for normal functioning of an electrical network. The role of regulation and control systems is to ensure that stability by determining the essential elements that influence it. This paper proposes a Particle Swarm Optimization (PSO based multiobjective function to tuning optimal parameters of Power System Stabilizer (PSS; this later is used as auxiliary to generator excitation system in order to damp electro mechanicals oscillations of the rotor and consequently improve Power system stability. The computer simulation results obtained by developed graphical user interface (GUI have proved the efficiency of PSS optimized by a Particle Swarm Optimization, in comparison with a conventional PSS, showing stable   system   responses   almost   insensitive   to   large parameter variations.Our present study was performed using a GUI realized under MATLAB in our work.

What is a Sorting Function?

DEFF Research Database (Denmark)

Henglein, Fritz

2009-01-01

What is a sorting function—not a sorting function for a given ordering relation, but a sorting function with nothing given? Formulating four basic properties of sorting algorithms as defining requirements, we arrive at intrinsic notions of sorting and stable sorting: A function is a sorting...... are derivable without compromising data abstraction. Finally we point out that stable sorting functions as default representations of ordering relations have the advantage of permitting linear-time sorting algorithms; inequality tests forfeit this possibility....... function if and only it is an intrinsically parametric permutation function. It is a stable sorting function if and only if it is an intrinsically stable permutation function. We show that ordering relations can be represented isomorphically as inequality tests, comparators and stable sorting functions...

DOA Estimation of Low Altitude Target Based on Adaptive Step Glowworm Swarm Optimization-multiple Signal Classification Algorithm

Directory of Open Access Journals (Sweden)

Zhou Hao

2015-06-01

Full Text Available The traditional MUltiple SIgnal Classification (MUSIC algorithm requires significant computational effort and can not be employed for the Direction Of Arrival (DOA estimation of targets in a low-altitude multipath environment. As such, a novel MUSIC approach is proposed on the basis of the algorithm of Adaptive Step Glowworm Swarm Optimization (ASGSO. The virtual spatial smoothing of the matrix formed by each snapshot is used to realize the decorrelation of the multipath signal and the establishment of a fullorder correlation matrix. ASGSO optimizes the function and estimates the elevation of the target. The simulation results suggest that the proposed method can overcome the low altitude multipath effect and estimate the DOA of target readily and precisely without radar effective aperture loss.

Study on Impact Acoustic-Visual Sensor-Based Sorting of ELV Plastic Materials.

Science.gov (United States)

Huang, Jiu; Tian, Chuyuan; Ren, Jingwei; Bian, Zhengfu

2017-06-08

This paper concentrates on a study of a novel multi-sensor aided method by using acoustic and visual sensors for detection, recognition and separation of End-of Life vehicles' (ELVs) plastic materials, in order to optimize the recycling rate of automotive shredder residues (ASRs). Sensor-based sorting technologies have been utilized for material recycling for the last two decades. One of the problems still remaining results from black and dark dyed plastics which are very difficult to recognize using visual sensors. In this paper a new multi-sensor technology for black plastic recognition and sorting by using impact resonant acoustic emissions (AEs) and laser triangulation scanning was introduced. A pilot sorting system which consists of a 3-dimensional visual sensor and an acoustic sensor was also established; two kinds commonly used vehicle plastics, polypropylene (PP) and acrylonitrile-butadiene-styrene (ABS) and two kinds of modified vehicle plastics, polypropylene/ethylene-propylene-diene-monomer (PP-EPDM) and acrylonitrile-butadiene-styrene/polycarbonate (ABS-PC) were tested. In this study the geometrical features of tested plastic scraps were measured by the visual sensor, and their corresponding impact acoustic emission (AE) signals were acquired by the acoustic sensor. The signal processing and feature extraction of visual data as well as acoustic signals were realized by virtual instruments. Impact acoustic features were recognized by using FFT based power spectral density analysis. The results shows that the characteristics of the tested PP and ABS plastics were totally different, but similar to their respective modified materials. The probability of scrap material recognition rate, i.e., the theoretical sorting efficiency between PP and PP-EPDM, could reach about 50%, and between ABS and ABS-PC it could reach about 75% with diameters ranging from 14 mm to 23 mm, and with exclusion of abnormal impacts, the actual separation rates were 39.2% for PP, 41

Predator confusion is sufficient to evolve swarming behaviour.

Science.gov (United States)

Olson, Randal S; Hintze, Arend; Dyer, Fred C; Knoester, David B; Adami, Christoph

2013-08-06

Swarming behaviours in animals have been extensively studied owing to their implications for the evolution of cooperation, social cognition and predator-prey dynamics. An important goal of these studies is discerning which evolutionary pressures favour the formation of swarms. One hypothesis is that swarms arise because the presence of multiple moving prey in swarms causes confusion for attacking predators, but it remains unclear how important this selective force is. Using an evolutionary model of a predator-prey system, we show that predator confusion provides a sufficient selection pressure to evolve swarming behaviour in prey. Furthermore, we demonstrate that the evolutionary effect of predator confusion on prey could in turn exert pressure on the structure of the predator's visual field, favouring the frontally oriented, high-resolution visual systems commonly observed in predators that feed on swarming animals. Finally, we provide evidence that when prey evolve swarming in response to predator confusion, there is a change in the shape of the functional response curve describing the predator's consumption rate as prey density increases. Thus, we show that a relatively simple perceptual constraint--predator confusion--could have pervasive evolutionary effects on prey behaviour, predator sensory mechanisms and the ecological interactions between predators and prey.

A Novel Chaotic Particle Swarm Optimization Algorithm for Parking Space Guidance

Directory of Open Access Journals (Sweden)

Na Dong

2016-01-01

Full Text Available An evolutionary approach of parking space guidance based upon a novel Chaotic Particle Swarm Optimization (CPSO algorithm is proposed. In the newly proposed CPSO algorithm, the chaotic dynamics is combined into the position updating rules of Particle Swarm Optimization to improve the diversity of solutions and to avoid being trapped in the local optima. This novel approach, that combines the strengths of Particle Swarm Optimization and chaotic dynamics, is then applied into the route optimization (RO problem of parking lots, which is an important issue in the management systems of large-scale parking lots. It is used to find out the optimized paths between any source and destination nodes in the route network. Route optimization problems based on real parking lots are introduced for analyzing and the effectiveness and practicability of this novel optimization algorithm for parking space guidance have been verified through the application results.

UCSD SORT Test (U-SORT): Examination of a newly developed organizational skills assessment tool for severely mentally ill adults.

Science.gov (United States)

Tiznado, Denisse; Mausbach, Brent T; Cardenas, Veronica; Jeste, Dilip V; Patterson, Thomas L

2010-12-01

The present investigation examined the validity of a new cognitive test intended to assess organizational skills. Participants were 180 middle-aged or older participants with a Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition diagnosis of schizophrenia or schizoaffective disorder. Participants' organizational skills were measured using our newly developed University of California, San Diego Sorting Test (U-SORT), a performance-based test of organizational ability in which subjects sort objects (e.g., battery, pens) from a "junk drawer" into "keep" versus "trash" piles. Significant correlations between U-SORT scores and theoretically similar constructs (i.e. functional capacity, cognitive functioning, and clinical symptoms) were acceptable (mean r = 0.34), and weak correlations were found between U-SORT scores and theoretically dissimilar constructs (e.g., health symptoms, social support, gender; mean r = 0.06 ). The correlation between assessment scores provides preliminary support for the U-SORT test as a brief, easily transportable, reliable, and valid measure of functioning for this population.

Web page sorting algorithm based on query keyword distance relation

Science.gov (United States)

Yang, Han; Cui, Hong Gang; Tang, Hao

2017-08-01

In order to optimize the problem of page sorting, according to the search keywords in the web page in the relationship between the characteristics of the proposed query keywords clustering ideas. And it is converted into the degree of aggregation of the search keywords in the web page. Based on the PageRank algorithm, the clustering degree factor of the query keyword is added to make it possible to participate in the quantitative calculation. This paper proposes an improved algorithm for PageRank based on the distance relation between search keywords. The experimental results show the feasibility and effectiveness of the method.

Precise Localization and Formation Control of Swarm Robots via Wireless Sensor Networks

Directory of Open Access Journals (Sweden)

Han Wu

2014-01-01

Full Text Available Precise localization and formation control are one of the key technologies to achieve coordination and control of swarm robots, which is also currently a bottleneck for practical applications of swarm robotic systems. Aiming at overcoming the limited individual perception and the difficulty of achieving precise localization and formation, a localization approach combining dead reckoning (DR with wireless sensor network- (WSN- based methods is proposed in this paper. Two kinds of WSN localization technologies are adopted in this paper, that is, ZigBee-based RSSI (received signal strength indication global localization and electronic tag floors for calibration of local positioning. First, the DR localization information is combined with the ZigBee-based RSSI position information using the Kalman filter method to achieve precise global localization and maintain the robot formation. Then the electronic tag floors provide the robots with their precise coordinates in some local areas and enable the robot swarm to calibrate its formation by reducing the accumulated position errors. Hence, the overall performance of localization and formation control of the swarm robotic system is improved. Both of the simulation results and the experimental results on a real schematic system are given to demonstrate the success of the proposed approach.

A new hybrid optimization method inspired from swarm intelligence: Fuzzy adaptive swallow swarm optimization algorithm (FASSO

Directory of Open Access Journals (Sweden)

Mehdi Neshat

2015-11-01

Full Text Available In this article, the objective was to present effective and optimal strategies aimed at improving the Swallow Swarm Optimization (SSO method. The SSO is one of the best optimization methods based on swarm intelligence which is inspired by the intelligent behaviors of swallows. It has been able to offer a relatively strong method for solving optimization problems. However, despite its many advantages, the SSO suffers from two shortcomings. Firstly, particles movement speed is not controlled satisfactorily during the search due to the lack of an inertia weight. Secondly, the variables of the acceleration coefficient are not able to strike a balance between the local and the global searches because they are not sufficiently flexible in complex environments. Therefore, the SSO algorithm does not provide adequate results when it searches in functions such as the Step or Quadric function. Hence, the fuzzy adaptive Swallow Swarm Optimization (FASSO method was introduced to deal with these problems. Meanwhile, results enjoy high accuracy which are obtained by using an adaptive inertia weight and through combining two fuzzy logic systems to accurately calculate the acceleration coefficients. High speed of convergence, avoidance from falling into local extremum, and high level of error tolerance are the advantages of proposed method. The FASSO was compared with eleven of the best PSO methods and SSO in 18 benchmark functions. Finally, significant results were obtained.

Online sorting of recovered wood waste by automated XRF-technology: part II. Sorting efficiencies.

Science.gov (United States)

Hasan, A Rasem; Solo-Gabriele, Helena; Townsend, Timothy

2011-04-01

Sorting of waste wood is an important process practiced at recycling facilities in order to detect and divert contaminants from recycled wood products. Contaminants of concern include arsenic, chromium and copper found in chemically preserved wood. The objective of this research was to evaluate the sorting efficiencies of both treated and untreated parts of the wood waste stream, and metal (As, Cr and Cu) mass recoveries by the use of automated X-ray fluorescence (XRF) systems. A full-scale system was used for experimentation. This unit consisted of an XRF-detection chamber mounted on the top of a conveyor and a pneumatic slide-way diverter which sorted wood into presumed treated and presumed untreated piles. A randomized block design was used to evaluate the operational conveyance parameters of the system, including wood feed rate and conveyor belt speed. Results indicated that online sorting efficiencies of waste wood by XRF technology were high based on number and weight of pieces (70-87% and 75-92% for treated wood and 66-97% and 68-96% for untreated wood, respectively). These sorting efficiencies achieved mass recovery for metals of 81-99% for As, 75-95% for Cu and 82-99% of Cr. The incorrect sorting of wood was attributed almost equally to deficiencies in the detection and conveyance/diversion systems. Even with its deficiencies, the system was capable of producing a recyclable portion that met residential soil quality levels established for Florida, for an infeed that contained 5% of treated wood. Copyright © 2010 Elsevier Ltd. All rights reserved.

Earthquake swarms near eastern Himalayan Syntaxis along Jiali Fault in Tibet: A seismotectonic appraisal

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Basab Mukhopadhyay

2015-09-01

Full Text Available The seismotectonic characteristics of ten repeated earthquake swarm sequence within a seismic cluster along Jiali Fault in eastern Himalayan Syntaxis (EHS have been analysed. The swarms are spatially disposed in and around Yigong Lake (a natural lake formed by blocking of Yigong River by landslide and are characterized by low magnitude, crustal events with low to moderate b values. Ms : mb discriminant functions though indicate anomalous nature of the earthquakes within swarm but are considered as natural events that occurred under condition of high apparent stress and stress gradients. Composite fault plane solutions of selected swarms indicate strike–slip sense of shear on fault planes; solution parameters show low plunging compression and tensional axes along NW–SE and NE–SW respectively with causative fault plane oriented ENE–WSW, dipping steeply towards south or north. The fault plane is in excellent agreement with the disposition and tectonic movement registered by right lateral Jiali Fault. The process of pore pressure perturbation and resultant ‘r–t plot’ with modelled diffusivity (D = 0.12 m2/s relates the diffusion of pore pressure to seismic sequence in a fractured poro-elastic fluid saturated medium at average crustal depth of 15–20 km. The low diffusivity depicts a highly fractured interconnected medium that is generated due to high stress activity near the eastern syntaxial bent of Himalaya. It is proposed that hydro fracturing with respect to periodic pore pressure variations is responsible for generation of swarms in the region. The fluid pressure generated due to shearing and infiltrations of surface water within dilated seismogenic fault (Jiali Fault are causative factors.

Genetic Particle Swarm Optimization–Based Feature Selection for Very-High-Resolution Remotely Sensed Imagery Object Change Detection

Science.gov (United States)

Chen, Qiang; Chen, Yunhao; Jiang, Weiguo

2016-01-01

In the field of multiple features Object-Based Change Detection (OBCD) for very-high-resolution remotely sensed images, image objects have abundant features and feature selection affects the precision and efficiency of OBCD. Through object-based image analysis, this paper proposes a Genetic Particle Swarm Optimization (GPSO)-based feature selection algorithm to solve the optimization problem of feature selection in multiple features OBCD. We select the Ratio of Mean to Variance (RMV) as the fitness function of GPSO, and apply the proposed algorithm to the object-based hybrid multivariate alternative detection model. Two experiment cases on Worldview-2/3 images confirm that GPSO can significantly improve the speed of convergence, and effectively avoid the problem of premature convergence, relative to other feature selection algorithms. According to the accuracy evaluation of OBCD, GPSO is superior at overall accuracy (84.17% and 83.59%) and Kappa coefficient (0.6771 and 0.6314) than other algorithms. Moreover, the sensitivity analysis results show that the proposed algorithm is not easily influenced by the initial parameters, but the number of features to be selected and the size of the particle swarm would affect the algorithm. The comparison experiment results reveal that RMV is more suitable than other functions as the fitness function of GPSO-based feature selection algorithm. PMID:27483285

Genetic Particle Swarm Optimization-Based Feature Selection for Very-High-Resolution Remotely Sensed Imagery Object Change Detection.

Science.gov (United States)

Chen, Qiang; Chen, Yunhao; Jiang, Weiguo

2016-07-30

In the field of multiple features Object-Based Change Detection (OBCD) for very-high-resolution remotely sensed images, image objects have abundant features and feature selection affects the precision and efficiency of OBCD. Through object-based image analysis, this paper proposes a Genetic Particle Swarm Optimization (GPSO)-based feature selection algorithm to solve the optimization problem of feature selection in multiple features OBCD. We select the Ratio of Mean to Variance (RMV) as the fitness function of GPSO, and apply the proposed algorithm to the object-based hybrid multivariate alternative detection model. Two experiment cases on Worldview-2/3 images confirm that GPSO can significantly improve the speed of convergence, and effectively avoid the problem of premature convergence, relative to other feature selection algorithms. According to the accuracy evaluation of OBCD, GPSO is superior at overall accuracy (84.17% and 83.59%) and Kappa coefficient (0.6771 and 0.6314) than other algorithms. Moreover, the sensitivity analysis results show that the proposed algorithm is not easily influenced by the initial parameters, but the number of features to be selected and the size of the particle swarm would affect the algorithm. The comparison experiment results reveal that RMV is more suitable than other functions as the fitness function of GPSO-based feature selection algorithm.

Foraging behavior analysis of swarm robotics system

Directory of Open Access Journals (Sweden)

Sakthivelmurugan E.

2018-01-01

Full Text Available Swarm robotics is a number of small robots that are synchronically works together to accomplish a given task. Swarm robotics faces many problems in performing a given task. The problems are pattern formation, aggregation, Chain formation, self-assembly, coordinated movement, hole avoidance, foraging and self-deployment. Foraging is most essential part in swarm robotics. Foraging is the task to discover the item and get back into the shell. The researchers conducted foraging experiments with random-movement of robots and they have end up with unique solutions. Most of the researchers have conducted experiments using the circular arena. The shell is placed at the centre of the arena and environment boundary is well known. In this study, an attempt is made to different strategic movements like straight line approach, parallel line approach, divider approach, expanding square approach, and parallel sweep approach. All these approaches are to be simulated by using player/stage open-source simulation software based on C and C++ programming language in Linux operating system. Finally statistical comparison will be done with task completion time of all these strategies using ANOVA to identify the significant searching strategy.

New tools for characterizing swarming systems: A comparison of minimal models

Science.gov (United States)

Huepe, Cristián; Aldana, Maximino

2008-05-01

We compare three simple models that reproduce qualitatively the emergent swarming behavior of bird flocks, fish schools, and other groups of self-propelled agents by using a new set of diagnosis tools related to the agents’ spatial distribution. Two of these correspond in fact to different implementations of the same model, which had been previously confused in the literature. All models appear to undergo a very similar order-to-disorder phase transition as the noise level is increased if we only compare the standard order parameter, which measures the degree of agent alignment. When considering our novel quantities, however, their properties are clearly distinguished, unveiling previously unreported qualitative characteristics that help determine which model best captures the main features of realistic swarms. Additionally, we analyze the agent clustering in space, finding that the distribution of cluster sizes is typically exponential at high noise, and approaches a power-law as the noise level is reduced. This trend is sometimes reversed at noise levels close to the phase transition, suggesting a non-trivial critical behavior that could be verified experimentally. Finally, we study a bi-stable regime that develops under certain conditions in large systems. By computing the probability distributions of our new quantities, we distinguish the properties of each of the coexisting metastable states. Our study suggests new experimental analyses that could be carried out to characterize real biological swarms.

Characterizing the Temporal and Spatial Distribution of Earthquake Swarms in the Puerto Rico - Virgin Island Block

Science.gov (United States)

Hernandez, F. J.; Lopez, A. M.; Vanacore, E. A.

2017-12-01

The presence of earthquake swarms and clusters in the north and northeast of the island of Puerto Rico in the northeastern Caribbean have been recorded by the Puerto Rico Seismic Network (PRSN) since it started operations in 1974. Although clusters in the Puerto Rico-Virgin Island (PRVI) block have been observed for over forty years, the nature of their enigmatic occurrence is still poorly understood. In this study, the entire seismic catalog of the PRSN, of approximately 31,000 seismic events, has been limited to a sub-set of 18,000 events located all along north of Puerto Rico in an effort to characterize and understand the underlying mechanism of these clusters. This research uses two de-clustering methods to identify cluster events in the PRVI block. The first method, known as Model Independent Stochastic Declustering (MISD), filters the catalog sub-set into cluster and background seismic events, while the second method uses a spatio-temporal algorithm applied to the catalog in order to link the separate seismic events into clusters. After using these two methods, identified clusters were classified into either earthquake swarms or seismic sequences. Once identified, each cluster was analyzed to identify correlations against other clusters in their geographic region. Results from this research seek to : (1) unravel their earthquake clustering behavior through the use of different statistical methods and (2) better understand the mechanism for these clustering of earthquakes. Preliminary results have allowed to identify and classify 128 clusters categorized in 11 distinctive regions based on their centers, and their spatio-temporal distribution have been used to determine intra- and interplate dynamics.

INHIBITION OF SWARMING BY UREA AND ITS DIAGNOSTIC ...

African Journals Online (AJOL)

The anti-swarming property of urea and effects on antibiotic susceptibility among 52 uropathogenic Proteus strains from Lagos, Nigeria were investigated. Urea caused a reduction in swarming and number of swarmed cells at 0.5% (n = 42, DOCZ = 15.5mm), 0.75% (n= 24, DOCZ = 10.7mm), 1% (n = 17, DOCZ = 3.4mm) and ...

Firefly as a novel swarm intelligence variable selection method in spectroscopy.

Science.gov (United States)

Goodarzi, Mohammad; dos Santos Coelho, Leandro

2014-12-10

A critical step in multivariate calibration is wavelength selection, which is used to build models with better prediction performance when applied to spectral data. Up to now, many feature selection techniques have been developed. Among all different types of feature selection techniques, those based on swarm intelligence optimization methodologies are more interesting since they are usually simulated based on animal and insect life behavior to, e.g., find the shortest path between a food source and their nests. This decision is made by a crowd, leading to a more robust model with less falling in local minima during the optimization cycle. This paper represents a novel feature selection approach to the selection of spectroscopic data, leading to more robust calibration models. The performance of the firefly algorithm, a swarm intelligence paradigm, was evaluated and compared with genetic algorithm and particle swarm optimization. All three techniques were coupled with partial least squares (PLS) and applied to three spectroscopic data sets. They demonstrate improved prediction results in comparison to when only a PLS model was built using all wavelengths. Results show that firefly algorithm as a novel swarm paradigm leads to a lower number of selected wavelengths while the prediction performance of built PLS stays the same. Copyright © 2014. Published by Elsevier B.V.

Magnethophoretic sorting of fluid catalytic cracking particles

NARCIS (Netherlands)

Solsona, Miguel; Nieuwelink, A. E.; Odijk, Mathieu; Meirer, Florian; Abelmann, Leon; Olthuis, Wouter; Weckhuysen, Bert M.; van den Berg, Albert; Lee, Abraham; DeVoe, Don

2017-01-01

We demonstrate an on-chip particle activity sorter, focused on iron concentration and based on magnetophoresis. This device was used for fast sorting of stepwise homogenously distributed [Fe]s. The preliminary results are very encouraging. We show that we can sort particles on magnetic moment, with

Collective motion with anticipation: flocking, spinning, and swarming.

Science.gov (United States)

Morin, Alexandre; Caussin, Jean-Baptiste; Eloy, Christophe; Bartolo, Denis

2015-01-01

We investigate the collective dynamics of self-propelled particles able to probe and anticipate the orientation of their neighbors. We show that a simple anticipation strategy hinders the emergence of homogeneous flocking patterns. Yet anticipation promotes two other forms of self-organization: collective spinning and swarming. In the spinning phase, all particles follow synchronous circular orbits, while in the swarming phase, the population condensates into a single compact swarm that cruises coherently without requiring any cohesive interactions. We quantitatively characterize and rationalize these phases of polar active matter and discuss potential applications to the design of swarming robots.

Genetic connectivity among swarming sites in the wide ranging and recently declining little brown bat (Myotis lucifugus)

Science.gov (United States)

Burns, Lynne E; Frasier, Timothy R; Broders, Hugh G

2014-01-01

Characterizing movement dynamics and spatial aspects of gene flow within a species permits inference on population structuring. As patterns of structuring are products of historical and current demographics and gene flow, assessment of structure through time can yield an understanding of evolutionary dynamics acting on populations that are necessary to inform management. Recent dramatic population declines in hibernating bats in eastern North America from white-nose syndrome have prompted the need for information on movement dynamics for multiple bat species. We characterized population genetic structure of the little brown bat, Myotis lucifugus, at swarming sites in southeastern Canada using 9 nuclear microsatellites and a 292-bp region of the mitochondrial genome. Analyses of FST, ΦST, and Bayesian clustering (STRUCTURE) found weak levels of genetic structure among swarming sites for the nuclear and mitochondrial genome (Global FST = 0.001, P bats that visit swarming sites whereby mainland areas of the region may be best considered as one large gene pool for management and conservation. PMID:25505539

The upper surface of an Escherichia coli swarm is stationary.

Science.gov (United States)

Zhang, Rongjing; Turner, Linda; Berg, Howard C

2010-01-05

When grown in a rich medium on agar, many bacteria elongate, produce more flagella, and swim in a thin film of fluid over the agar surface in swirling packs. Cells that spread in this way are said to swarm. The agar is a solid gel, with pores smaller than the bacteria, so the swarm/agar interface is fixed. Here we show, in experiments with Escherichia coli, that the swarm/air interface also is fixed. We deposited MgO smoke particles on the top surface of an E. coli swarm near its advancing edge, where cells move in a single layer, and then followed the motion of the particles by dark-field microscopy and the motion of the underlying cells by phase-contrast microscopy. Remarkably, the smoke particles remained fixed (diffusing only a few micrometers) while the swarming cells streamed past underneath. The diffusion coefficients of the smoke particles were smaller over the virgin agar ahead of the swarm than over the swarm itself. Changes between these two modes of behavior were evident within 10-20 microm of the swarm edge, indicating an increase in depth of the fluid in advance of the swarm. The only plausible way that the swarm/air interface can be fixed is that it is covered by a surfactant monolayer pinned at its edges. When a swarm is exposed to air, such a monolayer can markedly reduce water loss. When cells invade tissue, the ability to move rapidly between closely opposed fixed surfaces is a useful trait.

Genetic particle swarm parallel algorithm analysis of optimization arrangement on mistuned blades

Science.gov (United States)

Zhao, Tianyu; Yuan, Huiqun; Yang, Wenjun; Sun, Huagang

2017-12-01

This article introduces a method of mistuned parameter identification which consists of static frequency testing of blades, dichotomy and finite element analysis. A lumped parameter model of an engine bladed-disc system is then set up. A bladed arrangement optimization method, namely the genetic particle swarm optimization algorithm, is presented. It consists of a discrete particle swarm optimization and a genetic algorithm. From this, the local and global search ability is introduced. CUDA-based co-evolution particle swarm optimization, using a graphics processing unit, is presented and its performance is analysed. The results show that using optimization results can reduce the amplitude and localization of the forced vibration response of a bladed-disc system, while optimization based on the CUDA framework can improve the computing speed. This method could provide support for engineering applications in terms of effectiveness and efficiency.

Some recent studies of electron swarms in gases

International Nuclear Information System (INIS)

Tagashira, H.

1992-01-01

Some recent studies of electron swarms in gases under the action of an electric field are introduced. The studies include a new type of continuity equation for electrons having a form in which the partial derivative of the electron density with respect to position and to time are interchanged, a method to deduce the time-of-flight and arrival-time-spectrum swarm parameters based on a Fourier-transformed Boltzmann equation, an examination of the correspondence between experimental and theoretical electron drift velocities, and an automatic technique to deduce the electron-gas molecule collision cross section from electron drift velocity data. A method for the deduction of electron collision cross sections with gas molecules having vibrational excitation cross sections greater than the elastic momentum transfer cross section by using a gas mixture technique, an integral type of method for solution of the Boltzmann equation with salient numerical stability, a quantitative analysis of the effect of Penning ionisation, and the behaviour of electron swarms under radio frequency electric fields, are also briefly discussed. 28 refs., 3 figs

The infrared spectral transmittance of Aspergillus niger spore aggregated particle swarm

Science.gov (United States)

Zhao, Xinying; Hu, Yihua; Gu, Youlin; Li, Le

2015-10-01

Microorganism aggregated particle swarm, which is quite an important composition of complex media environment, can be developed as a new kind of infrared functional materials. Current researches mainly focus on the optical properties of single microorganism particle. As for the swarm, especially the microorganism aggregated particle swarm, a more accurate simulation model should be proposed to calculate its extinction effect. At the same time, certain parameters deserve to be discussed, which helps to better develop the microorganism aggregated particle swarm as a new kind of infrared functional materials. In this paper, take Aspergillus Niger spore as an example. On the one hand, a new calculation model is established. Firstly, the cluster-cluster aggregation (CCA) model is used to simulate the structure of Aspergillus Niger spore aggregated particle. Secondly, the single scattering extinction parameters for Aspergillus Niger spore aggregated particle are calculated by using the discrete dipole approximation (DDA) method. Thirdly, the transmittance of Aspergillus Niger spore aggregated particle swarm is simulated by using Monte Carlo method. On the other hand, based on the model proposed above, what influences can wavelength causes has been studied, including the spectral distribution of scattering intensity of Aspergillus Niger spore aggregated particle and the infrared spectral transmittance of the aggregated particle swarm within the range of 8-14μm incident infrared wavelengths. Numerical results indicate that the scattering intensity of Aspergillus Niger spore aggregated particle reduces with the increase of incident wavelengths at each scattering angle. Scattering energy mainly concentrates on the scattering angle between 0-40°, forward scattering has an obvious effect. In addition, the infrared transmittance of Aspergillus Niger spore aggregated particle swarm goes up with the increase of incident wavelengths. However, some turning points of the trend are

Bacterial Swarming: social behaviour or hydrodynamics?

Science.gov (United States)

Vermant, Jan

2010-03-01

Bacterial swarming of colonies is typically described as a social phenomenon between bacteria, whereby groups of bacteria collectively move atop solid surfaces. This multicellular behavior, during which the organized bacterial populations are embedded in an extracellular slime layer, is connected to important features such as biofilm formation and virulence. Despite the possible intricate quorum sensing mechanisms that regulate swarming, several physico-chemical phenomena may play a role in the dynamics of swarming and biofilm formation. Especially the striking fingering patterns formed by some swarmer colonies on relatively soft sub phases have attracted the attention as they could be the signatures of an instability. Recently, a parallel has been drawn between the swarming patterns and the spreading of viscous drops under the influence of a surfactant, which lead to similar patterns [1]. Starting from the observation that several of the molecules, essential in swarming systems, are strong biosurfactants, the possibility of flows driven by gradients in surface tension, has been proposed. This Marangoni flows are known to lead to these characteristic patterns. For Rhizobium etli not only the pattern formation, but also the experimentally observed spreading speed has been shown to be consistent with the one expected for Marangoni flows for the surface pressures, thickness, and viscosities that have been observed [2]. We will present an experimental study of swarming colonies of the bacteria Pseudomonas aeruginosa, the pattern formation, the surfactant gradients and height profiles in comparison with predictions of a thin film hydrodynamic model.[4pt] [1] Matar O.K. and Troian S., Phys. Fluids 11 : 3232 (1999)[0pt] [2] Daniels, R et al., PNAS, 103 (40): 14965-14970 (2006)

Measurement and interpretation of swarm parameters and their application in plasma modelling

International Nuclear Information System (INIS)

Petrovic, Z Lj; Dujko, S; Maric, D; Malovic, G; Nikitovic, Z; Sasic, O; Jovanovic, J; Stojanovic, V; Radmilovic-Radenovic, M

2009-01-01

In this review paper, we discuss the current status of the physics of charged particle swarms, mainly electrons, having plasma modelling in mind. The measurements of the swarm coefficients and the availability of the data are briefly discussed. We try to give a summary of the past ten years and cite the main reviews and databases, which store the majority of the earlier work. The need for reinitiating the swarm experiments and where and how those would be useful is pointed out. We also add some guidance on how to find information on ions and fast neutrals. Most space is devoted to interpretation of transport data, analysis of kinetic phenomena, and accuracy of calculation and proper use of transport data in plasma models. We have tried to show which aspects of kinetic theory developed for swarm physics and which segments of data would be important for further improvement of plasma models. Finally, several examples are given where actual models are mostly based on the physics of swarms and those include Townsend discharges, afterglows, breakdown and some atmospheric phenomena. Finally we stress that, while complex, some of the results from the kinetic theory of swarms and the related phenomenology must be used either to test the plasma models or even to bring in new physics or higher accuracy and reliability to the models. (review article)

Particle Swarm Based Approach of a Real-Time Discrete Neural Identifier for Linear Induction Motors

Directory of Open Access Journals (Sweden)

Alma Y. Alanis

2013-01-01

Full Text Available This paper focusses on a discrete-time neural identifier applied to a linear induction motor (LIM model, whose model is assumed to be unknown. This neural identifier is robust in presence of external and internal uncertainties. The proposed scheme is based on a discrete-time recurrent high-order neural network (RHONN trained with a novel algorithm based on extended Kalman filter (EKF and particle swarm optimization (PSO, using an online series-parallel con…figuration. Real-time results are included in order to illustrate the applicability of the proposed scheme.

Global Optimization Based on the Hybridization of Harmony Search and Particle Swarm Optimization Methods

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A. P. Karpenko

2014-01-01

Full Text Available We consider a class of stochastic search algorithms of global optimization which in various publications are called behavioural, intellectual, metaheuristic, inspired by the nature, swarm, multi-agent, population, etc. We use the last term.Experience in using the population algorithms to solve challenges of global optimization shows that application of one such algorithm may not always effective. Therefore now great attention is paid to hybridization of population algorithms of global optimization. Hybrid algorithms unite various algorithms or identical algorithms, but with various values of free parameters. Thus efficiency of one algorithm can compensate weakness of another.The purposes of the work are development of hybrid algorithm of global optimization based on known algorithms of harmony search (HS and swarm of particles (PSO, software implementation of algorithm, study of its efficiency using a number of known benchmark problems, and a problem of dimensional optimization of truss structure.We set a problem of global optimization, consider basic algorithms of HS and PSO, give a flow chart of the offered hybrid algorithm called PSO HS , present results of computing experiments with developed algorithm and software, formulate main results of work and prospects of its development.

Time Optimal Reachability Analysis Using Swarm Verification

DEFF Research Database (Denmark)

Zhang, Zhengkui; Nielsen, Brian; Larsen, Kim Guldstrand

2016-01-01

Time optimal reachability analysis employs model-checking to compute goal states that can be reached from an initial state with a minimal accumulated time duration. The model-checker may produce a corresponding diagnostic trace which can be interpreted as a feasible schedule for many scheduling...... and planning problems, response time optimization etc. We propose swarm verification to accelerate time optimal reachability using the real-time model-checker Uppaal. In swarm verification, a large number of model checker instances execute in parallel on a computer cluster using different, typically randomized...... search strategies. We develop four swarm algorithms and evaluate them with four models in terms scalability, and time- and memory consumption. Three of these cooperate by exchanging costs of intermediate solutions to prune the search using a branch-and-bound approach. Our results show that swarm...

Particle swarm optimisation classical and quantum perspectives

CERN Document Server

Sun, Jun; Wu, Xiao-Jun

2016-01-01

IntroductionOptimisation Problems and Optimisation MethodsRandom Search TechniquesMetaheuristic MethodsSwarm IntelligenceParticle Swarm OptimisationOverviewMotivationsPSO Algorithm: Basic Concepts and the ProcedureParadigm: How to Use PSO to Solve Optimisation ProblemsSome Harder Examples Some Variants of Particle Swarm Optimisation Why Does the PSO Algorithm Need to Be Improved? Inertia and Constriction-Acceleration Techniques for PSOLocal Best ModelProbabilistic AlgorithmsOther Variants of PSO Quantum-Behaved Particle Swarm Optimisation OverviewMotivation: From Classical Dynamics to Quantum MechanicsQuantum Model: Fundamentals of QPSOQPSO AlgorithmSome Essential ApplicationsSome Variants of QPSOSummary Advanced Topics Behaviour Analysis of Individual ParticlesConvergence Analysis of the AlgorithmTime Complexity and Rate of ConvergenceParameter Selection and PerformanceSummaryIndustrial Applications Inverse Problems for Partial Differential EquationsInverse Problems for Non-Linear Dynamical SystemsOptimal De...

SWARMs Ontology: A Common Information Model for the Cooperation of Underwater Robots

Science.gov (United States)

Li, Xin; Bilbao, Sonia; Martín-Wanton, Tamara; Bastos, Joaquim; Rodriguez, Jonathan

2017-01-01

In order to facilitate cooperation between underwater robots, it is a must for robots to exchange information with unambiguous meaning. However, heterogeneity, existing in information pertaining to different robots, is a major obstruction. Therefore, this paper presents a networked ontology, named the Smart and Networking Underwater Robots in Cooperation Meshes (SWARMs) ontology, to address information heterogeneity and enable robots to have the same understanding of exchanged information. The SWARMs ontology uses a core ontology to interrelate a set of domain-specific ontologies, including the mission and planning, the robotic vehicle, the communication and networking, and the environment recognition and sensing ontology. In addition, the SWARMs ontology utilizes ontology constructs defined in the PR-OWL ontology to annotate context uncertainty based on the Multi-Entity Bayesian Network (MEBN) theory. Thus, the SWARMs ontology can provide both a formal specification for information that is necessarily exchanged between robots and a command and control entity, and also support for uncertainty reasoning. A scenario on chemical pollution monitoring is described and used to showcase how the SWARMs ontology can be instantiated, be extended, represent context uncertainty, and support uncertainty reasoning. PMID:28287468

LinkMind: Link Optimization in Swarming Mobile Sensor Networks

DEFF Research Database (Denmark)

Ngo, Trung Dung

2012-01-01

of the most advantageous properties of the swarming wireless sensor network is that mobile nodes can work cooperatively to organize an ad-hoc network and optimize the network link capacity to maximize the transmission of gathered data from a source to a target. This paper describes a new method of link...... optimization of swarming mobile sensor networks. The new method is based on combination of the artificial potential force guaranteeing connectivities of the mobile sensor nodes and the max-flow min-cut theorem of graph theory ensuring optimization of the network link capacity. The developed algorithm...

Design of monitoring system for mail-sorting based on the Profibus S7 series PLC

Science.gov (United States)

Zhang, W.; Jia, S. H.; Wang, Y. H.; Liu, H.; Tang, G. C.

2017-01-01

With the rapid development of the postal express, the workload of mail sorting is increasing, but the automatic technology of mail sorting is not mature enough. In view of this, the system uses Siemens S7-300 PLC as the main station controller, PLC of Siemens S7-200/400 is from the station controller, through the man-machine interface configuration software MCGS, PROFIBUS-DP communication, RFID technology and mechanical sorting hand achieve mail classification sorting monitoring. Among them, distinguish mail-sorting by scanning RFID posted in the mail electronic bar code (fixed code), the system uses the corresponding controller on the acquisition of information processing, the processed information transmit to the sorting manipulator by PROFIBUS-DP. The system can realize accurate and efficient mail sorting, which will promote the development of mail sorting technology.

Particle swarm optimization algorithm based low cost magnetometer calibration

Science.gov (United States)

Ali, A. S.; Siddharth, S., Syed, Z., El-Sheimy, N.

2011-12-01

Inertial Navigation Systems (INS) consist of accelerometers, gyroscopes and a microprocessor provide inertial digital data from which position and orientation is obtained by integrating the specific forces and rotation rates. In addition to the accelerometers and gyroscopes, magnetometers can be used to derive the absolute user heading based on Earth's magnetic field. Unfortunately, the measurements of the magnetic field obtained with low cost sensors are corrupted by several errors including manufacturing defects and external electro-magnetic fields. Consequently, proper calibration of the magnetometer is required to achieve high accuracy heading measurements. In this paper, a Particle Swarm Optimization (PSO) based calibration algorithm is presented to estimate the values of the bias and scale factor of low cost magnetometer. The main advantage of this technique is the use of the artificial intelligence which does not need any error modeling or awareness of the nonlinearity. The estimated bias and scale factor errors from the proposed algorithm improve the heading accuracy and the results are also statistically significant. Also, it can help in the development of the Pedestrian Navigation Devices (PNDs) when combined with the INS and GPS/Wi-Fi especially in the indoor environments

Neural spike sorting using iterative ICA and a deflation-based approach.

Science.gov (United States)

Tiganj, Z; Mboup, M

2012-12-01

We propose a spike sorting method for multi-channel recordings. When applied in neural recordings, the performance of the independent component analysis (ICA) algorithm is known to be limited, since the number of recording sites is much lower than the number of neurons. The proposed method uses an iterative application of ICA and a deflation technique in two nested loops. In each iteration of the external loop, the spiking activity of one neuron is singled out and then deflated from the recordings. The internal loop implements a sequence of ICA and sorting for removing the noise and all the spikes that are not fired by the targeted neuron. Then a final step is appended to the two nested loops in order to separate simultaneously fired spikes. We solve this problem by taking all possible pairs of the sorted neurons and apply ICA only on the segments of the signal during which at least one of the neurons in a given pair was active. We validate the performance of the proposed method on simulated recordings, but also on a specific type of real recordings: simultaneous extracellular-intracellular. We quantify the sorting results on the extracellular recordings for the spikes that come from the neurons recorded intracellularly. The results suggest that the proposed solution significantly improves the performance of ICA in spike sorting.

Source characteristics and geological implications of the January 2016 induced earthquake swarm near Crooked Lake, Alberta

Science.gov (United States)

Wang, Ruijia; Gu, Yu Jeffrey; Schultz, Ryan; Zhang, Miao; Kim, Ahyi

2017-08-01

On 2016 January 12, an intraplate earthquake with an initial reported local magnitude (ML) of 4.8 shook the town of Fox Creek, Alberta. While there were no reported damages, this earthquake was widely felt by the local residents and suspected to be induced by the nearby hydraulic-fracturing (HF) operations. In this study, we determine the earthquake source parameters using moment tensor inversions, and then detect and locate the associated swarm using a waveform cross-correlation based method. The broad-band seismic recordings from regional arrays suggest a moment magnitude (M) 4.1 for this event, which is the largest in Alberta in the past decade. Similar to other recent M ∼ 3 earthquakes near Fox Creek, the 2016 January 12 earthquake exhibits a dominant strike-slip (strike = 184°) mechanism with limited non-double-couple components (∼22 per cent). This resolved focal mechanism, which is also supported by forward modelling and P-wave first motion analysis, indicates an NE-SW oriented compressional axis consistent with the maximum compressive horizontal stress orientations delineated from borehole breakouts. Further detection analysis on industry-contributed recordings unveils 1108 smaller events within 3 km radius of the epicentre of the main event, showing a close spatial-temporal relation to a nearby HF well. The majority of the detected events are located above the basement, comparable to the injection depth (3.5 km) on the Duvernay shale Formation. The spatial distribution of this earthquake cluster further suggests that (1) the source of the sequence is an N-S-striking fault system and (2) these earthquakes were induced by an HF well close to but different from the well that triggered a previous (January 2015) earthquake swarm. Reactivation of pre-existing, N-S oriented faults analogous to the Pine Creek fault zone, which was reported by earlier studies of active source seismic and aeromagnetic data, are likely responsible for the occurrence of the

Improved quantum-behaved particle swarm optimization with local search strategy

Directory of Open Access Journals (Sweden)

Maolong Xi

2017-03-01

Full Text Available Quantum-behaved particle swarm optimization, which was motivated by analysis of particle swarm optimization and quantum system, has shown compared performance in finding the optimal solutions for many optimization problems to other evolutionary algorithms. To address the problem of premature, a local search strategy is proposed to improve the performance of quantum-behaved particle swarm optimization. In proposed local search strategy, a super particle is presented which is a collection body of randomly selected particles’ dimension information in the swarm. The selected probability of particles in swarm is different and determined by their fitness values. To minimization problems, the fitness value of one particle is smaller; the selected probability is more and will contribute more information in constructing the super particle. In addition, in order to investigate the influence on algorithm performance with different local search space, four methods of computing the local search radius are applied in local search strategy and propose four variants of local search quantum-behaved particle swarm optimization. Empirical studies on a suite of well-known benchmark functions are undertaken in order to make an overall performance comparison among the proposed methods and other quantum-behaved particle swarm optimization. The simulation results show that the proposed quantum-behaved particle swarm optimization variants have better advantages over the original quantum-behaved particle swarm optimization.

Towards a Logical Distinction Between Swarms and Aftershock Sequences

Science.gov (United States)

Gardine, M.; Burris, L.; McNutt, S.

2007-12-01

The distinction between swarms and aftershock sequences has, up to this point, been fairly arbitrary and non- uniform. Typically 0.5 to 1 order of magnitude difference between the mainshock and largest aftershock has been a traditional choice, but there are many exceptions. Seismologists have generally assumed that the mainshock carries most of the energy, but this is only true if it is sufficiently large compared to the size and numbers of aftershocks. Here we present a systematic division based on energy of the aftershock sequence compared to the energy of the largest event of the sequence. It is possible to calculate the amount of aftershock energy assumed to be in the sequence using the b-value of the frequency-magnitude relation with a fixed choice of magnitude separation (M-mainshock minus M-largest aftershock). Assuming that the energy of an aftershock sequence is less than the energy of the mainshock, the b-value at which the aftershock energy exceeds that of the mainshock energy determines the boundary between aftershock sequences and swarms. The amount of energy for various choices of b-value is also calculated using different values of magnitude separation. When the minimum b-value at which the sequence energy exceeds that of the largest event/mainshock is plotted against the magnitude separation, a linear trend emerges. Values plotting above this line represent swarms and values plotting below it represent aftershock sequences. This scheme has the advantage that it represents a physical quantity - energy - rather than only statistical features of earthquake distributions. As such it may be useful to help distinguish swarms from mainshock/aftershock sequences and to better determine the underlying causes of earthquake swarms.

3rd international swarm seminar. Proceedings

International Nuclear Information System (INIS)

Lindinger, W.; Villinger, H.; Federer, W.

1983-01-01

47 papers on various problems of ion physics have been presented. The session headings are 1) recombination and electron attachment 2) transport of electrons in gases and liquids 3) swarm studies on collisions of metastable and on collisions of Rydberg atoms 4) ion neutral-interactions 5) ion transport in gases 6) applied aspects of swarm studies. (G.Q.)

Swarm Products and Space Weather Applications

DEFF Research Database (Denmark)

Stolle, Claudia; Olsen, Nils; Martini, Daniel

The Swarm satellite constellation mission provides high precision magnetic field data and models and other observations that enable us to explore near Earth space for example in terms of in situ electron density and electric fields. On board GPS observables can be used for sounding ionospheric...... in aeronomy and space weather. We will emphasize results from the Swarm mission....

The Q sort theory and technique.

Science.gov (United States)

Nyatanga, L

1989-10-01

This paper is based on the author's experience of using the Q sort technique with BA Social Sciences (BASS) students, and the community psychiatric nursing (CPN, ENB No 811 course). The paper focuses on two main issues: 1. The theoretical assumptions underpinning the Q Sort technique. Carl Rogers' self theory and some of the values of humanistic psychology are summarised. 2. The actual technique procedure and meaning of results are highlighted. As the Q Sort technique is potentially useful in a variety of sittings some of which are listed in this paper, the emphasis has deliberately been placed in understanding the theoretical underpinning and the operationalisation (sensitive interpretation) of the theory to practice.

Particle Swarm Transport through Immiscible Fluid Layers in a Fracture

Science.gov (United States)

Teasdale, N. D.; Boomsma, E.; Pyrak-Nolte, L. J.

2011-12-01

Immiscible fluids occur either naturally (e.g. oil & water) or from anthropogenic processes (e.g. liquid CO2 & water) in the subsurface and complicate the transport of natural or engineered micro- or nano-scale particles. In this study, we examined the effect of immiscible fluids on the formation and evolution of particle swarms in a fracture. A particle swarm is a collection of colloidal-size particles in a dilute suspension that exhibits cohesive behavior. Swarms fall under gravity with a velocity that is greater than the settling velocity of a single particle. Thus a particle swarm of colloidal contaminants can potentially travel farther and faster in a fracture than expected for a dispersion or emulsion of colloidal particles. We investigated the formation, evolution, and break-up of colloidal swarms under gravity in a uniform aperture fracture as hydrophobic/hydrophyllic particle swarms move across an oil-water interface. A uniform aperture fracture was fabricated from two transparent acrylic rectangular prisms (100 mm x 50 mm x 100 mm) that are separated by 1, 2.5, 5, 10 or 50 mm. The fracture was placed, vertically, inside a glass tank containing a layer of pure silicone oil (polydimethylsiloxane) on distilled water. Along the length of the fracture, 30 mm was filled with oil and 70 mm with water. Experiments were conducted using silicone oils with viscosities of 5, 10, 100, or 1000 cSt. Particle swarms (5 μl) were comprised of a 1% concentration (by mass) of 25 micron glass beads (hydrophilic) suspended in a water drop, or a 1% concentration (by mass) of 3 micron polystyrene fluorescent beads (hydrophobic) suspended in a water drop. The swarm behavior was imaged using an optical fluorescent imaging system composed of a CCD camera and by green (525 nm) LED arrays for illumination. Swarms were spherical and remained coherent as they fell through the oil because of the immiscibility of oil and water. However, as a swarm approached the oil-water interface, it

An initial ULF wave index derived from 2 years of Swarm observations

Science.gov (United States)

Papadimitriou, Constantinos; Balasis, Georgios; Daglis, Ioannis A.; Giannakis, Omiros

2018-03-01

The ongoing Swarm satellite mission provides an opportunity for better knowledge of the near-Earth electromagnetic environment. Herein, we use a new methodological approach for the detection and classification of ultra low-frequency (ULF) wave events observed by Swarm based on an existing time-frequency analysis (TFA) tool and utilizing a state-of-the-art high-resolution magnetic field model and Swarm Level 2 products (i.e., field-aligned currents - FACs - and the Ionospheric Bubble Index - IBI). We present maps of the dependence of ULF wave power with magnetic latitude and magnetic local time (MLT) as well as geographic latitude and longitude from the three satellites at their different locations in low-Earth orbit (LEO) for a period spanning 2 years after the constellation's final configuration. We show that the inclusion of the Swarm single-spacecraft FAC product in our analysis eliminates all the wave activity at high altitudes, which is physically unrealistic. Moreover, we derive a Swarm orbit-by-orbit Pc3 wave (20-100 MHz) index for the topside ionosphere and compare its values with the corresponding variations of solar wind variables and geomagnetic activity indices. This is the first attempt, to our knowledge, to derive a ULF wave index from LEO satellite data. The technique can be potentially used to define a new Level 2 product from the mission, the Swarm ULF wave index, which would be suitable for space weather applications.

Hyperspectral Image Classification Based on the Combination of Spatial-spectral Feature and Sparse Representation

Directory of Open Access Journals (Sweden)

YANG Zhaoxia

2015-07-01

Full Text Available In order to avoid the problem of being over-dependent on high-dimensional spectral feature in the traditional hyperspectral image classification, a novel approach based on the combination of spatial-spectral feature and sparse representation is proposed in this paper. Firstly, we extract the spatial-spectral feature by reorganizing the local image patch with the first d principal components(PCs into a vector representation, followed by a sorting scheme to make the vector invariant to local image rotation. Secondly, we learn the dictionary through a supervised method, and use it to code the features from test samples afterwards. Finally, we embed the resulting sparse feature coding into the support vector machine(SVM for hyperspectral image classification. Experiments using three hyperspectral data show that the proposed method can effectively improve the classification accuracy comparing with traditional classification methods.

Particle Transport and Size Sorting in Bubble Microstreaming Flow

Science.gov (United States)

Thameem, Raqeeb; Rallabandi, Bhargav; Wang, Cheng; Hilgenfeldt, Sascha

2014-11-01

Ultrasonic driving of sessile semicylindrical bubbles results in powerful steady streaming flows that are robust over a wide range of driving frequencies. In a microchannel, this flow field pattern can be fine-tuned to achieve size-sensitive sorting and trapping of particles at scales much smaller than the bubble itself; the sorting mechanism has been successfully described based on simple geometrical considerations. We investigate the sorting process in more detail, both experimentally (using new parameter variations that allow greater control over the sorting) and theoretically (incorporating the device geometry as well as the superimposed channel flow into an asymptotic theory). This results in optimized criteria for size sorting and a theoretical description that closely matches the particle behavior close to the bubble, the crucial region for size sorting.

A quantum particle swarm optimizer with chaotic mutation operator

International Nuclear Information System (INIS)

Coelho, Leandro dos Santos

2008-01-01

Particle swarm optimization (PSO) is a population-based swarm intelligence algorithm that shares many similarities with evolutionary computation techniques. However, the PSO is driven by the simulation of a social psychological metaphor motivated by collective behaviors of bird and other social organisms instead of the survival of the fittest individual. Inspired by the classical PSO method and quantum mechanics theories, this work presents a novel Quantum-behaved PSO (QPSO) using chaotic mutation operator. The application of chaotic sequences based on chaotic Zaslavskii map instead of random sequences in QPSO is a powerful strategy to diversify the QPSO population and improve the QPSO's performance in preventing premature convergence to local minima. The simulation results demonstrate good performance of the QPSO in solving a well-studied continuous optimization problem of mechanical engineering design

Monitoring the West Bohemian earthquake swarm in 2008/2009 by a temporary small-aperture seismic array

Science.gov (United States)

Hiemer, Stefan; Roessler, Dirk; Scherbaum, Frank

2012-04-01

The most recent intense earthquake swarm in West Bohemia lasted from 6 October 2008 to January 2009. Starting 12 days after the onset, the University of Potsdam monitored the swarm by a temporary small-aperture seismic array at 10 km epicentral distance. The purpose of the installation was a complete monitoring of the swarm including micro-earthquakes ( M L 0.0). In the course of this work, the main temporal features (frequency-magnitude distribution, propagation of back azimuth and horizontal slowness, occurrence rate of aftershock sequences and interevent-time distribution) of the recent 2008/2009 earthquake swarm are presented and discussed. Temporal changes of the coefficient of variation (based on interevent times) suggest that the swarm earthquake activity of the 2008/2009 swarm terminates by 12 January 2009. During the main phase in our studied swarm period after 19 October, the b value of the Gutenberg-Richter relation decreases from 1.2 to 0.8. This trend is also reflected in the power-law behavior of the seismic moment release. The corresponding total seismic moment release of 1.02×1017 Nm is equivalent to M L,max = 5.4.

Particle Swarm Optimization With Interswarm Interactive Learning Strategy.

Science.gov (United States)

Qin, Quande; Cheng, Shi; Zhang, Qingyu; Li, Li; Shi, Yuhui

2016-10-01

The learning strategy in the canonical particle swarm optimization (PSO) algorithm is often blamed for being the primary reason for loss of diversity. Population diversity maintenance is crucial for preventing particles from being stuck into local optima. In this paper, we present an improved PSO algorithm with an interswarm interactive learning strategy (IILPSO) by overcoming the drawbacks of the canonical PSO algorithm's learning strategy. IILPSO is inspired by the phenomenon in human society that the interactive learning behavior takes place among different groups. Particles in IILPSO are divided into two swarms. The interswarm interactive learning (IIL) behavior is triggered when the best particle's fitness value of both the swarms does not improve for a certain number of iterations. According to the best particle's fitness value of each swarm, the softmax method and roulette method are used to determine the roles of the two swarms as the learning swarm and the learned swarm. In addition, the velocity mutation operator and global best vibration strategy are used to improve the algorithm's global search capability. The IIL strategy is applied to PSO with global star and local ring structures, which are termed as IILPSO-G and IILPSO-L algorithm, respectively. Numerical experiments are conducted to compare the proposed algorithms with eight popular PSO variants. From the experimental results, IILPSO demonstrates the good performance in terms of solution accuracy, convergence speed, and reliability. Finally, the variations of the population diversity in the entire search process provide an explanation why IILPSO performs effectively.

Knowledge Management and Problem Solving in Real Time: The Role of Swarm Intelligence

Directory of Open Access Journals (Sweden)

Chris W Callaghan

2016-06-01

Full Text Available Knowledge management research applied to the development of real-time research capability, or capability to solve societal problems in hours and days instead of years and decades, is perhaps increasingly important, given persistent global problems such as the Zika virus and rapidly developing antibiotic resistance. Drawing on swarm intelligence theory, this paper presents an approach to real-time research problem-solving in the form of a framework for understanding the complexity of real-time research and the challenges associated with maximizing collaboration. The objective of this research is to make explicit certain theoretical, methodological, and practical implications deriving from new literature on emerging technologies and new forms of problem solving and to offer a model of real-time problem solving based on a synthesis of the literature. Drawing from ant colony, bee colony, and particle swarm optimization, as well as other population-based metaheuristics, swarm intelligence principles are derived in support of improved effectiveness and efficiency for multidisciplinary human swarm problem-solving. This synthesis seeks to offer useful insights into the research process, by offering a perspective of what maximized collaboration, as a system, implies for real-time problem solving.

A Novel and Simple Spike Sorting Implementation.

Science.gov (United States)

Petrantonakis, Panagiotis C; Poirazi, Panayiota

2017-04-01

Monitoring the activity of multiple, individual neurons that fire spikes in the vicinity of an electrode, namely perform a Spike Sorting (SS) procedure, comprises one of the most important tools for contemporary neuroscience in order to reverse-engineer the brain. As recording electrodes' technology rabidly evolves by integrating thousands of electrodes in a confined spatial setting, the algorithms that are used to monitor individual neurons from recorded signals have to become even more reliable and computationally efficient. In this work, we propose a novel framework of the SS approach in which a single-step processing of the raw (unfiltered) extracellular signal is sufficient for both the detection and sorting of the activity of individual neurons. Despite its simplicity, the proposed approach exhibits comparable performance with state-of-the-art approaches, especially for spike detection in noisy signals, and paves the way for a new family of SS algorithms with the potential for multi-recording, fast, on-chip implementations.

Particle ''swarm'' dynamics in triboelectric systems

International Nuclear Information System (INIS)

Vinay, Stephen J.; Jhon, Myung S.

2001-01-01

Using state-of-the-art flow/particle visualization and animation techniques, the time-dependent statistical distributions of charged-particle ''swarms'' exposed to external fields (both electrostatic and flow) are examined. We found that interparticle interaction and drag forces mainly influenced swarm dispersion in a Lagrangian reference frame, whereas the average particle trajectory was affected primarily by the external electric and flow fields

A Parallel Particle Swarm Optimization Algorithm Accelerated by Asynchronous Evaluations

Science.gov (United States)

Venter, Gerhard; Sobieszczanski-Sobieski, Jaroslaw

2005-01-01

A parallel Particle Swarm Optimization (PSO) algorithm is presented. Particle swarm optimization is a fairly recent addition to the family of non-gradient based, probabilistic search algorithms that is based on a simplified social model and is closely tied to swarming theory. Although PSO algorithms present several attractive properties to the designer, they are plagued by high computational cost as measured by elapsed time. One approach to reduce the elapsed time is to make use of coarse-grained parallelization to evaluate the design points. Previous parallel PSO algorithms were mostly implemented in a synchronous manner, where all design points within a design iteration are evaluated before the next iteration is started. This approach leads to poor parallel speedup in cases where a heterogeneous parallel environment is used and/or where the analysis time depends on the design point being analyzed. This paper introduces an asynchronous parallel PSO algorithm that greatly improves the parallel e ciency. The asynchronous algorithm is benchmarked on a cluster assembled of Apple Macintosh G5 desktop computers, using the multi-disciplinary optimization of a typical transport aircraft wing as an example.

Simultaneous field-aligned currents at Swarm and Cluster satellites

DEFF Research Database (Denmark)

Dunlop, M. W.; Yang, J. Y.; Yang, Y. Y.

2015-01-01

altitude) orbits using a particular Swarm and Cluster conjunction. The Cluster signatures are interpreted and ordered through joint mapping of the ground/magnetospheric footprints and estimation of the auroral zone boundaries (taken as indication of the boundaries of Region 1 and Region 2 currents). We...... find clear evidence of both small-scale and large-scale FACs and clear matching of the behavior and structure of the large-scale currents at both Cluster and Swarm. The methodology is made possible through the joint operations of Cluster and Swarm, which contain, in the first several months of Swarm...

ALGORITHM FOR SORTING GROUPED DATA

Science.gov (United States)

Evans, J. D.

1994-01-01

It is often desirable to sort data sets in ascending or descending order. This becomes more difficult for grouped data, i.e., multiple sets of data, where each set of data involves several measurements or related elements. The sort becomes increasingly cumbersome when more than a few elements exist for each data set. In order to achieve an efficient sorting process, an algorithm has been devised in which the maximum most significant element is found, and then compared to each element in succession. The program was written to handle the daily temperature readings of the Voyager spacecraft, particularly those related to the special tracking requirements of Voyager 2. By reducing each data set to a single representative number, the sorting process becomes very easy. The first step in the process is to reduce the data set of width 'n' to a data set of width '1'. This is done by representing each data set by a polynomial of length 'n' based on the differences of the maximum and minimum elements. These single numbers are then sorted and converted back to obtain the original data sets. Required input data are the name of the data file to read and sort, and the starting and ending record numbers. The package includes a sample data file, containing 500 sets of data with 5 elements in each set. This program will perform a sort of the 500 data sets in 3 - 5 seconds on an IBM PC-AT with a hard disk; on a similarly equipped IBM PC-XT the time is under 10 seconds. This program is written in BASIC (specifically the Microsoft QuickBasic compiler) for interactive execution and has been implemented on the IBM PC computer series operating under PC-DOS with a central memory requirement of approximately 40K of 8 bit bytes. A hard disk is desirable for speed considerations, but is not required. This program was developed in 1986.

The Weighted Support Vector Machine Based on Hybrid Swarm Intelligence Optimization for Icing Prediction of Transmission Line

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Xiaomin Xu

2015-01-01

Full Text Available Not only can the icing coat on transmission line cause the electrical fault of gap discharge and icing flashover but also it will lead to the mechanical failure of tower, conductor, insulators, and others. It will bring great harm to the people’s daily life and work. Thus, accurate prediction of ice thickness has important significance for power department to control the ice disaster effectively. Based on the analysis of standard support vector machine, this paper presents a weighted support vector machine regression model based on the similarity (WSVR. According to the different importance of samples, this paper introduces the weighted support vector machine and optimizes its parameters by hybrid swarm intelligence optimization algorithm with the particle swarm and ant colony (PSO-ACO, which improves the generalization ability of the model. In the case study, the actual data of ice thickness and climate in a certain area of Hunan province have been used to predict the icing thickness of the area, which verifies the validity and applicability of this proposed method. The predicted results show that the intelligent model proposed in this paper has higher precision and stronger generalization ability.

Modified Chaos Particle Swarm Optimization-Based Optimized Operation Model for Stand-Alone CCHP Microgrid

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Fei Wang

2017-07-01

Full Text Available The optimized dispatch of different distributed generations (DGs in stand-alone microgrid (MG is of great significance to the operation’s reliability and economy, especially for energy crisis and environmental pollution. Based on controllable load (CL and combined cooling-heating-power (CCHP model of micro-gas turbine (MT, a multi-objective optimization model with relevant constraints to optimize the generation cost, load cut compensation and environmental benefit is proposed in this paper. The MG studied in this paper consists of photovoltaic (PV, wind turbine (WT, fuel cell (FC, diesel engine (DE, MT and energy storage (ES. Four typical scenarios were designed according to different day types (work day or weekend and weather conditions (sunny or rainy in view of the uncertainty of renewable energy in variable situations and load fluctuation. A modified dispatch strategy for CCHP is presented to further improve the operation economy without reducing the consumers’ comfort feeling. Chaotic optimization and elite retention strategy are introduced into basic particle swarm optimization (PSO to propose modified chaos particle swarm optimization (MCPSO whose search capability and convergence speed are improved greatly. Simulation results validate the correctness of the proposed model and the effectiveness of MCPSO algorithm in the optimized operation application of stand-alone MG.

Colias: An Autonomous Micro Robot for Swarm Robotic Applications

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Farshad Arvin

2014-07-01

Full Text Available Robotic swarms that take inspiration from nature are becoming a fascinating topic for multi-robot researchers. The aim is to control a large number of simple robots in order to solve common complex tasks. Due to the hardware complexities and cost of robot platforms, current research in swarm robotics is mostly performed by simulation software. The simulation of large numbers of these robots in robotic swarm applications is extremely complex and often inaccurate due to the poor modelling of external conditions. In this paper, we present the design of a low-cost, open-platform, autonomous micro-robot (Colias for robotic swarm applications. Colias employs a circular platform with a diameter of 4 cm. It has a maximum speed of 35 cm/s which enables it to be used in swarm scenarios very quickly over large arenas. Long-range infrared modules with an adjustable output power allow the robot to communicate with its direct neighbours at a range of 0.5 cm to 2 m. Colias has been designed as a complete platform with supporting software development tools for robotics education and research. It has been tested in both individual and swarm scenarios, and the observed results demonstrate its feasibility for use as a micro-sized mobile robot and as a low-cost platform for robot swarm applications.

An Automated Sorting System Based on Virtual Instrumentation Techniques

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Rodica Holonec

2008-07-01

Full Text Available The application presented in this paper represents an experimental model and it refers to the implementing of an automated sorting system for pieces of same shape but different sizes and/or colors. The classification is made according to two features: the color and weight of these pieces. The system is a complex combination of NI Vision hardware and software tools, strain gauges transducers, signal conditioning connected to data acquisition boards, motion and control elements. The system is very useful for students to learn and experiment different virtual instrumentation techniques in order to be able to develop a large field of applications from inspection and process control to sorting and assembly

PREDIKSI KEMUNGKINAN BPREDIKSI BANJIR SUNGAI CITARUM DENGAN LOGIKA FUZZY HASIL ALGORITMA PARTICLE SWARM OPTIMIZATION

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Phitsa Mauliana

2016-09-01

Full Text Available Abstract The purpose of this paper is the prediction of the possibility of flooding using fuzzy logic results of data processing algorithms using particle swarm optimization (PSO. Flooding is the water level exceeds the normal stream. Usually on the face of water and erratic rainfall cause people cannot predict the occurrence of floods. It required an effort to predict the flood in order to minimize losses resulting from flooding. Particle swarm optimization algorithm can solve a system of nonlinear equations for predicting flooding is a non-linear data processing. Particle swarm optimization algorithm and sample used was rainfall and water level, the result is a flood prediction accuracy of 73% based on the resulting confusion matrix calculations. Implementation of fuzzy logic can help predict the likelihood of flooding around the Citarum River. Keywords: Prediction, Flood, Particle Swarm Optimization, Fuzzy Logic.

Sorting a distribution theory

CERN Document Server

Mahmoud, Hosam M

2011-01-01

A cutting-edge look at the emerging distributional theory of sorting Research on distributions associated with sorting algorithms has grown dramatically over the last few decades, spawning many exact and limiting distributions of complexity measures for many sorting algorithms. Yet much of this information has been scattered in disparate and highly specialized sources throughout the literature. In Sorting: A Distribution Theory, leading authority Hosam Mahmoud compiles, consolidates, and clarifies the large volume of available research, providing a much-needed, comprehensive treatment of the

A COMPARATIVE STUDY ON MULTI-SWARM OPTIMISATION AND BAT ALGORITHM FOR UNCONSTRAINED NON LINEAR OPTIMISATION PROBLEMS

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Evans BAIDOO

2016-12-01

Full Text Available A study branch that mocks-up a population of network of swarms or agents with the ability to self-organise is Swarm intelligence. In spite of the huge amount of work that has been done in this area in both theoretically and empirically and the greater success that has been attained in several aspects, it is still ongoing and at its infant stage. An immune system, a cloud of bats, or a flock of birds are distinctive examples of a swarm system. . In this study, two types of meta-heuristics algorithms based on population and swarm intelligence - Multi Swarm Optimization (MSO and Bat algorithms (BA - are set up to find optimal solutions of continuous non-linear optimisation models. In order to analyze and compare perfect solutions at the expense of performance of both algorithms, a chain of computational experiments on six generally used test functions for assessing the accuracy and the performance of algorithms, in swarm intelligence fields are used. Computational experiments show that MSO algorithm seems much superior to BA.

Phenology of Honey Bee Swarm Departure in New Jersey, United States.

Science.gov (United States)

Gilley, D C; Courtright, T J; Thom, C

2018-03-31

Departure of swarms from honey bee (Apis mellifera Linnaeus (Hymenoptera: Apidae)) nests is an important reproductive event for wild honey bee colonies and economically costly in managed bee colonies. The seasonal timing of swarm departure varies regionally and annually, creating challenges for honey bee management and emphasizing the potential for swarming behavior to be affected by plant-pollinator phenological mismatch. In this study, we first document variability in the timing of swarm departure across the large and heterogeneous geographical area of New Jersey over 4 years using 689 swarm-cluster observations. Second, hypothesizing that honey bee colonies adaptively tune the timing of swarm departure to match floral food-resource availability, we predicted that growing degree-days could be used to account for regional and annual variability. To test this idea, we used local weather records to determine the growing degree-day on which each swarm cluster was observed and tested for differences among climate regions and years. The state-wide mean swarm cluster date was May 15 (± 0.6 d), with moderate but significant differences among the state's five climate regions and between years. Use of degree-day information suggests that local heat accumulation can account for some climate-region differences in swarm-departure timing. Annual variation existed on a scale of only several days and was not accounted for by growing degree-days, suggesting little adaptive tuning of swarm-departure timing with respect to local heat accumulation.

Swarm Intelligence-Based Smart Energy Allocation Strategy for Charging Stations of Plug-In Hybrid Electric Vehicles

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Imran Rahman

2015-01-01

Full Text Available Recent researches towards the use of green technologies to reduce pollution and higher penetration of renewable energy sources in the transportation sector have been gaining popularity. In this wake, extensive participation of plug-in hybrid electric vehicles (PHEVs requires adequate charging allocation strategy using a combination of smart grid systems and smart charging infrastructures. Daytime charging stations will be needed for daily usage of PHEVs due to the limited all-electric range. Intelligent energy management is an important issue which has already drawn much attention of researchers. Most of these works require formulation of mathematical models with extensive use of computational intelligence-based optimization techniques to solve many technical problems. In this paper, gravitational search algorithm (GSA has been applied and compared with another member of swarm family, particle swarm optimization (PSO, considering constraints such as energy price, remaining battery capacity, and remaining charging time. Simulation results obtained for maximizing the highly nonlinear objective function evaluate the performance of both techniques in terms of best fitness.

Earthquake Swarm in Armutlu Peninsula, Eastern Marmara Region, Turkey

Science.gov (United States)

Yavuz, Evrim; Çaka, Deniz; Tunç, Berna; Serkan Irmak, T.; Woith, Heiko; Cesca, Simone; Lühr, Birger-Gottfried; Barış, Şerif

2015-04-01

conformable with the features of local faults in the region. The spatial vicinity of the earthquake swarm and the Yalova geothermal area may suggest a physical link between the ongoing exploitation of the reservoir and the earthquake activity. Keywords: Earthquake swarm, Armutlu Peninsula, ARNET, geothermal activity

Emergent runaway into an avoidance area in a swarm of soldier crabs.

Science.gov (United States)

Murakami, Hisashi; Tomaru, Takenori; Nishiyama, Yuta; Moriyama, Toru; Niizato, Takayuki; Gunji, Yukio-Pegio

2014-01-01

Emergent behavior that arises from a mass effect is one of the most striking aspects of collective animal groups. Investigating such behavior would be important in order to understand how individuals interact with their neighbors. Although there are many experiments that have used collective animals to investigate social learning or conflict between individuals and society such as that between a fish and a school, reports on mass effects are rare. In this study, we show that a swarm of soldier crabs could spontaneously enter a water pool, which are usually avoided, by forming densely populated part of a swarm at the edge of the water pool. Moreover, we show that the observed behavior can be explained by the model of collective behavior based on inherent noise that is individuals' different velocities in a directed group. Our results suggest that inherent noise, which is widely seen in collective animals, can contribute to formation and/or maintenance of a swarm and that the dense swarm can enter the pool by means of enhanced inherent noise.

Emergent runaway into an avoidance area in a swarm of soldier crabs.

Directory of Open Access Journals (Sweden)

Hisashi Murakami

Full Text Available Emergent behavior that arises from a mass effect is one of the most striking aspects of collective animal groups. Investigating such behavior would be important in order to understand how individuals interact with their neighbors. Although there are many experiments that have used collective animals to investigate social learning or conflict between individuals and society such as that between a fish and a school, reports on mass effects are rare. In this study, we show that a swarm of soldier crabs could spontaneously enter a water pool, which are usually avoided, by forming densely populated part of a swarm at the edge of the water pool. Moreover, we show that the observed behavior can be explained by the model of collective behavior based on inherent noise that is individuals' different velocities in a directed group. Our results suggest that inherent noise, which is widely seen in collective animals, can contribute to formation and/or maintenance of a swarm and that the dense swarm can enter the pool by means of enhanced inherent noise.

Interannual variability of sorted bedforms in the coastal German Bight (SE North Sea)

Science.gov (United States)

Mielck, F.; Holler, P.; Bürk, D.; Hass, H. C.

2015-12-01

Sorted bedforms are ubiquitous on the inner continental shelves worldwide. They are described as spatially-grain-size-sorted features consisting of small rippled medium-to-coarse sand and can remain stable for decades. However, the knowledge about their genesis and development is still fragmentary. For this study, a representative investigation area (water depthwinnowing and focusing processes take place during periodically recurring storm surges, which change the shapes of the features. Moreover, variations in alignments and sizes of the small ripple formations were detected. They seem to indicate the directions and intensities of previous storm events.

ADAPTIVE DISTRIBUTION OF A SWARM OF HETEROGENEOUS ROBOTS

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Amanda Prorok

2016-02-01

Full Text Available We present a method that distributes a swarm of heterogeneous robots among a set of tasks that require specialized capabilities in order to be completed. We model the system of heterogeneous robots as a community of species, where each species (robot type is defined by the traits (capabilities that it owns. Our method is based on a continuous abstraction of the swarm at a macroscopic level as we model robots switching between tasks. We formulate an optimization problem that produces an optimal set of transition rates for each species, so that the desired trait distribution is reached as quickly as possible. Since our method is based on the derivation of an analytical gradient, it is very efficient with respect to state-of-the-art methods. Building on this result, we propose a real-time optimization method that enables an online adaptation of transition rates. Our approach is well-suited for real-time applications that rely on online redistribution of large-scale robotic systems.

Fog computing job scheduling optimization based on bees swarm

Science.gov (United States)

Bitam, Salim; Zeadally, Sherali; Mellouk, Abdelhamid

2018-04-01

Fog computing is a new computing architecture, composed of a set of near-user edge devices called fog nodes, which collaborate together in order to perform computational services such as running applications, storing an important amount of data, and transmitting messages. Fog computing extends cloud computing by deploying digital resources at the premise of mobile users. In this new paradigm, management and operating functions, such as job scheduling aim at providing high-performance, cost-effective services requested by mobile users and executed by fog nodes. We propose a new bio-inspired optimization approach called Bees Life Algorithm (BLA) aimed at addressing the job scheduling problem in the fog computing environment. Our proposed approach is based on the optimized distribution of a set of tasks among all the fog computing nodes. The objective is to find an optimal tradeoff between CPU execution time and allocated memory required by fog computing services established by mobile users. Our empirical performance evaluation results demonstrate that the proposal outperforms the traditional particle swarm optimization and genetic algorithm in terms of CPU execution time and allocated memory.

Swarm Intelligence-Based Hybrid Models for Short-Term Power Load Prediction

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Jianzhou Wang

2014-01-01

Full Text Available Swarm intelligence (SI is widely and successfully applied in the engineering field to solve practical optimization problems because various hybrid models, which are based on the SI algorithm and statistical models, are developed to further improve the predictive abilities. In this paper, hybrid intelligent forecasting models based on the cuckoo search (CS as well as the singular spectrum analysis (SSA, time series, and machine learning methods are proposed to conduct short-term power load prediction. The forecasting performance of the proposed models is augmented by a rolling multistep strategy over the prediction horizon. The test results are representative of the out-performance of the SSA and CS in tuning the seasonal autoregressive integrated moving average (SARIMA and support vector regression (SVR in improving load forecasting, which indicates that both the SSA-based data denoising and SI-based intelligent optimization strategy can effectively improve the model’s predictive performance. Additionally, the proposed CS-SSA-SARIMA and CS-SSA-SVR models provide very impressive forecasting results, demonstrating their strong robustness and universal forecasting capacities in terms of short-term power load prediction 24 hours in advance.

Phenotypic and functional characterization of earthworm coelomocyte subsets: Linking light scatter-based cell typing and imaging of the sorted populations.

Science.gov (United States)

Engelmann, Péter; Hayashi, Yuya; Bodó, Kornélia; Ernszt, Dávid; Somogyi, Ildikó; Steib, Anita; Orbán, József; Pollák, Edit; Nyitrai, Miklós; Németh, Péter; Molnár, László

2016-12-01

Flow cytometry is a common approach to study invertebrate immune cells including earthworm coelomocytes. However, the link between light-scatter- and microscopy-based phenotyping remains obscured. Here we show, by means of light scatter-based cell sorting, both subpopulations (amoebocytes and eleocytes) can be physically isolated with good sort efficiency and purity confirmed by downstream morphological and cytochemical applications. Immunocytochemical analysis using anti-EFCC monoclonal antibodies combined with phalloidin staining has revealed antigenically distinct, sorted subsets. Screening of lectin binding capacity indicated wheat germ agglutinin (WGA) as the strongest reactor to amoebocytes. This is further evidenced by WGA inhibition assays that suggest high abundance of N-acetyl-d-glucosamine in amoebocytes. Post-sort phagocytosis assays confirmed the functional differences between amoebocytes and eleocytes, with the former being in favor of bacterial engulfment. This study has proved successful in linking flow cytometry and microscopy analysis and provides further experimental evidence of phenotypic and functional heterogeneity in earthworm coelomocyte subsets. Copyright © 2016 Elsevier Ltd. All rights reserved.

Set-Based Discrete Particle Swarm Optimization Based on Decomposition for Permutation-Based Multiobjective Combinatorial Optimization Problems.

Science.gov (United States)

Yu, Xue; Chen, Wei-Neng; Gu, Tianlong; Zhang, Huaxiang; Yuan, Huaqiang; Kwong, Sam; Zhang, Jun

2017-08-07

This paper studies a specific class of multiobjective combinatorial optimization problems (MOCOPs), namely the permutation-based MOCOPs. Many commonly seen MOCOPs, e.g., multiobjective traveling salesman problem (MOTSP), multiobjective project scheduling problem (MOPSP), belong to this problem class and they can be very different. However, as the permutation-based MOCOPs share the inherent similarity that the structure of their search space is usually in the shape of a permutation tree, this paper proposes a generic multiobjective set-based particle swarm optimization methodology based on decomposition, termed MS-PSO/D. In order to coordinate with the property of permutation-based MOCOPs, MS-PSO/D utilizes an element-based representation and a constructive approach. Through this, feasible solutions under constraints can be generated step by step following the permutation-tree-shaped structure. And problem-related heuristic information is introduced in the constructive approach for efficiency. In order to address the multiobjective optimization issues, the decomposition strategy is employed, in which the problem is converted into multiple single-objective subproblems according to a set of weight vectors. Besides, a flexible mechanism for diversity control is provided in MS-PSO/D. Extensive experiments have been conducted to study MS-PSO/D on two permutation-based MOCOPs, namely the MOTSP and the MOPSP. Experimental results validate that the proposed methodology is promising.

Particle Swarm Optimisation with Spatial Particle Extension

DEFF Research Database (Denmark)

Krink, Thiemo; Vesterstrøm, Jakob Svaneborg; Riget, Jacques

2002-01-01

In this paper, we introduce spatial extension to particles in the PSO model in order to overcome premature convergence in iterative optimisation. The standard PSO and the new model (SEPSO) are compared w.r.t. performance on well-studied benchmark problems. We show that the SEPSO indeed managed...

Protein Sorting Prediction

DEFF Research Database (Denmark)

Nielsen, Henrik

2017-01-01

and drawbacks of each of these approaches is described through many examples of methods that predict secretion, integration into membranes, or subcellular locations in general. The aim of this chapter is to provide a user-level introduction to the field with a minimum of computational theory.......Many computational methods are available for predicting protein sorting in bacteria. When comparing them, it is important to know that they can be grouped into three fundamentally different approaches: signal-based, global-property-based and homology-based prediction. In this chapter, the strengths...

Modelling Oil-Spill Detection with Swarm Drones

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F. Aznar

2014-01-01

Full Text Available Nowadays, swarm robotics research is having a great increase due to the benefits derived from its use, such as robustness, parallelism, and flexibility. Unlike distributed robotic systems, swarm robotics emphasizes a large number of robots, and promotes scalability. Among the multiple applications of such systems we could find are exploring unstructured environments, resource monitoring, or distributed sensing. Two of these applications, monitoring, and perimeter/area detection of a given resource, have several ecological uses. One of them is the detection and monitoring of pollutants to delimit their perimeter and area accurately. Maritime activity has been increasing gradually in recent years. Many ships carry products such as oil that can adversely affect the environment. Such products can produce high levels of pollution in case of being spilled into sea. In this paper we will present a distributed system which monitors, covers, and surrounds a resource by using a swarm of homogeneous low cost drones. These drones only use their local sensory information and do not require any direct communication between them. Taking into account the properties of this kind of oil spills we will present a microscopic model for a swarm of drones, capable of monitoring these spills properly. Furthermore, we will analyse the proper macroscopic operation of the swarm. The analytical and experimental results presented here show the proper evolution of our system.

The January 2006 Volcanic-Tectonic Earthquake Swarm at Mount Martin, Alaska

Science.gov (United States)

Dixon, James P.; Power, John A.

2009-01-01

On January 8, 2006, a swarm of volcanic-tectonic earthquakes began beneath Mount Martin at the southern end of the Katmai volcanic cluster. This was the first recorded swarm at Mount Martin since continuous seismic monitoring began in 1996. The number of located earthquakes increased during the next four days, reaching a peak on January 11. For the next two days, the seismic activity decreased, and on January 14, the number of events increased to twice the previous day's total. Following this increase in activity, seismicity declined, returning to background levels by the end of the month. The Alaska Volcano Observatory located 860 earthquakes near Mount Martin during January 2006. No additional signs of volcanic unrest were noted in association with this earthquake swarm. The earthquakes in the Mount Martin swarm, relocated using the double difference technique, formed an elongated cluster dipping to the southwest. Focal mechanisms beneath Mount Martin show a mix of normal, thrust, and strike-slip solutions, with normal focal mechanisms dominating. For earthquakes more than 1 km from Mount Martin, all focal mechanisms showed normal faulting. The calculated b-value for the Mount Martin swarm is 0.98 and showed no significant change before, during, or after the swarm. The triggering mechanism for the Mount Martin swarm is unknown. The time-history of earthquake occurrence is indicative of a volcanic cause; however, there were no low-frequency events or observations, such as increased steaming associated with the swarm. During the swarm, there was no change in the b-value, and the distribution and type of focal mechanisms were similar to those in the period before the anomalous activity. The short duration of the swarm, the similarity in observed focal mechanisms, and the lack of additional signs of unrest suggest this swarm did not result from a large influx of magma within the shallow crust beneath Mount Martin.

MODELING WORK OF SORTING STATION USING UML

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O. V. Gorbova

2014-12-01

Full Text Available Purpose. The purpose of this paper is the construction of methods and models for the graphical representation process of sorting station, using the unified modeling language (UML. Methodology. Methods of graph theory, finite automata and the representation theory of queuing systems were used as the methods of investigation. A graphical representation of the process was implemented with using the Unified Modeling Language UML. The sorting station process representation is implemented as a state diagram and actions through a set of IBM Rational Rose. Graphs can show parallel operation of sorting station, the parallel existence and influence of objects process and the transition from one state to another. The IBM Rational Rose complex allows developing a diagram of work sequence of varying degrees of detailing. Findings. The study has developed a graphical representation method of the process of sorting station of different kind of complexity. All graphical representations are made using the UML. They are represented as a directed graph with the states. It is clear enough in the study of the subject area. Applying the methodology of the representation process, it allows becoming friendly with the work of any automation object very fast, and exploring the process during algorithms construction of sorting stations and other railway facilities. This model is implemented with using the Unified Modeling Language (UML using a combination of IBM Rational Rose. Originality. The representation process of sorting station was developed by means of the Unified Modeling Language (UML use. Methodology of representation process allows creating the directed graphs based on the order of execution of the works chain, objects and performers of these works. The UML allows visualizing, specifying, constructing and documenting, formalizing the representation process of sorting station and developing sequence diagrams of works of varying degrees of detail. Practical

Two Invariants of Human-Swarm Interaction

Science.gov (United States)

2018-01-16

Goodrich, 2013; Kolling, Sycara, Nunnally, & Lewis, 2013). Nunnally et al. explore bandwidth constraints on swarm-to- human communications , but assume that...the human can communicate with all of the agents in the swarm (Nunnally et al., 2012). Walker et al. investigate communication la- tency between a...Claiming that the collective state is the fundamental percept requires that the human is able to perceive, understand , and influence the abstracted

Hybrid glowworm swarm optimization for task scheduling in the cloud environment

Science.gov (United States)

Zhou, Jing; Dong, Shoubin

2018-06-01

In recent years many heuristic algorithms have been proposed to solve task scheduling problems in the cloud environment owing to their optimization capability. This article proposes a hybrid glowworm swarm optimization (HGSO) based on glowworm swarm optimization (GSO), which uses a technique of evolutionary computation, a strategy of quantum behaviour based on the principle of neighbourhood, offspring production and random walk, to achieve more efficient scheduling with reasonable scheduling costs. The proposed HGSO reduces the redundant computation and the dependence on the initialization of GSO, accelerates the convergence and more easily escapes from local optima. The conducted experiments and statistical analysis showed that in most cases the proposed HGSO algorithm outperformed previous heuristic algorithms to deal with independent tasks.

Elephant swarm water search algorithm for global optimization

Indian Academy of Sciences (India)

S Mandal

2018-02-07

Feb 7, 2018 ... Evolutionary computation and metaheuristics based on swarm intelligence are .... pollen for reproduction or flowering of plants by different pollinators such as insects. Due to long-distance ...... nodes of the denote genes and regulatory interactions between genes are ..... ioral ecology, 3rd ed. Oxford, UK: ...

Simultaneous Perturbation Particle Swarm Optimization and Its FPGA Implementation

OpenAIRE

Maeda, Yutaka; Matsushita, Naoto

2009-01-01

In this paper, we presented hardware implementation of the particle swarm optimization algorithm which is combination of the ordinary particle swarm optimization and the simultaneous perturbation method. FPGA is used to realize the system. This algorithm utilizes local information of objective function effectively without lack of advantage of the original particle swarm optimization. Moreover, the FPGA implementation gives higher operation speed effectively using parallelism of the particle s...

Color Image Enhancement Using Multiscale Retinex Based on Particle Swarm Optimization Method

Science.gov (United States)

Matin, F.; Jeong, Y.; Kim, K.; Park, K.

2018-01-01

This paper introduces, a novel method for the image enhancement using multiscale retinex and practical swarm optimization. Multiscale retinex is widely used image enhancement technique which intemperately pertains on parameters such as Gaussian scales, gain and offset, etc. To achieve the privileged effect, the parameters need to be tuned manually according to the image. In order to handle this matter, a developed retinex algorithm based on PSO has been used. The PSO method adjusted the parameters for multiscale retinex with chromaticity preservation (MSRCP) attains better outcome to compare with other existing methods. The experimental result indicates that the proposed algorithm is an efficient one and not only provides true color loyalty in low light conditions but also avoid color distortion at the same time.

Swarm Data Processing and First Scientific Results

DEFF Research Database (Denmark)

Olsen, Nils

2014-01-01

, accelerometer, plasma and electric field measurements. These observations will be distributed by ESA as Level-1b data, which are the calibrated and formatted time series of e.g. the magnetic field measurements taken by each of the three Swarm satellites. The talks presents a first scientific validation of Swarm...... Level-1b data products....

The electron drift velocity and longitudinal diffusion coefficient of an electron swarm in hydrogen at elevated swarm energies

International Nuclear Information System (INIS)

Blevin, H.A.; Fletcher, J.; Hunter, S.R.

1976-01-01

A study of the photons produced at electron-molecule excitation collisions has been used to obtain information on the behaviour of an electron swarm moving through a neutral gas under the influence of a uniform electric field. Specifically, values have been obtained for the electron drift velocity and the longitudinal diffusion coefficients under equilibrium swarm conditions, i.e. remote from any electrode. (author)

Swarm based mean-variance mapping optimization (MVMOS) for solving economic dispatch

Science.gov (United States)

Khoa, T. H.; Vasant, P. M.; Singh, M. S. Balbir; Dieu, V. N.

2014-10-01

The economic dispatch (ED) is an essential optimization task in the power generation system. It is defined as the process of allocating the real power output of generation units to meet required load demand so as their total operating cost is minimized while satisfying all physical and operational constraints. This paper introduces a novel optimization which named as Swarm based Mean-variance mapping optimization (MVMOS). The technique is the extension of the original single particle mean-variance mapping optimization (MVMO). Its features make it potentially attractive algorithm for solving optimization problems. The proposed method is implemented for three test power systems, including 3, 13 and 20 thermal generation units with quadratic cost function and the obtained results are compared with many other methods available in the literature. Test results have indicated that the proposed method can efficiently implement for solving economic dispatch.

Benefits of collective intelligence: Swarm intelligent foraging, an ethnographic research

Directory of Open Access Journals (Sweden)

Sivave Mashingaidze

2014-12-01

Full Text Available Wisdom of crowds; bees, colonies of ants, schools of fish, flocks of birds, and fireflies flashing synchronously are all examples of highly coordinated behaviors that emerge from collective, decentralized intelligence. This article is an ethnographic study of swarm intelligence foraging of swarms and the benefits derived from collective decision making. The author used using secondary data analysis to look at the benefits of swarm intelligence in decision making to achieve intended goals. Concepts like combined decision making and consensus were discussed and four principles of swarm intelligence were also discussed viz; coordination, cooperation, deliberation and collaboration. The research found out that collective decision making in swarms is the touchstone of achieving their goals. The research further recommended corporate to adopt collective intelligence for business sustainability.

Artificial Fish Swarm Algorithm-Based Particle Filter for Li-Ion Battery Life Prediction

Directory of Open Access Journals (Sweden)

Ye Tian

2014-01-01

Full Text Available An intelligent online prognostic approach is proposed for predicting the remaining useful life (RUL of lithium-ion (Li-ion batteries based on artificial fish swarm algorithm (AFSA and particle filter (PF, which is an integrated approach combining model-based method with data-driven method. The parameters, used in the empirical model which is based on the capacity fade trends of Li-ion batteries, are identified dependent on the tracking ability of PF. AFSA-PF aims to improve the performance of the basic PF. By driving the prior particles to the domain with high likelihood, AFSA-PF allows global optimization, prevents particle degeneracy, thereby improving particle distribution and increasing prediction accuracy and algorithm convergence. Data provided by NASA are used to verify this approach and compare it with basic PF and regularized PF. AFSA-PF is shown to be more accurate and precise.

Wage Sorting Trends

DEFF Research Database (Denmark)

Bagger, Jesper; Vejlin, Rune Majlund; Sørensen, Kenneth Lykke

Using a population-wide Danish Matched Employer-Employee panel from 1980-2006, we document a strong trend towards more positive assortative wage sorting. The correlation between worker and firm fixed effects estimated from a log wage regression increases from -0.07 in 1981 to .14 in 2001. The non......Using a population-wide Danish Matched Employer-Employee panel from 1980-2006, we document a strong trend towards more positive assortative wage sorting. The correlation between worker and firm fixed effects estimated from a log wage regression increases from -0.07 in 1981 to .14 in 2001....... The nonstationary wage sorting pattern is not due to compositional changes in the labor market, primarily occurs among high wage workers, and comprises 41 percent of the increase in the standard deviation of log real wages between 1980 and 2006. We show that the wage sorting trend is associated with worker...

Environmental species sorting dominates forest-bird community assembly across scales.

Science.gov (United States)

Özkan, Korhan; Svenning, Jens-Christian; Jeppesen, Erik

2013-01-01

Environmental species sorting and dispersal are seen as key factors in community assembly, but their relative importance and scale dependence remain uncertain, as the extent to which communities are consistently assembled throughout their biomes. To address these issues, we analysed bird metacommunity structure in a 1200-km(2) forested landscape (Istranca Forests) in Turkish Thrace at the margin of the Western Palaearctic (WP) temperate-forest biome. First, we used spatial regressions and Mantel tests to assess the relative importance of environmental and spatial factors as drivers of local species richness and composition within the metacommunity. Second, we analysed species' abundance-occupancy relationship across the metacommunity and used null models to assess whether occupancy is determined by species' environmental niches. Third, we used generalized linear models to test for links between species' metacommunity-wide occupancy and their broader WP regional populations and assessed whether these links are consistent with environmental species sorting. There was strong environmental control on local species richness and composition patterns within the metacommunity, but non-environmental spatial factors had also an important joint role. Null model analyses on randomized communities showed that species' occupancy across the metacommunity was strongly determined by species' environmental niches, with occupancy being related to niche position marginality. Species' metacommunity-wide occupancy correlated with their local abundance as well as with their range size and total abundance for the whole WP, suggesting that the same assembly mechanisms act consistently across local to regional scales. A species specialization index that was estimated by bird species' habitat use across France, incorporating both niche position and breadth, was significantly related to species' occupancy and abundance at both metacommunity and WP regional scales. Hence, the same niche

Flow sorting in aquatic ecology

Directory of Open Access Journals (Sweden)

Marcus Reckermann

2000-06-01

Full Text Available Flow sorting can be a very helpful tool in revealing phytoplankton and bacterial community structure and elaborating specific physiological parameters of isolated species. Droplet sorting has been the most common technique. Despite the high optical and hydro-dynamic stress for the cells to be sorted, many species grow in culture subsequent to sorting. To date, flow sorting has been applied to post-incubation separation in natural water samples to account for group-specific physiological parameters (radiotracer-uptake rates, to the production of clonal or non-clonal cultures from mixtures, to the isolaton of cell groups from natural assemblages for molecular analyses, and for taxonomic identification of sorted cells by microscopy. The application of cell sorting from natural water samples from the Wadden Sea, including different cryptophytes, cyanobacteria and diatoms, is shown, as well as the establishment of laboratory cultures from field samples. The optional use of a red laser to account for phycocyanine-rich cells is also discussed.

Swarm: robust and fast clustering method for amplicon-based studies

Science.gov (United States)

Rognes, Torbjørn; Quince, Christopher; de Vargas, Colomban; Dunthorn, Micah

2014-01-01

Popular de novo amplicon clustering methods suffer from two fundamental flaws: arbitrary global clustering thresholds, and input-order dependency induced by centroid selection. Swarm was developed to address these issues by first clustering nearly identical amplicons iteratively using a local threshold, and then by using clusters’ internal structure and amplicon abundances to refine its results. This fast, scalable, and input-order independent approach reduces the influence of clustering parameters and produces robust operational taxonomic units. PMID:25276506

Swarm: robust and fast clustering method for amplicon-based studies

Directory of Open Access Journals (Sweden)

Frédéric Mahé

2014-09-01

Full Text Available Popular de novo amplicon clustering methods suffer from two fundamental flaws: arbitrary global clustering thresholds, and input-order dependency induced by centroid selection. Swarm was developed to address these issues by first clustering nearly identical amplicons iteratively using a local threshold, and then by using clusters’ internal structure and amplicon abundances to refine its results. This fast, scalable, and input-order independent approach reduces the influence of clustering parameters and produces robust operational taxonomic units.

Cat Swarm Optimization Based Functional Link Artificial Neural Network Filter for Gaussian Noise Removal from Computed Tomography Images

Directory of Open Access Journals (Sweden)

M. Kumar

2016-01-01

Full Text Available Gaussian noise is one of the dominant noises, which degrades the quality of acquired Computed Tomography (CT image data. It creates difficulties in pathological identification or diagnosis of any disease. Gaussian noise elimination is desirable to improve the clarity of a CT image for clinical, diagnostic, and postprocessing applications. This paper proposes an evolutionary nonlinear adaptive filter approach, using Cat Swarm Functional Link Artificial Neural Network (CS-FLANN to remove the unwanted noise. The structure of the proposed filter is based on the Functional Link Artificial Neural Network (FLANN and the Cat Swarm Optimization (CSO is utilized for the selection of optimum weight of the neural network filter. The applied filter has been compared with the existing linear filters, like the mean filter and the adaptive Wiener filter. The performance indices, such as peak signal to noise ratio (PSNR, have been computed for the quantitative analysis of the proposed filter. The experimental evaluation established the superiority of the proposed filtering technique over existing methods.

Sorting Tubules Regulate Blood-Brain Barrier Transcytosis

Directory of Open Access Journals (Sweden)

Roberto Villaseñor

2017-12-01

Full Text Available Transcytosis across the blood-brain barrier (BBB regulates key processes of the brain, but the intracellular sorting mechanisms that determine successful receptor-mediated transcytosis in brain endothelial cells (BECs remain unidentified. Here, we used Transferrin receptor-based Brain Shuttle constructs to investigate intracellular transport in BECs, and we uncovered a pathway for the regulation of receptor-mediated transcytosis. By combining live-cell imaging and mathematical modeling in vitro with super-resolution microscopy of the BBB, we show that intracellular tubules promote transcytosis across the BBB. A monovalent construct (sFab sorted for transcytosis was localized to intracellular tubules, whereas a bivalent construct (dFab sorted for degradation formed clusters with impaired transport along tubules. Manipulating tubule biogenesis by overexpressing the small GTPase Rab17 increased dFab transport into tubules and induced its transcytosis in BECs. We propose that sorting tubules regulate transcytosis in BECs and may be a general mechanism for receptor-mediated transport across the BBB.

Ore sorting

International Nuclear Information System (INIS)

Hawkins, A.P.; Richards, A.W.

1982-01-01

In an ore sorting apparatus, ore particles are bombarded with neutrons in a chamber and sorted by detecting radiation emitted by isotopes of elements, such as gold, forming or contained in the particles, using detectors and selectively controlling fluid jets. The isotopes can be selectively recognised by their radiation characteristics. In an alternative embodiment, shorter life isotopes are formed by neutron bombardment and detection of radiation takes place immediately adjacent the region of bombardment

Traffic design and signal timing of staggered intersections based on a sorting strategy

Directory of Open Access Journals (Sweden)

Zhengyi Cai

2016-04-01

Full Text Available A staggered intersection is a special type of intersection in a road network. Its geographical characteristics consist of two T-legged intersections that cause the lost time per cycle to become longer than at cross intersections under conventional signal control, thus leading to low intersection efficiency. This article shows that the problem can be eliminated at the left–right type of staggered intersection by channelization and signal phasing, based on a sorting strategy and pre-signal, which reduce the amount of lost time during the signal cycle using the split distance as the sorting area. VISSIM was used to model and analyze the proposed method as well as the conventional method for comparison purposes. The simulation revealed that the proposed method reduced the average delays and maximum queue lengths in each movement and for the entire intersection, both in the peak hours and in the off-peak hour.

External parallel sorting with multiprocessor computers

International Nuclear Information System (INIS)

Comanceau, S.I.

1984-01-01

This article describes methods of external sorting in which the entire main computer memory is used for the internal sorting of entries, forming out of them sorted segments of the greatest possible size, and outputting them to external memories. The obtained segments are merged into larger segments until all entries form one ordered segment. The described methods are suitable for sequential files stored on magnetic tape. The needs of the sorting algorithm can be met by using the relatively slow peripheral storage devices (e.g., tapes, disks, drums). The efficiency of the external sorting methods is determined by calculating the total sorting time as a function of the number of entries to be sorted and the number of parallel processors participating in the sorting process

Design for Sustainability of Industrial Symbiosis based on Emergy and Multi-objective Particle Swarm Optimization

DEFF Research Database (Denmark)

Ren, Jingzheng; Liang, Hanwei; Dong, Liang

2016-01-01

approach for supporting decision-making in the design for the sustainability with the implementation of industrial symbiosis in chemical complex. Through incorporating the emergy theory, the model is formulated as a multi-objective approach that can optimize both the economic benefit and sustainable...... performance of the integrated industrial system. A set of emergy based evaluation index are designed. Multi-objective Particle Swarm Algorithm is proposed to solve the model, and the decision-makers are allowed to choose the suitable solutions form the Pareto solutions. An illustrative case has been studied...

Designing Artificial Neural Networks Using Particle Swarm Optimization Algorithms.

Science.gov (United States)

Garro, Beatriz A; Vázquez, Roberto A

2015-01-01

Artificial Neural Network (ANN) design is a complex task because its performance depends on the architecture, the selected transfer function, and the learning algorithm used to train the set of synaptic weights. In this paper we present a methodology that automatically designs an ANN using particle swarm optimization algorithms such as Basic Particle Swarm Optimization (PSO), Second Generation of Particle Swarm Optimization (SGPSO), and a New Model of PSO called NMPSO. The aim of these algorithms is to evolve, at the same time, the three principal components of an ANN: the set of synaptic weights, the connections or architecture, and the transfer functions for each neuron. Eight different fitness functions were proposed to evaluate the fitness of each solution and find the best design. These functions are based on the mean square error (MSE) and the classification error (CER) and implement a strategy to avoid overtraining and to reduce the number of connections in the ANN. In addition, the ANN designed with the proposed methodology is compared with those designed manually using the well-known Back-Propagation and Levenberg-Marquardt Learning Algorithms. Finally, the accuracy of the method is tested with different nonlinear pattern classification problems.

Particle swarm optimization based PID controller tuning for level control of two tank system

Science.gov (United States)

Vincent, Anju K.; Nersisson, Ruban

2017-11-01

Automatic control plays a vital role in industrial operation. In process industries, in order to have an improved and stable control system, we need a robust tuning method. In this paper Particle Swarm Optimization (PSO) based algorithm is proposed for the optimization of a PID controller for level control process. A two tank system is considered. Initially a PID controller is designed using an Internal Model Control (IMC). The results are compared with the PSO based controller setting. The performance of the controller is compared and analyzed by time domain specification. In order to validate the robustness of PID controller, disturbance is imposed. The system is simulated using MATLAB. The results show that the proposed method provides better controller performance.

Application of particle swarm optimization algorithm in the heating system planning problem.

Science.gov (United States)

Ma, Rong-Jiang; Yu, Nan-Yang; Hu, Jun-Yi

2013-01-01

Based on the life cycle cost (LCC) approach, this paper presents an integral mathematical model and particle swarm optimization (PSO) algorithm for the heating system planning (HSP) problem. The proposed mathematical model minimizes the cost of heating system as the objective for a given life cycle time. For the particularity of HSP problem, the general particle swarm optimization algorithm was improved. An actual case study was calculated to check its feasibility in practical use. The results show that the improved particle swarm optimization (IPSO) algorithm can more preferably solve the HSP problem than PSO algorithm. Moreover, the results also present the potential to provide useful information when making decisions in the practical planning process. Therefore, it is believed that if this approach is applied correctly and in combination with other elements, it can become a powerful and effective optimization tool for HSP problem.

High-resolution microwave diagnostics of architectural components by particle swarm optimization

Science.gov (United States)

Genovesi, Simone; Salerno, Emanuele; Monorchio, Agostino; Manara, Giuliano

2010-05-01

the discretization grid used by the forward solver. The algorithm we chose to optimize the objective is based on the particle swarm paradigm. Each feasible solution is coded as a location in a multidimensional space, explored by a number of "particles" each moving with a certain velocity, which is partly random and partly induced by the experience of both the particle itself and the "swarm" of all the other particles. In our case, the search is complicated by the mixed continuous-binary nature of our unknowns, but the swarm intelligence approach maintains the advantage of its intrinsic parallelism. The experimental results we obtained from both simulated and real measurements show that, for typical permittivity values and radiation wavelengths, the spatial resolution is highly improved by the line process. From real measurements in the range 1.7-2.6 GHz, we accurately reconstructed the permittivity values of our test phantom and located the discontinuities within the limits imposed by our discretization grid (with 1.5 mm cell thickness). At present, the applicability of our reconstruction method is still limited by the forward solver, which is based on a cascaded transmission-line model that assumes normal and plane-wave incidence. We are developing a new solver based on a closed-form Green's function in multilayered media, which should enable us to model appropriately both the microwave sensor and the illumination geometry, thus improving the accuracy of the computed reflection coefficients in the objective functional.

Fine-tuning of T-cell development by the CD3γ di-leucine-based TCR-sorting motif

DEFF Research Database (Denmark)

Lauritsen, Jens Peter Holst; Boding, Lasse; Buus, Terkild B

2015-01-01

The CD3γ di-leucine-based (diL) receptor-sorting motif plays a central role in TCR down-regulation and in clonal expansion of virus-specific T cells. However, the role of the CD3γ diL motif in T-cell development is not known. In this study, we show that protein kinase C-induced TCR down-regulatio......The CD3γ di-leucine-based (diL) receptor-sorting motif plays a central role in TCR down-regulation and in clonal expansion of virus-specific T cells. However, the role of the CD3γ diL motif in T-cell development is not known. In this study, we show that protein kinase C-induced TCR down...

Cell motility and antibiotic tolerance of bacterial swarms

Science.gov (United States)

Zuo, Wenlong

Many bacteria species can move across moist surfaces in a coordinated manner known as swarming. It is reported that swarm cells show higher tolerance to a wide variety of antibiotics than planktonic cells. We used the model bacterium E. coli to study how motility affects the antibiotic tolerance of swarm cells. Our results provide new insights for the control of pathogenic invasion via regulating cell motility. Mailing address: Room 306 Science Centre North Block, The Chinese University of Hong Kong, Shatin, N.T. Hong Kong SAR. Phone: +852-3943-6354. Fax: +852-2603-5204. E-mail: zwlong@live.com.

A Chaotic Particle Swarm Optimization-Based Heuristic for Market-Oriented Task-Level Scheduling in Cloud Workflow Systems.

Science.gov (United States)

Li, Xuejun; Xu, Jia; Yang, Yun

2015-01-01

Cloud workflow system is a kind of platform service based on cloud computing. It facilitates the automation of workflow applications. Between cloud workflow system and its counterparts, market-oriented business model is one of the most prominent factors. The optimization of task-level scheduling in cloud workflow system is a hot topic. As the scheduling is a NP problem, Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) have been proposed to optimize the cost. However, they have the characteristic of premature convergence in optimization process and therefore cannot effectively reduce the cost. To solve these problems, Chaotic Particle Swarm Optimization (CPSO) algorithm with chaotic sequence and adaptive inertia weight factor is applied to present the task-level scheduling. Chaotic sequence with high randomness improves the diversity of solutions, and its regularity assures a good global convergence. Adaptive inertia weight factor depends on the estimate value of cost. It makes the scheduling avoid premature convergence by properly balancing between global and local exploration. The experimental simulation shows that the cost obtained by our scheduling is always lower than the other two representative counterparts.

Discordant introgression in a rapidly expanding hybrid swarm

Science.gov (United States)

Ward, Jessica L.; Blum, Mike J.; Walters, David M.; Porter, Brady A.; Burkhead, Noel; Freeman, Byron

2012-01-01

The erosion of species boundaries can involve rapid evolutionary change. Consequently, many aspects of the process remain poorly understood, including the formation, expansion, and evolution of hybrid swarms. Biological invasions involving hybridization present exceptional opportunities to study the erosion of species boundaries because timelines of interactions and outcomes are frequently well known. Here, we examined clinal variation across codominant and maternally inherited genetic markers as well as phenotypic traits to characterize the expansion and evolution of a hybrid swarm between native Cyprinella venusta and invasive Cyprinella lutrensis minnows. Discordant introgression of phenotype, microsatellite multilocus genotype, and mtDNA haplotype indicates that the observable expansion of the C. venusta x C. lutrensis hybrid swarm is a false invasion front. Both parental and hybrid individuals closely resembling C. lutrensis are numerically dominant in the expansion wake, indicating that the non-native parental phenotype may be selectively favored. These findings show that cryptic introgression can extend beyond the phenotypic boundaries of hybrid swarms and that hybrid swarms likely expand more rapidly than can be documented from phenotypic variation alone. Similarly, dominance of a single parental phenotype following an introduction event may lead to instances of species erosion being mistaken for species displacement without hybridization.

Energy-Aware Multipath Routing Scheme Based on Particle Swarm Optimization in Mobile Ad Hoc Networks.

Science.gov (United States)

Robinson, Y Harold; Rajaram, M

2015-01-01

Mobile ad hoc network (MANET) is a collection of autonomous mobile nodes forming an ad hoc network without fixed infrastructure. Dynamic topology property of MANET may degrade the performance of the network. However, multipath selection is a great challenging task to improve the network lifetime. We proposed an energy-aware multipath routing scheme based on particle swarm optimization (EMPSO) that uses continuous time recurrent neural network (CTRNN) to solve optimization problems. CTRNN finds the optimal loop-free paths to solve link disjoint paths in a MANET. The CTRNN is used as an optimum path selection technique that produces a set of optimal paths between source and destination. In CTRNN, particle swarm optimization (PSO) method is primly used for training the RNN. The proposed scheme uses the reliability measures such as transmission cost, energy factor, and the optimal traffic ratio between source and destination to increase routing performance. In this scheme, optimal loop-free paths can be found using PSO to seek better link quality nodes in route discovery phase. PSO optimizes a problem by iteratively trying to get a better solution with regard to a measure of quality. The proposed scheme discovers multiple loop-free paths by using PSO technique.

Setting value optimization method in integration for relay protection based on improved quantum particle swarm optimization algorithm

Science.gov (United States)

Yang, Guo Sheng; Wang, Xiao Yang; Li, Xue Dong

2018-03-01

With the establishment of the integrated model of relay protection and the scale of the power system expanding, the global setting and optimization of relay protection is an extremely difficult task. This paper presents a kind of application in relay protection of global optimization improved particle swarm optimization algorithm and the inverse time current protection as an example, selecting reliability of the relay protection, selectivity, quick action and flexibility as the four requires to establish the optimization targets, and optimizing protection setting values of the whole system. Finally, in the case of actual power system, the optimized setting value results of the proposed method in this paper are compared with the particle swarm algorithm. The results show that the improved quantum particle swarm optimization algorithm has strong search ability, good robustness, and it is suitable for optimizing setting value in the relay protection of the whole power system.

Research on Multiaircraft Cooperative Suppression Interference Array Based on an Improved Multiobjective Particle Swarm Optimization Algorithm

Directory of Open Access Journals (Sweden)

Huan Zhang

2017-01-01

Full Text Available For the problem of multiaircraft cooperative suppression interference array (MACSIA against the enemy air defense radar network in electronic warfare mission planning, firstly, the concept of route planning security zone is proposed and the solution to get the minimum width of security zone based on mathematical morphology is put forward. Secondly, the minimum width of security zone and the sum of the distance between each jamming aircraft and the center of radar network are regarded as objective function, and the multiobjective optimization model of MACSIA is built, and then an improved multiobjective particle swarm optimization algorithm is used to solve the model. The decomposition mechanism is adopted and the proportional distribution is used to maintain diversity of the new found nondominated solutions. Finally, the Pareto optimal solutions are analyzed by simulation, and the optimal MACSIA schemes of each jamming aircraft suppression against the enemy air defense radar network are obtained and verify that the built multiobjective optimization model is corrected. It also shows that the improved multiobjective particle swarm optimization algorithm for solving the problem of MACSIA is feasible and effective.

Swarm analysis by using transport equations, 1

International Nuclear Information System (INIS)

Dote, Toshihiko; Shimada, Masatoshi

1980-01-01

By evolving Maxwell-Boltzmann transport equations, various quantities on swarm of charged particles have been analyzed. Although this treatment is properly general, and common transport equations for charged particles ought to be given, in particular, equations only for electrons were presented here. The relation between the random energy and the drift energy was first derived and the general expression of the electron velocity was deduced too. For a simple example, one dimensional steady-state electron swarm in a uniform medium was treated. Electron swarm characteristics numerically calculated in He, Ne or Ar exhibited some interesting properties, which were physically clearly elucidated. These results were also compared with several data already published. Agreements between them were qualitatively rather well in detailed structures. (author)

Spike sorting based upon machine learning algorithms (SOMA).

Science.gov (United States)

Horton, P M; Nicol, A U; Kendrick, K M; Feng, J F

2007-02-15

We have developed a spike sorting method, using a combination of various machine learning algorithms, to analyse electrophysiological data and automatically determine the number of sampled neurons from an individual electrode, and discriminate their activities. We discuss extensions to a standard unsupervised learning algorithm (Kohonen), as using a simple application of this technique would only identify a known number of clusters. Our extra techniques automatically identify the number of clusters within the dataset, and their sizes, thereby reducing the chance of misclassification. We also discuss a new pre-processing technique, which transforms the data into a higher dimensional feature space revealing separable clusters. Using principal component analysis (PCA) alone may not achieve this. Our new approach appends the features acquired using PCA with features describing the geometric shapes that constitute a spike waveform. To validate our new spike sorting approach, we have applied it to multi-electrode array datasets acquired from the rat olfactory bulb, and from the sheep infero-temporal cortex, and using simulated data. The SOMA sofware is available at http://www.sussex.ac.uk/Users/pmh20/spikes.

Discovery of Transition Rules for Cellular Automata Using Artificial Bee Colony and Particle Swarm Optimization Algorithms in Urban Growth Modeling

Directory of Open Access Journals (Sweden)

Fereydoun Naghibi

2016-12-01

Full Text Available This paper presents an advanced method in urban growth modeling to discover transition rules of cellular automata (CA using the artificial bee colony (ABC optimization algorithm. Also, comparisons between the simulation results of CA models optimized by the ABC algorithm and the particle swarm optimization algorithms (PSO as intelligent approaches were performed to evaluate the potential of the proposed methods. According to previous studies, swarm intelligence algorithms for solving optimization problems such as discovering transition rules of CA in land use change/urban growth modeling can produce reasonable results. Modeling of urban growth as a dynamic process is not straightforward because of the existence of nonlinearity and heterogeneity among effective involved variables which can cause a number of challenges for traditional CA. ABC algorithm, the new powerful swarm based optimization algorithms, can be used to capture optimized transition rules of CA. This paper has proposed a methodology based on remote sensing data for modeling urban growth with CA calibrated by the ABC algorithm. The performance of ABC-CA, PSO-CA, and CA-logistic models in land use change detection is tested for the city of Urmia, Iran, between 2004 and 2014. Validations of the models based on statistical measures such as overall accuracy, figure of merit, and total operating characteristic were made. We showed that the overall accuracy of the ABC-CA model was 89%, which was 1.5% and 6.2% higher than those of the PSO-CA and CA-logistic model, respectively. Moreover, the allocation disagreement (simulation error of the simulation results for the ABC-CA, PSO-CA, and CA-logistic models are 11%, 12.5%, and 17.2%, respectively. Finally, for all evaluation indices including running time, convergence capability, flexibility, statistical measurements, and the produced spatial patterns, the ABC-CA model performance showed relative improvement and therefore its superiority was

Climatology of the Occurrence Rate and Amplitudes of Local Time Distinguished Equatorial Plasma Depletions Observed by Swarm Satellite

Science.gov (United States)

Wan, Xin; Xiong, Chao; Rodriguez-Zuluaga, Juan; Kervalishvili, Guram N.; Stolle, Claudia; Wang, Hui

2018-04-01

In this study, we developed an autodetection technique for the equatorial plasma depletions (EPDs) and their occurrence and depletion amplitudes based on in situ electron density measurements gathered by Swarm A satellite. For the first time, comparisons are made among the detected EPDs and their amplitudes with the loss of Global Positioning System (GPS) signal of receivers onboard Swarm A, and the Swarm Level-2 product, Ionospheric Bubble Index (IBI). It has been found that the highest rate of EPD occurrence takes place generally between 2200 and 0000 magnetic local time (MLT), in agreement with the IBI. However, the largest amplitudes of EPD are detected earlier at about 1900-2100 MLT. This coincides with the moment of higher background electron density and the largest occurrence of GPS signal loss. From a longitudinal perspective, the higher depletion amplitude is always witnessed in spatial bins with higher background electron density. At most longitudes, the occurrence rate of postmidnight EPDs is reduced compared to premidnight ones; while more postmidnight EPDs are observed at African longitudes. CHAMP observations confirm this point regardless of high or low solar activity condition. Further by comparing with previous studies and the plasma vertical drift velocity from ROCSAT-1, we suggest that while the F region vertical plasma drift plays a key role in dominating the occurrence of EPDs during premidnight hours, the postmidnight EPDs are the combined results from the continuing of former EPDs and newborn EPDs, especially during June solstice. And these newborn EPDs during postmidnight hours seem to be less related to the plasma vertical drift.

Improved particle swarm optimization combined with chaos

International Nuclear Information System (INIS)

Liu Bo; Wang Ling; Jin Yihui; Tang Fang; Huang Dexian

2005-01-01

As a novel optimization technique, chaos has gained much attention and some applications during the past decade. For a given energy or cost function, by following chaotic ergodic orbits, a chaotic dynamic system may eventually reach the global optimum or its good approximation with high probability. To enhance the performance of particle swarm optimization (PSO), which is an evolutionary computation technique through individual improvement plus population cooperation and competition, hybrid particle swarm optimization algorithm is proposed by incorporating chaos. Firstly, adaptive inertia weight factor (AIWF) is introduced in PSO to efficiently balance the exploration and exploitation abilities. Secondly, PSO with AIWF and chaos are hybridized to form a chaotic PSO (CPSO), which reasonably combines the population-based evolutionary searching ability of PSO and chaotic searching behavior. Simulation results and comparisons with the standard PSO and several meta-heuristics show that the CPSO can effectively enhance the searching efficiency and greatly improve the searching quality

Enhancing Speech Recognition Using Improved Particle Swarm Optimization Based Hidden Markov Model

Directory of Open Access Journals (Sweden)

Lokesh Selvaraj

2014-01-01

Full Text Available Enhancing speech recognition is the primary intention of this work. In this paper a novel speech recognition method based on vector quantization and improved particle swarm optimization (IPSO is suggested. The suggested methodology contains four stages, namely, (i denoising, (ii feature mining (iii, vector quantization, and (iv IPSO based hidden Markov model (HMM technique (IP-HMM. At first, the speech signals are denoised using median filter. Next, characteristics such as peak, pitch spectrum, Mel frequency Cepstral coefficients (MFCC, mean, standard deviation, and minimum and maximum of the signal are extorted from the denoised signal. Following that, to accomplish the training process, the extracted characteristics are given to genetic algorithm based codebook generation in vector quantization. The initial populations are created by selecting random code vectors from the training set for the codebooks for the genetic algorithm process and IP-HMM helps in doing the recognition. At this point the creativeness will be done in terms of one of the genetic operation crossovers. The proposed speech recognition technique offers 97.14% accuracy.

Thermospheric neutral densities derived from Swarm accelerometer and GPS data

DEFF Research Database (Denmark)

Doornbos, Eelco; Encarnacao, Joao; van den IJss, Jose

Over the past years, a lot of effort has been put into characterising and correcting the various disturbance signals that were found in the accelerometer data provided by the Swarm satellites. This effort was first and foremost aimed at the Swarm C along-track axis data, which seems to be the least...... affected and most promising data for scientific use. The goal to make the Swarm C accelerometer along-track axis data ready for further processing into level 2 thermosphere density data has now been accomplished, with the help of information on the satellite motion from the GPS tracking as well...... approach, affects the possibility of determining densities from the accelerometer measurements of the Swarm A and B satellites. We also investigate the possibility of determining crosswind speeds from Swarm data.In the meantime, we have investigated the possibility of deriving thermosphere neutral density...

Precise Orbit Solution for Swarm Using Space-Borne GPS Data and Optimized Pseudo-Stochastic Pulses

Directory of Open Access Journals (Sweden)

Bingbing Zhang

2017-03-01

Full Text Available Swarm is a European Space Agency (ESA project that was launched on 22 November 2013, which consists of three Swarm satellites. Swarm precise orbits are essential to the success of the above project. This study investigates how well Swarm zero-differenced (ZD reduced-dynamic orbit solutions can be determined using space-borne GPS data and optimized pseudo-stochastic pulses under high ionospheric activity. We choose Swarm space-borne GPS data from 1–25 October 2014, and Swarm reduced-dynamic orbits are obtained. Orbit quality is assessed by GPS phase observation residuals and compared with Precise Science Orbits (PSOs released by ESA. Results show that pseudo-stochastic pulses with a time interval of 6 min and a priori standard deviation (STD of 10−2 mm/s in radial (R, along-track (T and cross-track (N directions are optimized to Swarm ZD reduced-dynamic precise orbit determination (POD. During high ionospheric activity, the mean Root Mean Square (RMS of Swarm GPS phase residuals is at 9–11 mm, Swarm orbit solutions are also compared with Swarm PSOs released by ESA and the accuracy of Swarm orbits can reach 2–4 cm in R, T and N directions. Independent Satellite Laser Ranging (SLR validation indicates that Swarm reduced-dynamic orbits have an accuracy of 2–4 cm. Swarm-B orbit quality is better than those of Swarm-A and Swarm-C. The Swarm orbits can be applied to the geomagnetic, geoelectric and gravity field recovery.

Development of a reactor thermalhydraulic experiment databank(SORTED1)

International Nuclear Information System (INIS)

Bang, Young Seck; Kim, Eun Kyoung; Kim, Hho Jung; Lee, Sang Yong

1994-01-01

The recent trend in thermalhydraulic safety analysis of nuclear power plant shows the best-estimate and probabilistic approaches, therefore, the verification of the best-estimate code based on the applicable experiment data has been required. The present study focused on developing a simple databank, SORTED1, to be effectively used for code verification. The development of SORTED1 includes a data collection from the various sources including ENCOUNTER, which is the reactor safety data bank of U.S. Nuclear Regulatory Commission, a reorganization of collected resources suitable for requirements of SORTED1 database management system (DBMS), and a development of a simple DBMS. The SORTED1 is designed in Unix environment with graphic user interface to improve a user convenience and has a capability to provide the test related information. The currently registered data in SORTED1 cover 759 thermalhydraulic tests including LOFT, Semiscale, etc

Vertical sorting and the morphodynamics of bed form-dominated rivers : a sorting evolution model

NARCIS (Netherlands)

Blom, Astrid; Ribberink, Jan S.; Parker, Gary

2008-01-01

Existing sediment continuity models for nonuniform sediment suffer from a number of shortcomings, as they fail to describe vertical sorting fluxes other than through net aggradation or degradation of the bed and are based on a discrete representation of the bed material interacting with the flow. We

Fractional order Darwinian particle swarm optimization applications and evaluation of an evolutionary algorithm

CERN Document Server

Couceiro, Micael

2015-01-01

This book examines the bottom-up applicability of swarm intelligence to solving multiple problems, such as curve fitting, image segmentation, and swarm robotics. It compares the capabilities of some of the better-known bio-inspired optimization approaches, especially Particle Swarm Optimization (PSO), Darwinian Particle Swarm Optimization (DPSO) and the recently proposed Fractional Order Darwinian Particle Swarm Optimization (FODPSO), and comprehensively discusses their advantages and disadvantages. Further, it demonstrates the superiority and key advantages of using the FODPSO algorithm, suc

A Hybrid Chaos-Particle Swarm Optimization Algorithm for the Vehicle Routing Problem with Time Window

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Qi Hu

2013-04-01

Full Text Available State-of-the-art heuristic algorithms to solve the vehicle routing problem with time windows (VRPTW usually present slow speeds during the early iterations and easily fall into local optimal solutions. Focusing on solving the above problems, this paper analyzes the particle encoding and decoding strategy of the particle swarm optimization algorithm, the construction of the vehicle route and the judgment of the local optimal solution. Based on these, a hybrid chaos-particle swarm optimization algorithm (HPSO is proposed to solve VRPTW. The chaos algorithm is employed to re-initialize the particle swarm. An efficient insertion heuristic algorithm is also proposed to build the valid vehicle route in the particle decoding process. A particle swarm premature convergence judgment mechanism is formulated and combined with the chaos algorithm and Gaussian mutation into HPSO when the particle swarm falls into the local convergence. Extensive experiments are carried out to test the parameter settings in the insertion heuristic algorithm and to evaluate that they are corresponding to the data’s real-distribution in the concrete problem. It is also revealed that the HPSO achieves a better performance than the other state-of-the-art algorithms on solving VRPTW.

Cold, muon-catalyzed fusion - just another swarm experiment?

International Nuclear Information System (INIS)

Robson, R.E.

1992-01-01

The paper briefly reviewed the muon-catalyzed fusion cycle and indicated how it may be likened to a swarm experiment. In particular, it has been pointed out that an external electric field can influence the properties of a muon swarm (and reactive derivatives), just as it can for ion and electron swarms. Since n 0 is typically around liquid hydrogen densities, very large fields, E≥10 9 V/m, would be required to achieve the desired outcome. This is presently achievable in small regions of intense laser focus, but it remains to be seen whether muon-catalyzed fusion experiments can actually be influenced in this way. 20 refs., 4 figs

Triaxial Accelerometer Error Coefficients Identification with a Novel Artificial Fish Swarm Algorithm

Directory of Open Access Journals (Sweden)

Yanbin Gao

2015-01-01

Full Text Available Artificial fish swarm algorithm (AFSA is one of the state-of-the-art swarm intelligence techniques, which is widely utilized for optimization purposes. Triaxial accelerometer error coefficients are relatively unstable with the environmental disturbances and aging of the instrument. Therefore, identifying triaxial accelerometer error coefficients accurately and being with lower costs are of great importance to improve the overall performance of triaxial accelerometer-based strapdown inertial navigation system (SINS. In this study, a novel artificial fish swarm algorithm (NAFSA that eliminated the demerits (lack of using artificial fishes’ previous experiences, lack of existing balance between exploration and exploitation, and high computational cost of AFSA is introduced at first. In NAFSA, functional behaviors and overall procedure of AFSA have been improved with some parameters variations. Second, a hybrid accelerometer error coefficients identification algorithm has been proposed based on NAFSA and Monte Carlo simulation (MCS approaches. This combination leads to maximum utilization of the involved approaches for triaxial accelerometer error coefficients identification. Furthermore, the NAFSA-identified coefficients are testified with 24-position verification experiment and triaxial accelerometer-based SINS navigation experiment. The priorities of MCS-NAFSA are compared with that of conventional calibration method and optimal AFSA. Finally, both experiments results demonstrate high efficiency of MCS-NAFSA on triaxial accelerometer error coefficients identification.

A Review of Particle Swarm Optimization

Science.gov (United States)

Jain, N. K.; Nangia, Uma; Jain, Jyoti

2018-03-01

This paper presents an overview of the research progress in Particle Swarm Optimization (PSO) during 1995-2017. Fifty two papers have been reviewed. They have been categorized into nine categories based on various aspects. This technique has attracted many researchers because of its simplicity which led to many improvements and modifications of the basic PSO. Some researchers carried out the hybridization of PSO with other evolutionary techniques. This paper discusses the progress of PSO, its improvements, modifications and applications.

Automatized Parameterization of DFTB Using Particle Swarm Optimization.

Science.gov (United States)

Chou, Chien-Pin; Nishimura, Yoshifumi; Fan, Chin-Chai; Mazur, Grzegorz; Irle, Stephan; Witek, Henryk A

2016-01-12

We present a novel density-functional tight-binding (DFTB) parametrization toolkit developed to optimize the parameters of various DFTB models in a fully automatized fashion. The main features of the algorithm, based on the particle swarm optimization technique, are discussed, and a number of initial pilot applications of the developed methodology to molecular and solid systems are presented.

Swarm, genetic and evolutionary programming algorithms applied to multiuser detection

Directory of Open Access Journals (Sweden)

Paul Jean Etienne Jeszensky

2005-02-01

Full Text Available In this paper, the particles swarm optimization technique, recently published in the literature, and applied to Direct Sequence/Code Division Multiple Access systems (DS/CDMA with multiuser detection (MuD is analyzed, evaluated and compared. The Swarm algorithm efficiency when applied to the DS-CDMA multiuser detection (Swarm-MuD is compared through the tradeoff performance versus computational complexity, being the complexity expressed in terms of the number of necessary operations in order to reach the performance obtained through the optimum detector or the Maximum Likelihood detector (ML. The comparison is accomplished among the genetic algorithm, evolutionary programming with cloning and Swarm algorithm under the same simulation basis. Additionally, it is proposed an heuristics-MuD complexity analysis through the number of computational operations. Finally, an analysis is carried out for the input parameters of the Swarm algorithm in the attempt to find the optimum parameters (or almost-optimum for the algorithm applied to the MuD problem.

In-Line Sorting of Harumanis Mango Based on External Quality Using Visible Imaging.

Science.gov (United States)

Ibrahim, Mohd Firdaus; Ahmad Sa'ad, Fathinul Syahir; Zakaria, Ammar; Md Shakaff, Ali Yeon

2016-10-27

The conventional method of grading Harumanis mango is time-consuming, costly and affected by human bias. In this research, an in-line system was developed to classify Harumanis mango using computer vision. The system was able to identify the irregularity of mango shape and its estimated mass. A group of images of mangoes of different size and shape was used as database set. Some important features such as length, height, centroid and parameter were extracted from each image. Fourier descriptor and size-shape parameters were used to describe the mango shape while the disk method was used to estimate the mass of the mango. Four features have been selected by stepwise discriminant analysis which was effective in sorting regular and misshapen mango. The volume from water displacement method was compared with the volume estimated by image processing using paired t -test and Bland-Altman method. The result between both measurements was not significantly different (P > 0.05). The average correct classification for shape classification was 98% for a training set composed of 180 mangoes. The data was validated with another testing set consist of 140 mangoes which have the success rate of 92%. The same set was used for evaluating the performance of mass estimation. The average success rate of the classification for grading based on its mass was 94%. The results indicate that the in-line sorting system using machine vision has a great potential in automatic fruit sorting according to its shape and mass.

Adaptive feature selection using v-shaped binary particle swarm optimization.

Science.gov (United States)

Teng, Xuyang; Dong, Hongbin; Zhou, Xiurong

2017-01-01

Feature selection is an important preprocessing method in machine learning and data mining. This process can be used not only to reduce the amount of data to be analyzed but also to build models with stronger interpretability based on fewer features. Traditional feature selection methods evaluate the dependency and redundancy of features separately, which leads to a lack of measurement of their combined effect. Moreover, a greedy search considers only the optimization of the current round and thus cannot be a global search. To evaluate the combined effect of different subsets in the entire feature space, an adaptive feature selection method based on V-shaped binary particle swarm optimization is proposed. In this method, the fitness function is constructed using the correlation information entropy. Feature subsets are regarded as individuals in a population, and the feature space is searched using V-shaped binary particle swarm optimization. The above procedure overcomes the hard constraint on the number of features, enables the combined evaluation of each subset as a whole, and improves the search ability of conventional binary particle swarm optimization. The proposed algorithm is an adaptive method with respect to the number of feature subsets. The experimental results show the advantages of optimizing the feature subsets using the V-shaped transfer function and confirm the effectiveness and efficiency of the feature subsets obtained under different classifiers.

Increased Tolerance to Heavy Metals Exhibited by Swarming Bacteria

Science.gov (United States)

Anyan, M.; Shrout, J. D.

2014-12-01

Pseudomonas aeruginosa is a ubiquitous, Gram-negative bacterium that utilizes several different modes of motility to colonize surfaces, including swarming, which is the coordinated movement of cells over surfaces in groups. Swarming facilitates surface colonization and biofilm development for P. aeruginosa, and it is known that swarming behavior is influenced by changes in nutrient composition and surface moisture. To understand the fate and cycling of heavy metals in the environment, it is important to understand the interaction and toxicity of these metals upon bacteria. While previous studies have shown surface-attached bacterial biofilms to be highly resistant to heavy metal toxicity, little is known about the influence of heavy metals upon surface motile bacteria and developing biofilms. Using a combination of laboratory assays we examined differences in bacterial behavior in response to two metals, Cd and Ni. We find that surface swarming bacteria are able to grow on 4x and 2.5x more Cd and Ni, respectively, than planktonic cells (i.e., test tube cultures). P. aeruginosa was able to swarm in the presence ≤0.051mM Ni and ≤0.045mM Cd. To investigate the bioavailability of metals to bacteria growing under our examined conditions, we separated cell and supernatant fractions of P. aeruginosa cultures, and used ICP-MS techniques to measure Cd and Ni sorption. A greater percentage of Cd than Ni was sorbed by both cells and supernatant (which contains rhamnolipid, a surfactant known to sorb some metals and improve swarming). While we show that cell products such as rhamnolipid bind heavy metals (as expected) and should limit metal bioavailability, our results suggest at least one additional mechanism (as yet undetermined) that promotes cell survival during swarming in the presence of these heavy metals.

Sensory coding of nest-site value in honeybee swarms.

Science.gov (United States)

Seeley, Thomas D; Visscher, P Kirk

2008-12-01

This study investigates the first stage of the decision-making process of a honeybee swarm as it chooses a nest site: how a scout bee codes the value of a potential nest site in the waggle dances she produces to represent this site. We presented honeybee swarms with a two-alternative choice between a high-value site and a medium-value site and recorded the behavior of individually identifiable scout bees as they reported on these two alternatives. We found that bees performed equally lengthy inspections at the two sites, but that, on the swarm cluster, they performed more dance circuits per bee for the high-value site. We also found that there was much individual-level noise in the coding of site value, but that there were clear population-level differences in total dance circuits produced for the two sites. The first bee to find a site had a high probability of reporting the site with a waggle dance, regardless of its value. This discoverer-should-dance phenomenon may help ensure that a swarm gives attention to all discovered sites. There was rapid decay in the dance response; the number of dance circuits produced by a bee after visiting a site decreased linearly over sequential visits, and eventually each bee ceased visiting her site. This decay, or ;leakage', in the accumulation of bees at a site improves a swarm's decision-making ability by helping a swarm avoid making fast-decision errors.

Spike sorting for polytrodes: a divide and conquer approach

Directory of Open Access Journals (Sweden)

Nicholas V. Swindale

2014-02-01

Full Text Available In order to determine patterns of neural activity, spike signals recorded by extracellular electrodes have to be clustered (sorted with the aim of ensuring that each cluster represents all the spikes generated by an individual neuron. Many methods for spike sorting have been proposed but few are easily applicable to recordings from polytrodes which may have 16 or more recording sites. As with tetrodes, these are spaced sufficiently closely that signals from single neurons will usually be recorded on several adjacent sites. Although this offers a better chance of distinguishing neurons with similarly shaped spikes, sorting is difficult in such cases because of the high dimensionality of the space in which the signals must be classified. This report details a method for spike sorting based on a divide and conquer approach. Clusters are initially formed by assigning each event to the channel on which it is largest. Each channel-based cluster is then sub-divided into as many distinct clusters as possible. These are then recombined on the basis of pairwise tests into a final set of clusters. Pairwise tests are also performed to establish how distinct each cluster is from the others. A modified gradient ascent clustering (GAC algorithm is used to do the clustering. The method can sort spikes with minimal user input in times comparable to real time for recordings lasting up to 45 minutes. Our results illustrate some of the difficulties inherent in spike sorting, including changes in spike shape over time. We show that some physiologically distinct units may have very similar spike shapes. We show that RMS measures of spike shape similarity are not sensitive enough to discriminate clusters that can otherwise be separated by principal components analysis. Hence spike sorting based on least-squares matching to templates may be unreliable. Our methods should be applicable to tetrodes and scaleable to larger multi-electrode arrays (MEAs.