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.

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

A Performance Study on Synchronous and Asynchronous Update Rules for A Plug-In Direct Particle Swarm Repetitive Controller

Directory of Open Access Journals (Sweden)

Ufnalski Bartlomiej

2014-12-01

Full Text Available In this paper two different update schemes for the recently developed plug-in direct particle swarm repetitive controller (PDPSRC are investigated and compared. The proposed approach employs the particle swarm optimizer (PSO to solve in on-line mode a dynamic optimization problem (DOP related to the control task in the constant-amplitude constant-frequency voltage-source inverter (CACF VSI with an LC output filter. The effectiveness of synchronous and asynchronous update rules, both commonly used in static optimization problems (SOPs, is assessed and compared in the case of PDPSRC. The performance of the controller, when synthesized using each of the update schemes, is studied numerically.

Transport of Particle Swarms Through Fractures

Science.gov (United States)

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

2011-12-01

The transport of engineered micro- and nano-scale particles through fractured rock is often assumed to occur as dispersions or emulsions. Another potential transport mechanism is the release of particle swarms from natural or industrial processes where small liquid drops, containing thousands to millions of colloidal-size particles, are released over time from seepage or leaks. Swarms have higher velocities than any individual colloid because the interactions among the particles maintain the cohesiveness of the swarm as it falls under gravity. Thus particle swarms give rise to the possibility that engineered particles may be transported farther and faster in fractures than predicted by traditional dispersion models. In this study, the effect of fractures on colloidal swarm cohesiveness and evolution was studied as a swarm falls under gravity and interacts with fracture walls. Transparent acrylic was used to fabricate synthetic fracture samples with either (1) a uniform aperture or (2) a converging aperture followed by a uniform aperture (funnel-shaped). The samples consisted of two blocks that measured 100 x 100 x 50 mm. The separation between these blocks determined the aperture (0.5 mm to 50 mm). During experiments, a fracture was fully submerged in water and swarms were released into it. The swarms consisted of dilute suspensions of either 25 micron soda-lime glass beads (2% by mass) or 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. In the uniform aperture fracture, the speed of the swarm prior to bifurcation increased with aperture up to a maximum at a fracture width of approximately 10 mm. For apertures greater than ~15 mm, the velocity was essentially constant with fracture width (but less than at 10 mm). This peak suggests that two competing mechanisms affect swarm velocity in fractures. The wall provides both drag, which

Quantum Behaved Particle Swarm Optimization Algorithm Based on Artificial Fish Swarm

OpenAIRE

Yumin, Dong; Li, Zhao

2014-01-01

Quantum behaved particle swarm algorithm is a new intelligent optimization algorithm; the algorithm has less parameters and is easily implemented. In view of the existing quantum behaved particle swarm optimization algorithm for the premature convergence problem, put forward a quantum particle swarm optimization algorithm based on artificial fish swarm. The new algorithm based on quantum behaved particle swarm algorithm, introducing the swarm and following activities, meanwhile using the a...

Moving without a purpose: an experimental study of swarm guidance in the Western honey bee, Apis mellifera.

Science.gov (United States)

Makinson, James C; Beekman, Madeleine

2014-06-01

During reproductive swarming, honey bee scouts perform two very important functions. Firstly, they find new nesting locations and return to the swarm cluster to communicate their discoveries. Secondly, once the swarm is ready to depart, informed scout bees act as guides, leading the swarm to its final destination. We have previously hypothesised that the two processes, selecting a new nest site and swarm guidance, are tightly linked in honey bees. When swarms can be laissez faire about where they nest, reaching directional consensus prior to lift off seems unnecessary. If, in contrast, it is essential that the swarm reaches a precise location, either directional consensus must be near unanimous prior to swarm departure or only a select subgroup of the scouts guide the swarm. Here, we tested experimentally whether directional consensus is necessary for the successful guidance of swarms of the Western honey bee Apis mellifera by forcing swarms into the air prior to the completion of the decision-making process. Our results show that swarms were unable to guide themselves prior to the swarm reaching the pre-flight buzzing phase of the decision-making process, even when directional consensus was high. We therefore suggest that not all scouts involved in the decision-making process attempt to guide the swarm. © 2014. Published by The Company of Biologists Ltd.

Heterogeneous architecture to process swarm optimization algorithms

Directory of Open Access Journals (Sweden)

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.

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.

Origin of meteor swarms of the Arietid and Geminid types

International Nuclear Information System (INIS)

Lebedinets, V.N.

1985-01-01

The author proposes a physical mechanism for the formation of meteor swarms on orbits of small size and very small perihelion distance, similar to the orbits of Arietid and Geminid meteor swarms, which are rarely encountered among the larger bodies of the solar system, and he justifies the mechanism mathematically. He shows that comets can transfer to such orbits from orbits of large size during evaporation of their ice nuclei under the action of reactive drag

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)

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.

Task partitioning in a robot swarm: object retrieval as a sequence of subtasks with direct object transfer.

Science.gov (United States)

Pini, Giovanni; Brutschy, Arne; Scheidler, Alexander; Dorigo, Marco; Birattari, Mauro

2014-01-01

We study task partitioning in the context of swarm robotics. Task partitioning is the decomposition of a task into subtasks that can be tackled by different workers. We focus on the case in which a task is partitioned into a sequence of subtasks that must be executed in a certain order. This implies that the subtasks must interface with each other, and that the output of a subtask is used as input for the subtask that follows. A distinction can be made between task partitioning with direct transfer and with indirect transfer. We focus our study on the first case: The output of a subtask is directly transferred from an individual working on that subtask to an individual working on the subtask that follows. As a test bed for our study, we use a swarm of robots performing foraging. The robots have to harvest objects from a source, situated in an unknown location, and transport them to a home location. When a robot finds the source, it memorizes its position and uses dead reckoning to return there. Dead reckoning is appealing in robotics, since it is a cheap localization method and it does not require any additional external infrastructure. However, dead reckoning leads to errors that grow in time if not corrected periodically. We compare a foraging strategy that does not make use of task partitioning with one that does. We show that cooperation through task partitioning can be used to limit the effect of dead reckoning errors. This results in improved capability of locating the object source and in increased performance of the swarm. We use the implemented system as a test bed to study benefits and costs of task partitioning with direct transfer. We implement the system with real robots, demonstrating the feasibility of our approach in a foraging scenario.

The Bi-Directional Prediction of Carbon Fiber Production Using a Combination of Improved Particle Swarm Optimization and Support Vector Machine.

Science.gov (United States)

Xiao, Chuncai; Hao, Kuangrong; Ding, Yongsheng

2014-12-30

This paper creates a bi-directional prediction model to predict the performance of carbon fiber and the productive parameters based on a support vector machine (SVM) and improved particle swarm optimization (IPSO) algorithm (SVM-IPSO). In the SVM, it is crucial to select the parameters that have an important impact on the performance of prediction. The IPSO is proposed to optimize them, and then the SVM-IPSO model is applied to the bi-directional prediction of carbon fiber production. The predictive accuracy of SVM is mainly dependent on its parameters, and IPSO is thus exploited to seek the optimal parameters for SVM in order to improve its prediction capability. Inspired by a cell communication mechanism, we propose IPSO by incorporating information of the global best solution into the search strategy to improve exploitation, and we employ IPSO to establish the bi-directional prediction model: in the direction of the forward prediction, we consider productive parameters as input and property indexes as output; in the direction of the backward prediction, we consider property indexes as input and productive parameters as output, and in this case, the model becomes a scheme design for novel style carbon fibers. The results from a set of the experimental data show that the proposed model can outperform the radial basis function neural network (RNN), the basic particle swarm optimization (PSO) method and the hybrid approach of genetic algorithm and improved particle swarm optimization (GA-IPSO) method in most of the experiments. In other words, simulation results demonstrate the effectiveness and advantages of the SVM-IPSO model in dealing with the problem of forecasting.

The Bi-Directional Prediction of Carbon Fiber Production Using a Combination of Improved Particle Swarm Optimization and Support Vector Machine

Directory of Open Access Journals (Sweden)

Chuncai Xiao

2014-12-01

Full Text Available This paper creates a bi-directional prediction model to predict the performance of carbon fiber and the productive parameters based on a support vector machine (SVM and improved particle swarm optimization (IPSO algorithm (SVM-IPSO. In the SVM, it is crucial to select the parameters that have an important impact on the performance of prediction. The IPSO is proposed to optimize them, and then the SVM-IPSO model is applied to the bi-directional prediction of carbon fiber production. The predictive accuracy of SVM is mainly dependent on its parameters, and IPSO is thus exploited to seek the optimal parameters for SVM in order to improve its prediction capability. Inspired by a cell communication mechanism, we propose IPSO by incorporating information of the global best solution into the search strategy to improve exploitation, and we employ IPSO to establish the bi-directional prediction model: in the direction of the forward prediction, we consider productive parameters as input and property indexes as output; in the direction of the backward prediction, we consider property indexes as input and productive parameters as output, and in this case, the model becomes a scheme design for novel style carbon fibers. The results from a set of the experimental data show that the proposed model can outperform the radial basis function neural network (RNN, the basic particle swarm optimization (PSO method and the hybrid approach of genetic algorithm and improved particle swarm optimization (GA-IPSO method in most of the experiments. In other words, simulation results demonstrate the effectiveness and advantages of the SVM-IPSO model in dealing with the problem of forecasting.

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)

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 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.

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.

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.

Symbiosis-Based Alternative Learning Multi-Swarm Particle Swarm Optimization.

Science.gov (United States)

Niu, Ben; Huang, Huali; Tan, Lijing; Duan, Qiqi

2017-01-01

Inspired by the ideas from the mutual cooperation of symbiosis in natural ecosystem, this paper proposes a new variant of PSO, named Symbiosis-based Alternative Learning Multi-swarm Particle Swarm Optimization (SALMPSO). A learning probability to select one exemplar out of the center positions, the local best position, and the historical best position including the experience of internal and external multiple swarms, is used to keep the diversity of the population. Two different levels of social interaction within and between multiple swarms are proposed. In the search process, particles not only exchange social experience with others that are from their own sub-swarms, but also are influenced by the experience of particles from other fellow sub-swarms. According to the different exemplars and learning strategy, this model is instantiated as four variants of SALMPSO and a set of 15 test functions are conducted to compare with some variants of PSO including 10, 30 and 50 dimensions, respectively. Experimental results demonstrate that the alternative learning strategy in each SALMPSO version can exhibit better performance in terms of the convergence speed and optimal values on most multimodal functions in our simulation.

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.

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.

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.

Modeling dynamic swarms

KAUST Repository

Ghanem, Bernard

2013-01-01

This paper proposes the problem of modeling video sequences of dynamic swarms (DSs). We define a DS as a large layout of stochastically repetitive spatial configurations of dynamic objects (swarm elements) whose motions exhibit local spatiotemporal interdependency and stationarity, i.e., the motions are similar in any small spatiotemporal neighborhood. Examples of DS abound in nature, e.g., herds of animals and flocks of birds. To capture the local spatiotemporal properties of the DS, we present a probabilistic model that learns both the spatial layout of swarm elements (based on low-level image segmentation) and their joint dynamics that are modeled as linear transformations. To this end, a spatiotemporal neighborhood is associated with each swarm element, in which local stationarity is enforced both spatially and temporally. We assume that the prior on the swarm dynamics is distributed according to an MRF in both space and time. Embedding this model in a MAP framework, we iterate between learning the spatial layout of the swarm and its dynamics. We learn the swarm transformations using ICM, which iterates between estimating these transformations and updating their distribution in the spatiotemporal neighborhoods. We demonstrate the validity of our method by conducting experiments on real and synthetic video sequences. Real sequences of birds, geese, robot swarms, and pedestrians evaluate the applicability of our model to real world data. © 2012 Elsevier Inc. All rights reserved.

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.

Supervised self-organization of homogeneous swarms using ergodic projections of Markov chains.

Science.gov (United States)

Chattopadhyay, Ishanu; Ray, Asok

2009-12-01

This paper formulates a self-organization algorithm to address the problem of global behavior supervision in engineered swarms of arbitrarily large population sizes. The swarms considered in this paper are assumed to be homogeneous collections of independent identical finite-state agents, each of which is modeled by an irreducible finite Markov chain. The proposed algorithm computes the necessary perturbations in the local agents' behavior, which guarantees convergence to the desired observed state of the swarm. The ergodicity property of the swarm, which is induced as a result of the irreducibility of the agent models, implies that while the local behavior of the agents converges to the desired behavior only in the time average, the overall swarm behavior converges to the specification and stays there at all times. A simulation example illustrates the underlying concept.

A Two Teraflop Swarm

Directory of Open Access Journals (Sweden)

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.

The West Bohemian 2008-earthquake swarm: When, where, what size and data

Czech Academy of Sciences Publication Activity Database

Horálek, Josef; Fischer, Tomáš; Boušková, Alena; Michálek, Jan; Hrubcová, Pavla

2009-01-01

Roč. 53, č. 3 (2009), s. 351-358 ISSN 0039-3169 Institutional research plan: CEZ:AV0Z30120515 Keywords : West Bohemia/Vogtland * earthquake swarm * WEBNET Subject RIV: DC - Siesmology, Volcanology, Earth Structure Impact factor: 1.000, year: 2009

The Feasibility of Radio Direction Finding for Swarm Localization

Science.gov (United States)

2017-09-01

small swarm agents do not exist. This has led ARL to begin development of a custom RDF system using small, standalone, software-defined radios (SDRs...First, basic RDF theory is presented. Next, a laboratory experiment to evaluate RDF using a SDR is developed. Finally, experimental data are presented... relationships between many agents to achieve accurate relative attitude and position information.1 This is particularly important in GPS-denied environments

Cell-Division Behavior in a Heterogeneous Swarm Environment.

Science.gov (United States)

Erskine, Adam; Herrmann, J Michael

2015-01-01

We present a system of virtual particles that interact using simple kinetic rules. It is known that heterogeneous mixtures of particles can produce particularly interesting behaviors. Here we present a two-species three-dimensional swarm in which a behavior emerges that resembles cell division. We show that the dividing behavior exists across a narrow but finite band of parameters and for a wide range of population sizes. When executed in a two-dimensional environment the swarm's characteristics and dynamism manifest differently. In further experiments we show that repeated divisions can occur if the system is extended by a biased equilibrium process to control the split of populations. We propose that this repeated division behavior provides a simple model for cell-division mechanisms and is of interest for the formation of morphological structure and to swarm robotics.

Particle Swarms in Fractures: Open Versus Partially Closed Systems

Science.gov (United States)

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

2014-12-01

In the field, fractures may be isolated or connected to fluid reservoirs anywhere along the perimeter of a fracture. These boundaries affect fluid circulation, flow paths and communication with external reservoirs. The transport of drop like collections of colloidal-sized particles (particle swarms) in open and partially closed systems was studied. A uniform aperture synthetic fracture was constructed using two blocks (100 x 100 x 50 mm) of transparent acrylic placed parallel to each other. The fracture was fully submerged a tank filled with 100cSt silicone oil. Fracture apertures were varied from 5-80 mm. Partially closed systems were created by sealing the sides of the fracture with plastic film. The four boundary conditions study were: (Case 1) open, (Case 2) closed on the sides, (Case 3) closed on the bottom, and (Case 4) closed on both the sides and bottom of the fracture. A 15 μL dilute suspension of soda-lime glass particles in oil (2% by mass) were released into the fracture. Particle swarms were illuminated using a green (525 nm) LED array and imaged with a CCD camera. The presence of the additional boundaries modified the speed of the particle swarms (see figure). In Case 1, enhanced swarm transport was observed for a range of apertures, traveling faster than either very small or very large apertures. In Case 2, swarm velocities were enhanced over a larger range of fracture apertures than in any of the other cases. Case 3 shifted the enhanced transport regime to lower apertures and also reduced swarm speed when compared to Case 2. Finally, Case 4 eliminated the enhanced transport regime entirely. Communication between the fluid in the fracture and an external fluid reservoir resulted in enhanced swarm transport in Cases 1-3. The non-rigid nature of a swarm enables drag from the fracture walls to modify the swarm geometry. The particles composing a swarm reorganize in response to the fracture, elongating the swarm and maintaining its density. Unlike a

A Local and Global Search Combined Particle Swarm Optimization Algorithm and Its Convergence Analysis

Directory of Open Access Journals (Sweden)

Weitian Lin

2014-01-01

Full Text Available Particle swarm optimization algorithm (PSOA is an advantage optimization tool. However, it has a tendency to get stuck in a near optimal solution especially for middle and large size problems and it is difficult to improve solution accuracy by fine-tuning parameters. According to the insufficiency, this paper researches the local and global search combine particle swarm algorithm (LGSCPSOA, and its convergence and obtains its convergence qualification. At the same time, it is tested with a set of 8 benchmark continuous functions and compared their optimization results with original particle swarm algorithm (OPSOA. Experimental results indicate that the LGSCPSOA improves the search performance especially on the middle and large size benchmark functions significantly.

Capture of Planetesimals into a Circumterrestrial Swarm

Science.gov (United States)

Weidenschilling, S. J.

1985-01-01

The lunar origin model considered in this report involves processing of protolunar material through a circumterrestrial swarm of particles. Once such a swarm has formed, it can gain mass by capturing infalling planetesimals and ejecta from giant impacts on the Earth, although the angular momentum supply from these sources remains a problem. The first stage of formation of a geocentric swarm by capture of planetesimals from initially heliocentric orbits is examined. The only plausible capture mechanism that is not dependent on very low approach velocities is the mutual collision of planetesimals passing within Earth's sphere of influence. The dissipation of energy in inelastic collisions or accretion events changes the value of the Jacobi parameter, allowing capture into bound geocentric orbits. This capture scenario was tested directly by many body numerical integration of planetesimal orbits in near Earth space.

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.

Modelling Oil-Spill Detection with Swarm Drones

Directory of Open Access Journals (Sweden)

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.

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.

Swarm-based medicine.

Science.gov (United States)

Putora, Paul Martin; Oldenburg, Jan

2013-09-19

Occasionally, medical decisions have to be taken in the absence of evidence-based guidelines. Other sources can be drawn upon to fill in the gaps, including experience and intuition. Authorities or experts, with their knowledge and experience, may provide further input--known as "eminence-based medicine". Due to the Internet and digital media, interactions among physicians now take place at a higher rate than ever before. With the rising number of interconnected individuals and their communication capabilities, the medical community is obtaining the properties of a swarm. The way individual physicians act depends on other physicians; medical societies act based on their members. Swarm behavior might facilitate the generation and distribution of knowledge as an unconscious process. As such, "swarm-based medicine" may add a further source of information to the classical approaches of evidence- and eminence-based medicine. How to integrate swarm-based medicine into practice is left to the individual physician, but even this decision will be influenced by the swarm.

Formation control of robotic swarm using bounded artificial forces.

Science.gov (United States)

Qin, Long; Zha, Yabing; Yin, Quanjun; Peng, Yong

2013-01-01

Formation control of multirobot systems has drawn significant attention in the recent years. This paper presents a potential field control algorithm, navigating a swarm of robots into a predefined 2D shape while avoiding intermember collisions. The algorithm applies in both stationary and moving targets formation. We define the bounded artificial forces in the form of exponential functions, so that the behavior of the swarm drove by the forces can be adjusted via selecting proper control parameters. The theoretical analysis of the swarm behavior proves the stability and convergence properties of the algorithm. We further make certain modifications upon the forces to improve the robustness of the swarm behavior in the presence of realistic implementation considerations. The considerations include obstacle avoidance, local minima, and deformation of the shape. Finally, detailed simulation results validate the efficiency of the proposed algorithm, and the direction of possible futrue work is discussed in the conclusions.

Application of Dynamic Mutated Particle Swarm Optimization Algorithm to Design Water Distribution Networks

Directory of Open Access Journals (Sweden)

Kazem Mohammadi- Aghdam

2015-10-01

Full Text Available This paper proposes the application of a new version of the heuristic particle swarm optimization (PSO method for designing water distribution networks (WDNs. The optimization problem of looped water distribution networks is recognized as an NP-hard combinatorial problem which cannot be easily solved using traditional mathematical optimization techniques. In this paper, the concept of dynamic swarm size is considered in an attempt to increase the convergence speed of the original PSO algorithm. In this strategy, the size of the swarm is dynamically changed according to the iteration number of the algorithm. Furthermore, a novel mutation approach is introduced to increase the diversification property of the PSO and to help the algorithm to avoid trapping in local optima. The new version of the PSO algorithm is called dynamic mutated particle swarm optimization (DMPSO. The proposed DMPSO is then applied to solve WDN design problems. Finally, two illustrative examples are used for comparison to verify the efficiency of the proposed DMPSO as compared to other intelligent algorithms.

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...

Formation Control of Robotic Swarm Using Bounded Artificial Forces

Science.gov (United States)

Zha, Yabing; Peng, Yong

2013-01-01

Formation control of multirobot systems has drawn significant attention in the recent years. This paper presents a potential field control algorithm, navigating a swarm of robots into a predefined 2D shape while avoiding intermember collisions. The algorithm applies in both stationary and moving targets formation. We define the bounded artificial forces in the form of exponential functions, so that the behavior of the swarm drove by the forces can be adjusted via selecting proper control parameters. The theoretical analysis of the swarm behavior proves the stability and convergence properties of the algorithm. We further make certain modifications upon the forces to improve the robustness of the swarm behavior in the presence of realistic implementation considerations. The considerations include obstacle avoidance, local minima, and deformation of the shape. Finally, detailed simulation results validate the efficiency of the proposed algorithm, and the direction of possible futrue work is discussed in the conclusions. PMID:24453809

Formation Control of Robotic Swarm Using Bounded Artificial Forces

Directory of Open Access Journals (Sweden)

Long Qin

2013-01-01

Full Text Available Formation control of multirobot systems has drawn significant attention in the recent years. This paper presents a potential field control algorithm, navigating a swarm of robots into a predefined 2D shape while avoiding intermember collisions. The algorithm applies in both stationary and moving targets formation. We define the bounded artificial forces in the form of exponential functions, so that the behavior of the swarm drove by the forces can be adjusted via selecting proper control parameters. The theoretical analysis of the swarm behavior proves the stability and convergence properties of the algorithm. We further make certain modifications upon the forces to improve the robustness of the swarm behavior in the presence of realistic implementation considerations. The considerations include obstacle avoidance, local minima, and deformation of the shape. Finally, detailed simulation results validate the efficiency of the proposed algorithm, and the direction of possible futrue work is discussed in the conclusions.

Modeling dynamic swarms

KAUST Repository

Ghanem, Bernard; Ahuja, Narendra

2013-01-01

This paper proposes the problem of modeling video sequences of dynamic swarms (DSs). We define a DS as a large layout of stochastically repetitive spatial configurations of dynamic objects (swarm elements) whose motions exhibit local spatiotemporal

Steering Micro-Robotic Swarm by Dynamic Actuating Fields

NARCIS (Netherlands)

Chao, Q.; Yu, J; Dai, C.; Xu, T; Zhang, L.; Wang, C.C.; Jin, X.

2016-01-01

We present a general solution for steering microrobotic
swarm by dynamic actuating fields. In our approach, the
motion of micro-robots is controlled by changing the actuating
direction of a field applied to them. The time-series sequence
of actuating field’s directions can be

Water reservoir maintained by cell growth fuels the spreading of a bacterial swarm.

Science.gov (United States)

Wu, Yilin; Berg, Howard C

2012-03-13

Flagellated bacteria can swim across moist surfaces within a thin layer of fluid, a means for surface colonization known as swarming. This fluid spreads with the swarm, but how it does so is unclear. We used micron-sized air bubbles to study the motion of this fluid within swarms of Escherichia coli. The bubbles moved diffusively, with drift. Bubbles starting at the swarm edge drifted inward for the first 5 s and then moved outward. Bubbles starting 30 μm from the swarm edge moved inward for the first 20 s, wandered around in place for the next 40 s, and then moved outward. Bubbles starting at 200 or 300 μm from the edge moved outward or wandered around in place, respectively. So the general trend was inward near the outer edge of the swarm and outward farther inside, with flows converging on a region about 100 μm from the swarm edge. We measured cellular metabolic activities with cells expressing a short-lived GFP and cell densities with cells labeled with a membrane fluorescent dye. The fluorescence plots were similar, with peaks about 80 μm from the swarm edge and slopes that mimicked the particle drift rates. These plots suggest that net fluid flow is driven by cell growth. Fluid depth is largest in the multilayered region between approximately 30 and 200 μm from the swarm edge, where fluid agitation is more vigorous. This water reservoir travels with the swarm, fueling its spreading. Intercellular communication is not required; cells need only grow.

Intelligent sizing of a series hybrid electric power-train system based on Chaos-enhanced accelerated particle swarm optimization

International Nuclear Information System (INIS)

Zhou, Quan; Zhang, Wei; Cash, Scott; Olatunbosun, Oluremi; Xu, Hongming; Lu, Guoxiang

2017-01-01

Highlights: • A novel algorithm for hybrid electric powertrain intelligent sizing is introduced and applied. • The proposed CAPSO algorithm is capable of finding the real optimal result with much higher reputation. • Logistic mapping is the most effective strategy to build CAPSO. • The CAPSO gave more reliable results and increased the efficiency by 1.71%. - Abstract: This paper firstly proposed a novel HEV sizing method using the Chaos-enhanced Accelerated Particle Swarm Optimization (CAPSO) algorithm and secondly provided a demonstration on sizing a series hybrid electric powertrain with investigations of chaotic mapping strategies to achieve the global optimization. In this paper, the intelligent sizing of a series hybrid electric powertrain is formulated as an integer multi-objective optimization issue by modelling the powertrain system. The intelligent sizing mechanism based on APSO is then introduced, and 4 types of the most effective chaotic mapping strategy are investigated to upgrade the standard APSO into CAPSO algorithms for intelligent sizing. The evaluation of the intelligent sizing systems based on standard APSO and CAPSOs are then performed. The Monte Carlo analysis and reputation evaluation indicate that the CAPSO outperforms the standard APSO for finding the real optimal sizing result with much higher reputation, and CAPSO with logistic mapping strategy is the most effective algorithm for HEV powertrain components intelligent sizing. In addition, this paper also performs the sensitivity analysis and Pareto analysis to help engineers customize the intelligent sizing system.

A New Hybrid Nelder-Mead Particle Swarm Optimization for Coordination Optimization of Directional Overcurrent Relays

Directory of Open Access Journals (Sweden)

An Liu

2012-01-01

Full Text Available Coordination optimization of directional overcurrent relays (DOCRs is an important part of an efficient distribution system. This optimization problem involves obtaining the time dial setting (TDS and pickup current (Ip values of each DOCR. The optimal results should have the shortest primary relay operating time for all fault lines. Recently, the particle swarm optimization (PSO algorithm has been considered an effective tool for linear/nonlinear optimization problems with application in the protection and coordination of power systems. With a limited runtime period, the conventional PSO considers the optimal solution as the final solution, and an early convergence of PSO results in decreased overall performance and an increase in the risk of mistaking local optima for global optima. Therefore, this study proposes a new hybrid Nelder-Mead simplex search method and particle swarm optimization (proposed NM-PSO algorithm to solve the DOCR coordination optimization problem. PSO is the main optimizer, and the Nelder-Mead simplex search method is used to improve the efficiency of PSO due to its potential for rapid convergence. To validate the proposal, this study compared the performance of the proposed algorithm with that of PSO and original NM-PSO. The findings demonstrate the outstanding performance of the proposed NM-PSO in terms of computation speed, rate of convergence, and feasibility.

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

Multiscale Characterization of Bacterial Swarming Illuminates Principles Governing Directed Surface Motility

Science.gov (United States)

Strickland, Ben; Hoeger, Kentaro; Ursell, Tristan

In many systems, individual characteristics interact, leading to the spontaneous emergence of order and complexity. In biological settings like microbes, such collective behaviors can imbue a variety of benefits to constituent individuals, including increased spatial range, improved access to nutrients, and enhanced resistance to antibiotic threats. To untangle the biophysical underpinnings of collective motility, we use passive tracers and a curated genetic library of Bacillus subtilis, including motile, non-motile, biofilm-deficient, and non-chemotactic mutants. We characterize and connect individual behavior on the microscopic scale to macroscopic colony morphology and motility of dendritic swarming. We analyze the persistence and dynamics of coordinated movement on length scales up to 4 orders of magnitude larger than that of individual cells, revealing rapid and directed responses of microbial groups to external stimuli, such as avoidance dynamics across chemical gradients. Our observations uncover the biophysical interplay between individual motility, surface wetness, phenotypic diversity, and external physical forces that robustly precipitate coordinated group behavior in microbes, and suggest general principles that govern the transition from individual to group behavior.

Binary Cockroach Swarm Optimization for Combinatorial Optimization Problem

Directory of Open Access Journals (Sweden)

Ibidun Christiana Obagbuwa

2016-09-01

Full Text Available The Cockroach Swarm Optimization (CSO algorithm is inspired by cockroach social behavior. It is a simple and efficient meta-heuristic algorithm and has been applied to solve global optimization problems successfully. The original CSO algorithm and its variants operate mainly in continuous search space and cannot solve binary-coded optimization problems directly. Many optimization problems have their decision variables in binary. Binary Cockroach Swarm Optimization (BCSO is proposed in this paper to tackle such problems and was evaluated on the popular Traveling Salesman Problem (TSP, which is considered to be an NP-hard Combinatorial Optimization Problem (COP. A transfer function was employed to map a continuous search space CSO to binary search space. The performance of the proposed algorithm was tested firstly on benchmark functions through simulation studies and compared with the performance of existing binary particle swarm optimization and continuous space versions of CSO. The proposed BCSO was adapted to TSP and applied to a set of benchmark instances of symmetric TSP from the TSP library. The results of the proposed Binary Cockroach Swarm Optimization (BCSO algorithm on TSP were compared to other meta-heuristic algorithms.

Diffusion tensor in electron swarm transport

International Nuclear Information System (INIS)

Makabe, T.; Mori, T.

1983-01-01

Expression for the diffusion tensor of the electron (or light ion) swarm is presented from the higher-order expansion of the velocity distribution in the Boltzmann equation in hydrodynamic stage. Derived diffusion coefficients for the transverse and longitudinal directions include the additional terms representative of the curvature effect under the action of an electric field with the usual-two-term expressions. Numerical analysis is given for the electron swarm in model gases having the momentum transfer cross section Qsub(m)(epsilon)=Q 0 epsilon sup(beta) (β=0, 1/2, 1) using the present theory. As the result, appreciable degree of discrepancy appears between the transverse diffusion coefficient defined here and the conventional expression with increasing of β in Qsub(m). (Author)

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

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...

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

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...

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.

Swarm v2: highly-scalable and high-resolution amplicon clustering.

Science.gov (United States)

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

2015-01-01

Previously we presented Swarm v1, a novel and open source amplicon clustering program that produced fine-scale molecular operational taxonomic units (OTUs), free of arbitrary global clustering thresholds and input-order dependency. Swarm v1 worked with an initial phase that used iterative single-linkage with a local clustering threshold (d), followed by a phase that used the internal abundance structures of clusters to break chained OTUs. Here we present Swarm v2, which has two important novel features: (1) a new algorithm for d = 1 that allows the computation time of the program to scale linearly with increasing amounts of data; and (2) the new fastidious option that reduces under-grouping by grafting low abundant OTUs (e.g., singletons and doubletons) onto larger ones. Swarm v2 also directly integrates the clustering and breaking phases, dereplicates sequencing reads with d = 0, outputs OTU representatives in fasta format, and plots individual OTUs as two-dimensional networks.

Swarm v2: highly-scalable and high-resolution amplicon clustering

Directory of Open Access Journals (Sweden)

Frédéric Mahé

2015-12-01

Full Text Available Previously we presented Swarm v1, a novel and open source amplicon clustering program that produced fine-scale molecular operational taxonomic units (OTUs, free of arbitrary global clustering thresholds and input-order dependency. Swarm v1 worked with an initial phase that used iterative single-linkage with a local clustering threshold (d, followed by a phase that used the internal abundance structures of clusters to break chained OTUs. Here we present Swarm v2, which has two important novel features: (1 a new algorithm for d = 1 that allows the computation time of the program to scale linearly with increasing amounts of data; and (2 the new fastidious option that reduces under-grouping by grafting low abundant OTUs (e.g., singletons and doubletons onto larger ones. Swarm v2 also directly integrates the clustering and breaking phases, dereplicates sequencing reads with d = 0, outputs OTU representatives in fasta format, and plots individual OTUs as two-dimensional networks.

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.

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

Imaging the onset kinetics of the swarming transition using light-controlled bacteria

Science.gov (United States)

Peng, Yi; Tai, Yishu; Zhang, Kechun; Cheng, Xiang

Active fluids are a novel class of nonequilibrium soft materials, which are composed of a large number of self-propelled particles. These particles collectively form coherent structures at high densities, as illustrated vividly by the striking patterns of flocking birds, schooling fishes and swarming bacteria. Although the disorder-swarming transition of active fluids has been extensively studied, its very nature is still under heated debate. Here, using an engineered E. coli strain, whose locomotion can be reversibly controlled by light, we experimentally study the onset of the swarming transition of active fluids and explore its kinetic pathway. Particularly, we trigger bacterial swarming using a blue light and image the emergence of the collective structure in concentrated bacterial suspensions. We find a discontinuous jump in the order parameter of the transition and observe a hysteresis in the formation of swarming, which indicate the discontinuous nature. We further investigate the microscopic dynamics in the context of nucleation-and-growth processes and measure the incubation time and the size distribution of nuclei. Our study sheds light on the phase transition of active fluids and the emergent properties of many-body nonequilibrium systems.

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.)

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.

Reversals and collisions optimize protein exchange in bacterial swarms

Energy Technology Data Exchange (ETDEWEB)

Amiri, Aboutaleb; Harvey, Cameron; Buchmann, Amy; Christley, Scott; Shrout, Joshua D.; Aranson, Igor S.; Alber, Mark

2017-03-01

Swarming groups of bacteria coordinate their behavior by self-organizing as a population to move over surfaces in search of nutrients and optimal niches for colonization. Many open questions remain about the cues used by swarming bacteria to achieve this self-organization. While chemical cue signaling known as quorum sensing is well-described, swarming bacteria often act and coordinate on time scales that could not be achieved via these extracellular quorum sensing cues. Here, cell-cell contact-dependent protein exchange is explored as amechanism of intercellular signaling for the bacterium Myxococcus xanthus. A detailed biologically calibrated computational model is used to study how M. xanthus optimizes the connection rate between cells and maximizes the spread of an extracellular protein within the population. The maximum rate of protein spreading is observed for cells that reverse direction optimally for swarming. Cells that reverse too slowly or too fast fail to spread extracellular protein efficiently. In particular, a specific range of cell reversal frequencies was observed to maximize the cell-cell connection rate and minimize the time of protein spreading. Furthermore, our findings suggest that predesigned motion reversal can be employed to enhance the collective behavior of biological synthetic active systems.

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.

Self-focusing therapeutic gene delivery with intelligent gene vector swarms: intra-swarm signalling through receptor transgene expression in targeted cells.

Science.gov (United States)

Tolmachov, Oleg E

2015-01-01

Gene delivery in vivo that is tightly focused on the intended target cells is essential to maximize the benefits of gene therapy and to reduce unwanted side-effects. Cell surface markers are immediately available for probing by therapeutic gene vectors and are often used to direct gene transfer with these vectors to specific target cell populations. However, it is not unusual for the choice of available extra-cellular markers to be too scarce to provide a reliable definition of the desired therapeutically relevant set of target cells. Therefore, interrogation of intra-cellular determinants of cell-specificity, such as tissue-specific transcription factors, can be vital in order to provide detailed cell-guiding information to gene vector particles. An important improvement in cell-specific gene delivery can be achieved through auto-buildup in vector homing efficiency using intelligent 'self-focusing' of swarms of vector particles on target cells. Vector self-focusing was previously suggested to rely on the release of diffusible chemo-attractants after a successful target-specific hit by 'scout' vector particles. I hypothesize that intelligent self-focusing behaviour of swarms of cell-targeted therapeutic gene vectors can be accomplished without the employment of difficult-to-use diffusible chemo-attractants, instead relying on the intra-swarm signalling through cells expressing a non-diffusible extra-cellular receptor for the gene vectors. In the proposed model, cell-guiding information is gathered by the 'scout' gene vector particles, which: (1) attach to a variety of cells via a weakly binding (low affinity) receptor; (2) successfully facilitate gene transfer into these cells; (3) query intra-cellular determinants of cell-specificity with their transgene expression control elements and (4) direct the cell-specific biosynthesis of a vector-encoded strongly binding (high affinity) cell-surface receptor. Free members of the vector swarm loaded with therapeutic cargo

Shape differences rather than size differences between castes in the Neotropical swarm-founding wasp Metapolybia docilis (Hymenoptera: Vespidae, Epiponini

Directory of Open Access Journals (Sweden)

Noll Fernando B

2003-05-01

Full Text Available Abstract Background Swarm-founding epiponine wasps are an intriguing group of social insects in which colonies are polygynic (several queens share reproduction and differentiation between castes is often not obvious. However, caste differences in some may be more pronounced in later phases of the colony cycle. Results Using morphometric analyses and multivariate statistics, it was found that caste differences in Metapolybia docilis are slight but more distinct in latter stages of the colony cycle. Conclusions Because differences in body parts are so slight, it is proposed that such variation may be due to differential growth rates of body parts rather than to queens being larger in size, similar to other previously observed epiponines.

Particle swarm optimization - Genetic algorithm (PSOGA) on linear transportation problem

Science.gov (United States)

Rahmalia, Dinita

2017-08-01

Linear Transportation Problem (LTP) is the case of constrained optimization where we want to minimize cost subject to the balance of the number of supply and the number of demand. The exact method such as northwest corner, vogel, russel, minimal cost have been applied at approaching optimal solution. In this paper, we use heurisitic like Particle Swarm Optimization (PSO) for solving linear transportation problem at any size of decision variable. In addition, we combine mutation operator of Genetic Algorithm (GA) at PSO to improve optimal solution. This method is called Particle Swarm Optimization - Genetic Algorithm (PSOGA). The simulations show that PSOGA can improve optimal solution resulted by PSO.

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.

Paleo magnetism of the Ceara-Mirim dyke swarm, Northeastern Brazil

International Nuclear Information System (INIS)

Ernesto, M.; Furtado, M.H.; Martins, G.; Macedo, J.W.P.

1991-01-01

The Mesozoic tholeiitic Ceara-Mirim dyke swarm has a general east-west trend cutting the Precambrian basement of northeastern Brazil. The dykes occur mainly in the State of Rio Grande do Norte (RN) but enter the neighbouring State of Ceara to the west where they trend SW-NE. Available K-Ar radiometric dates vary between 214 and 216 Ma. HORN et al. (1988) used a procedure which allowed the removal of argon-loss effects to conclude that the ages might be situated between Middle Jurassic and Early Cretaceous. Paleo magnetic data suggest that the emplacement of the sub-swarms was not simultaneous since they show distinct magnetization directions. New paleo magnetic results that confirm the above conclusion are presented here for the western part of the swarm, where the dykes show a SW-NE structural orientation. (author)

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

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.

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...

Swarm size and iteration number effects to the performance of PSO algorithm in RFID tag coverage optimization

Science.gov (United States)

Prathabrao, M.; Nawawi, Azli; Sidek, Noor Azizah

2017-04-01

Radio Frequency Identification (RFID) system has multiple benefits which can improve the operational efficiency of the organization. The advantages are the ability to record data systematically and quickly, reducing human errors and system errors, update the database automatically and efficiently. It is often more readers (reader) is needed for the installation purposes in RFID system. Thus, it makes the system more complex. As a result, RFID network planning process is needed to ensure the RFID system works perfectly. The planning process is also considered as an optimization process and power adjustment because the coordinates of each RFID reader to be determined. Therefore, algorithms inspired by the environment (Algorithm Inspired by Nature) is often used. In the study, PSO algorithm is used because it has few number of parameters, the simulation time is fast, easy to use and also very practical. However, PSO parameters must be adjusted correctly, for robust and efficient usage of PSO. Failure to do so may result in disruption of performance and results of PSO optimization of the system will be less good. To ensure the efficiency of PSO, this study will examine the effects of two parameters on the performance of PSO Algorithm in RFID tag coverage optimization. The parameters to be studied are the swarm size and iteration number. In addition to that, the study will also recommend the most optimal adjustment for both parameters that is, 200 for the no. iterations and 800 for the no. of swarms. Finally, the results of this study will enable PSO to operate more efficiently in order to optimize RFID network planning system.

Optimization of Transformation Coefficients Using Direct Search and Swarm Intelligence

Directory of Open Access Journals (Sweden)

Manusov V.Z.

2017-04-01

Full Text Available This research considers optimization of tap position of transformers in power systems to reduce power losses. Now, methods based on heuristic rules and fuzzy logic, or methods that optimize parts of the whole system separately, are applied to this problem. The first approach requires expert knowledge about processes in the network. The second methods are not able to consider all the interrelations of system’s parts, while changes in segment affect the entire system. Both approaches are tough to implement and require adjustment to the tasks solved. It needs to implement algorithms that can take into account complex interrelations of optimized variables and self-adapt to optimization task. It is advisable to use algorithms given complex interrelations of optimized variables and independently adapting from optimization tasks. Such algorithms include Swarm Intelligence algorithms. Their main features are self-organization, which allows them to automatically adapt to conditions of tasks, and the ability to efficiently exit from local extremes. Thus, they do not require specialized knowledge of the system, in contrast to fuzzy logic. In addition, they can efficiently find quasi-optimal solutions converging to the global optimum. This research applies Particle Swarm Optimization algorithm (PSO. The model of Tajik power system used in experiments. It was found out that PSO is much more efficient than greedy heuristics and more flexible and easier to use than fuzzy logic. PSO allows reducing active power losses from 48.01 to 45.83 MW (4.5%. With al, the effect of using greedy heuristics or fuzzy logic is two times smaller (2.3%.

Penerapan Metode Particle Swarm Optimization Pada Optimasi Prediksi Pemasaran Langsung

Directory of Open Access Journals (Sweden)

Yuni Eka Achyani

2018-04-01

consumers less responded by the market, therefore the company should strive for its products. , convincing, and influencing consumers to create demand for these products. Steps that can be done by the company to do so is to do direct marketing. Increased accuracy of direct marketing predictions can be done by selecting attributes, because of the selection. Data mining can run more effectively and quickly. In this study the method to be used is. With particle swarm optimization for direct marketing prediction optimization. After testing, the results obtained are support vector engine yield value of 88.71%, precision value 89.47% and AUC value of 0.896. Then the attribute selection is done using particle swarm optimization where the original attribute uses 16 predictor variables selected 12 attributes used. The results showed a higher value of 89.38%, 89.89% accuracy and AUC value of 0.909 with very good fair value (excellent classification. The price increase is 0.67%, and the increase of AUC is 0,013. Keywords: Particle Swarm Optimization, Direct Marketing, Selection Attributes.

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

Two-dimensional simulation of intermediate-sized bubbles in low viscous liquids using counter diffusion lattice Boltzmann method

Energy Technology Data Exchange (ETDEWEB)

Ryu, Seungyeob, E-mail: syryu@kaeri.re.kr [Korea Atomic Energy Research Institute (KAERI), 1045 Daeduk-daero, Yuseong-gu, Daejeon 305-353 (Korea, Republic of); Kim, Youngin; Kang, Hanok; Kim, Keung Koo [Korea Atomic Energy Research Institute (KAERI), 1045 Daeduk-daero, Yuseong-gu, Daejeon 305-353 (Korea, Republic of); Ko, Sungho, E-mail: sunghoko@cnu.ac.kr [Department of Mechanical Design Engineering, Chungnam National University, 220 Gung-dong, Yuseong-gu, Daejeon 305-764 (Korea, Republic of)

2016-08-15

Highlights: • We directly simulate intermediate-sized bubbles in low viscous liquids. • The path instability and shape oscillation can be successfully simulated. • The motion of a pair bubble and bubble swarm is presented. • Bubbles with high-Reynolds-number can be simulated with under-resolved grids. • The counter diffusion multiphase method is feasible for the direct simulation of bubbly flows. - Abstract: The counter diffusion lattice Boltzmann method (LBM) is used to simulate intermediate-sized bubbles in low viscous liquids. Bubbles at high Reynolds numbers ranging from hundreds to thousands are simulated successfully, which cannot be done for the existing LBM versions. The characteristics of the path instability of two rising bubbles are studied for a wide range of Eotvos and Morton numbers. Finally, the study presented how bubble swarms move within the flow and how the flow surrounding the bubbles is affected by the bubble motions.

Siting and sizing of distributed generators based on improved simulated annealing particle swarm optimization.

Science.gov (United States)

Su, Hongsheng

2017-12-18

Distributed power grids generally contain multiple diverse types of distributed generators (DGs). Traditional particle swarm optimization (PSO) and simulated annealing PSO (SA-PSO) algorithms have some deficiencies in site selection and capacity determination of DGs, such as slow convergence speed and easily falling into local trap. In this paper, an improved SA-PSO (ISA-PSO) algorithm is proposed by introducing crossover and mutation operators of genetic algorithm (GA) into SA-PSO, so that the capabilities of the algorithm are well embodied in global searching and local exploration. In addition, diverse types of DGs are made equivalent to four types of nodes in flow calculation by the backward or forward sweep method, and reactive power sharing principles and allocation theory are applied to determine initial reactive power value and execute subsequent correction, thus providing the algorithm a better start to speed up the convergence. Finally, a mathematical model of the minimum economic cost is established for the siting and sizing of DGs under the location and capacity uncertainties of each single DG. Its objective function considers investment and operation cost of DGs, grid loss cost, annual purchase electricity cost, and environmental pollution cost, and the constraints include power flow, bus voltage, conductor current, and DG capacity. Through applications in an IEEE33-node distributed system, it is found that the proposed method can achieve desirable economic efficiency and safer voltage level relative to traditional PSO and SA-PSO algorithms, and is a more effective planning method for the siting and sizing of DGs in distributed power grids.

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)

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 ...

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...

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.

Guidance and control of swarms of spacecraft

Science.gov (United States)

Morgan, Daniel James

There has been considerable interest in formation flying spacecraft due to their potential to perform certain tasks at a cheaper cost than monolithic spacecraft. Formation flying enables the use of smaller, cheaper spacecraft that distribute the risk of the mission. Recently, the ideas of formation flying have been extended to spacecraft swarms made up of hundreds to thousands of 100-gram-class spacecraft known as femtosatellites. The large number of spacecraft and limited capabilities of each individual spacecraft present a significant challenge in guidance, navigation, and control. This dissertation deals with the guidance and control algorithms required to enable the flight of spacecraft swarms. The algorithms developed in this dissertation are focused on achieving two main goals: swarm keeping and swarm reconfiguration. The objectives of swarm keeping are to maintain bounded relative distances between spacecraft, prevent collisions between spacecraft, and minimize the propellant used by each spacecraft. Swarm reconfiguration requires the transfer of the swarm to a specific shape. Like with swarm keeping, minimizing the propellant used and preventing collisions are the main objectives. Additionally, the algorithms required for swarm keeping and swarm reconfiguration should be decentralized with respect to communication and computation so that they can be implemented on femtosats, which have limited hardware capabilities. The algorithms developed in this dissertation are concerned with swarms located in low Earth orbit. In these orbits, Earth oblateness and atmospheric drag have a significant effect on the relative motion of the swarm. The complicated dynamic environment of low Earth orbits further complicates the swarm-keeping and swarm-reconfiguration problems. To better develop and test these algorithms, a nonlinear, relative dynamic model with J2 and drag perturbations is developed. This model is used throughout this dissertation to validate the algorithms

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.

Selecting Optimal Feature Set in High-Dimensional Data by Swarm Search

Directory of Open Access Journals (Sweden)

Simon Fong

2013-01-01

Full Text Available Selecting the right set of features from data of high dimensionality for inducing an accurate classification model is a tough computational challenge. It is almost a NP-hard problem as the combinations of features escalate exponentially as the number of features increases. Unfortunately in data mining, as well as other engineering applications and bioinformatics, some data are described by a long array of features. Many feature subset selection algorithms have been proposed in the past, but not all of them are effective. Since it takes seemingly forever to use brute force in exhaustively trying every possible combination of features, stochastic optimization may be a solution. In this paper, we propose a new feature selection scheme called Swarm Search to find an optimal feature set by using metaheuristics. The advantage of Swarm Search is its flexibility in integrating any classifier into its fitness function and plugging in any metaheuristic algorithm to facilitate heuristic search. Simulation experiments are carried out by testing the Swarm Search over some high-dimensional datasets, with different classification algorithms and various metaheuristic algorithms. The comparative experiment results show that Swarm Search is able to attain relatively low error rates in classification without shrinking the size of the feature subset to its minimum.

Camera Network Coverage Improving by Particle Swarm Optimization

NARCIS (Netherlands)

Xu, Y.C.; Lei, B.; Hendriks, E.A.

2011-01-01

This paper studies how to improve the field of view (FOV) coverage of a camera network. We focus on a special but practical scenario where the cameras are randomly scattered in a wide area and each camera may adjust its orientation but cannot move in any direction. We propose a particle swarm

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.

Photometric estimation of defect size in radiation direction

International Nuclear Information System (INIS)

Zuev, V.M.

1993-01-01

Factors, affecting accuracy of photometric estimation of defect size in radiation transmission direction, are analyzed. Experimentally obtained dependences of contrast of defect image on its size in radiation transmission direction are presented. Practical recommendations on improving accuracy of photometric estimation of defect size in radiation transmission direction, are developed

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....

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...

Smart swarms of bacteria-inspired agents with performance adaptable interactions.

Directory of Open Access Journals (Sweden)

Adi Shklarsh

2011-09-01

Full Text Available Collective navigation and swarming have been studied in animal groups, such as fish schools, bird flocks, bacteria, and slime molds. Computer modeling has shown that collective behavior of simple agents can result from simple interactions between the agents, which include short range repulsion, intermediate range alignment, and long range attraction. Here we study collective navigation of bacteria-inspired smart agents in complex terrains, with adaptive interactions that depend on performance. More specifically, each agent adjusts its interactions with the other agents according to its local environment--by decreasing the peers' influence while navigating in a beneficial direction, and increasing it otherwise. We show that inclusion of such performance dependent adaptable interactions significantly improves the collective swarming performance, leading to highly efficient navigation, especially in complex terrains. Notably, to afford such adaptable interactions, each modeled agent requires only simple computational capabilities with short-term memory, which can easily be implemented in simple swarming robots.

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.

Smart swarms of bacteria-inspired agents with performance adaptable interactions.

Science.gov (United States)

Shklarsh, Adi; Ariel, Gil; Schneidman, Elad; Ben-Jacob, Eshel

2011-09-01

Collective navigation and swarming have been studied in animal groups, such as fish schools, bird flocks, bacteria, and slime molds. Computer modeling has shown that collective behavior of simple agents can result from simple interactions between the agents, which include short range repulsion, intermediate range alignment, and long range attraction. Here we study collective navigation of bacteria-inspired smart agents in complex terrains, with adaptive interactions that depend on performance. More specifically, each agent adjusts its interactions with the other agents according to its local environment--by decreasing the peers' influence while navigating in a beneficial direction, and increasing it otherwise. We show that inclusion of such performance dependent adaptable interactions significantly improves the collective swarming performance, leading to highly efficient navigation, especially in complex terrains. Notably, to afford such adaptable interactions, each modeled agent requires only simple computational capabilities with short-term memory, which can easily be implemented in simple swarming robots.

Optimasi Bobot Jaringan Syaraf Tiruan Mengunakan Particle Swarm Optimization

Directory of Open Access Journals (Sweden)

Harry Ganda Nugraha

2014-01-01

Abstract Forecasting problem is common problem that easily found in decision making process. The popular tool to handle that problem is artificial neural network. Artificial neural network have been widely use because its ability to forecast nonlinear time series data. The learning method that have been widely use to train artificial neural network weight is backpropagation. Otherwise backpropagation learning process sometimes find problem such as over fiting so it can’t generalized the problem. Particle swarm optimization method had been proposed to train artificial neural network weigth. Mean square error, mean absolute percentage error, normalized mean square error, prediction of change in direction, average relative variance had been use to measures the model performance. Indonesia inflation time series data had been use to analyzed the model. The proposed method show that hybrid system could handle the time series forecasting problem as good as backpropagation artificial neural network   Keywords—artificial neural network, particle swarm optimization, prediction of change in direction, average relative variance.

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.

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.

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.

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)

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

Gene expression in Pseudomonas aeruginosa swarming motility

Directory of Open Access Journals (Sweden)

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...

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

Seasonal Modulation of Earthquake Swarm Activity Near Maupin, Oregon

Science.gov (United States)

Braunmiller, J.; Nabelek, J.; Trehu, A. M.

2012-12-01

Between December 2006 and November 2011, the Pacific Northwest Seismic Network (PNSN) reported 464 earthquakes in a swarm about 60 km east-southeast of Mt. Hood near the town of Maupin, Oregon. Relocation of forty-five MD≥2.5 earthquakes and regional moment tensor analysis of nine 3.3≤Mw≤3.9 earthquakes reveals a north-northwest trending, less than 1 km2 sized active fault patch on a 70° west dipping fault. At about 17 km depth, the swarm occurred at or close to the bottom of the seismogenic crust. The swarm's cumulative seismic moment release, equivalent to an Mw=4.4 earthquake, is not dominated by a single shock; it is rather mainly due to 20 MD≥3.0 events, which occurred throughout the swarm. The swarm started at the southern end and, during the first 18 months of activity, migrated to the northwest at a rate of about 1-2 m/d until reaching its northern terminus. A 10° fault bend, inferred from locations and fault plane solutions, acted as geometrical barrier that temporarily halted event migration in mid-2007 before continuing north in early 2008. The slow event migration points to a pore pressure diffusion process suggesting the swarm onset was triggered by fluid inflow into the fault zone. At 17 km depth, triggering by meteoritic water seems unlikely for a normal crustal permeability. The double couple source mechanisms preclude a magmatic intrusion at the depth of the earthquakes. However, fluids (or gases) associated with a deeper, though undocumented, magma injection beneath the Cascade Mountains, could trigger seismicity in a pre-stressed region when they have migrated upward and reached the seismogenic crust. Superimposed on overall swarm evolution, we found a statistically significant annual seismicity variation, which is likely surface driven. The annual seismicity peak during spring (March-May) coincides with the maximum snow load on the near-by Cascades. The load corresponds to a surface pressure variation of about 6 kPa, which likely

Optimal design and operation of a photovoltaic-electrolyser system using particle swarm optimisation

Science.gov (United States)

Sayedin, Farid; Maroufmashat, Azadeh; Roshandel, Ramin; Khavas, Sourena Sattari

2016-07-01

In this study, hydrogen generation is maximised by optimising the size and the operating conditions of an electrolyser (EL) directly connected to a photovoltaic (PV) module at different irradiance. Due to the variations of maximum power points of the PV module during a year and the complexity of the system, a nonlinear approach is considered. A mathematical model has been developed to determine the performance of the PV/EL system. The optimisation methodology presented here is based on the particle swarm optimisation algorithm. By this method, for the given number of PV modules, the optimal sizeand operating condition of a PV/EL system areachieved. The approach can be applied for different sizes of PV systems, various ambient temperatures and different locations with various climaticconditions. The results show that for the given location and the PV system, the energy transfer efficiency of PV/EL system can reach up to 97.83%.

Improving Vector Evaluated Particle Swarm Optimisation by Incorporating Nondominated Solutions

Directory of Open Access Journals (Sweden)

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.

Seismic swarm associated with the 2008 eruption of Kasatochi Volcano, Alaska: earthquake locations and source parameters

Science.gov (United States)

Ruppert, Natalia G.; Prejean, Stephanie G.; Hansen, Roger A.

2011-01-01

An energetic seismic swarm accompanied an eruption of Kasatochi Volcano in the central Aleutian volcanic arc in August of 2008. In retrospect, the first earthquakes in the swarm were detected about 1 month prior to the eruption onset. Activity in the swarm quickly intensified less than 48 h prior to the first large explosion and subsequently subsided with decline of eruptive activity. The largest earthquake measured as moment magnitude 5.8, and a dozen additional earthquakes were larger than magnitude 4. The swarm exhibited both tectonic and volcanic characteristics. Its shear failure earthquake features were b value = 0.9, most earthquakes with impulsive P and S arrivals and higher-frequency content, and earthquake faulting parameters consistent with regional tectonic stresses. Its volcanic or fluid-influenced seismicity features were volcanic tremor, large CLVD components in moment tensor solutions, and increasing magnitudes with time. Earthquake location tests suggest that the earthquakes occurred in a distributed volume elongated in the NS direction either directly under the volcano or within 5-10 km south of it. Following the MW 5.8 event, earthquakes occurred in a new crustal volume slightly east and north of the previous earthquakes. The central Aleutian Arc is a tectonically active region with seismicity occurring in the crusts of the Pacific and North American plates in addition to interplate events. We postulate that the Kasatochi seismic swarm was a manifestation of the complex interaction of tectonic and magmatic processes in the Earth's crust. Although magmatic intrusion triggered the earthquakes in the swarm, the earthquakes failed in context of the regional stress field.

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.

A Novel Adaptive Particle Swarm Optimization Algorithm with Foraging Behavior in Optimization Design

Directory of Open Access Journals (Sweden)

Liu Yan

2018-01-01

Full Text Available The method of repeated trial and proofreading is generally used to the convention reducer design, but these methods is low efficiency and the size of the reducer is often large. Aiming the problems, this paper presents an adaptive particle swarm optimization algorithm with foraging behavior, in this method, the bacterial foraging process is introduced into the adaptive particle swarm optimization algorithm, which can provide the function of particle chemotaxis, swarming, reproduction, elimination and dispersal, to improve the ability of local search and avoid premature behavior. By test verification through typical function and the application of the optimization design in the structure of the reducer with discrete and continuous variables, the results are shown that the new algorithm has the advantages of good reliability, strong searching ability and high accuracy. It can be used in engineering design, and has a strong applicability.

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.

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 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.

Scaling and spatial complementarity of tectonic earthquake swarms

KAUST Repository

Passarelli, Luigi

2017-11-10

Tectonic earthquake swarms (TES) often coincide with aseismic slip and sometimes precede damaging earthquakes. In spite of recent progress in understanding the significance and properties of TES at plate boundaries, their mechanics and scaling are still largely uncertain. Here we evaluate several TES that occurred during the past 20 years on a transform plate boundary in North Iceland. We show that the swarms complement each other spatially with later swarms discouraged from fault segments activated by earlier swarms, which suggests efficient strain release and aseismic slip. The fault area illuminated by earthquakes during swarms may be more representative of the total moment release than the cumulative moment of the swarm earthquakes. We use these findings and other published results from a variety of tectonic settings to discuss general scaling properties for TES. The results indicate that the importance of TES in releasing tectonic strain at plate boundaries may have been underestimated.

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.

Optimal Sizing of a Photovoltaic-Hydrogen Power System for HALE Aircraft by means of Particle Swarm Optimization

Directory of Open Access Journals (Sweden)

Victor M. Sanchez

2015-01-01

Full Text Available Over the last decade there has been a growing interest in the research of feasibility to use high altitude long endurance (HALE aircrafts in order to provide mobile communications. The use of HALEs for telecommunication networks has the potential to deliver a wide range of communication services (from high-quality voice to high-definition videos, as well as high-data-rate wireless channels cost effectively. One of the main challenges of this technology is to design its power supply system, which must provide the enough energy for long time flights in a reliable way. In this paper a photovoltaic/hydrogen system is proposed as power system for a HALE aircraft due its high power density characteristic. In order to obtain the optimal sizing for photovoltaic/hydrogen system a particle swarm optimizer (PSO is used. As a case study, theoretical design of the photovoltaic/hydrogen power system for three different HALE aircrafts located at 18° latitude is presented. At this latitude, the range of solar radiation intensity was from 310 to 450 Wh/sq·m/day. The results obtained show that the photovoltaic/hydrogen systems calculated by PSO can operate during one year with efficacies ranging between 45.82% and 47.81%. The obtained sizing result ensures that the photovoltaic/hydrogen system supplies adequate energy for HALE aircrafts.

Parameter Improved Particle Swarm Optimization Based Direct-Current Vector Control Strategy for Solar PV System

Directory of Open Access Journals (Sweden)

NAMMALVAR, P.

2018-02-01

Full Text Available This paper projects Parameter Improved Particle Swarm Optimization (PIPSO based direct current vector control technology for the integration of photovoltaic array in an AC micro-grid to enhance the system performance and stability. A photovoltaic system incorporated with AC micro-grid is taken as the pursuit of research study. The test system features two power converters namely, PV side converter which consists of DC-DC boost converter with Perturbation and Observe (P&O MPPT control to reap most extreme power from the PV array, and grid side converter which consists of Grid Side-Voltage Source Converter (GS-VSC with proposed direct current vector control strategy. The gain of the proposed controller is chosen from a set of three values obtained using apriori test and tuned through the PIPSO algorithm so that the Integral of Time multiplied Absolute Error (ITAE between the actual and the desired DC link capacitor voltage reaches a minimum and allows the system to extract maximum power from PV system, whereas the existing d-q control strategy is found to perform slowly to control the DC link voltage under varying solar insolation and load fluctuations. From simulation results, it is evident that the proposed optimal control technique provides robust control and improved efficiency.

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.

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.

A Local and Global Search Combine Particle Swarm Optimization Algorithm for Job-Shop Scheduling to Minimize Makespan

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Zhigang Lian

2010-01-01

Full Text Available The Job-shop scheduling problem (JSSP is a branch of production scheduling, which is among the hardest combinatorial optimization problems. Many different approaches have been applied to optimize JSSP, but for some JSSP even with moderate size cannot be solved to guarantee optimality. The original particle swarm optimization algorithm (OPSOA, generally, is used to solve continuous problems, and rarely to optimize discrete problems such as JSSP. In OPSOA, through research I find that it has a tendency to get stuck in a near optimal solution especially for middle and large size problems. The local and global search combine particle swarm optimization algorithm (LGSCPSOA is used to solve JSSP, where particle-updating mechanism benefits from the searching experience of one particle itself, the best of all particles in the swarm, and the best of particles in neighborhood population. The new coding method is used in LGSCPSOA to optimize JSSP, and it gets all sequences are feasible solutions. Three representative instances are made computational experiment, and simulation shows that the LGSCPSOA is efficacious for JSSP to minimize makespan.

Participation of irradiated Anopheles arabiensis males in swarms following field release in Sudan

International Nuclear Information System (INIS)

Ageep, Tellal B; Alsharif, Bashir; Ahmed, Ayman; Salih, Elwaleed HO; Ahmed, Fayez TA; El Sayed, Badria B; Damiens, David; Gilles, Jeremie RL; Lees, Rosemary S; Diabaté, Abdoulaye

2015-01-01

BACKGROUND: The success of the SIT depends on the; release of large numbers of sterile males, which are able to; compete for mates with the wild male population within the; target area. The processes of colonisation, mass production; and irradiation may reduce the competitiveness of sterile; males through genetic selection, loss of natural traits and; somatic damage. In this context, the capacity of released; sterile Anopheles arabiensis males to survive, disperse and; participate in swarms occurring at varying distances from; the release site was studied using mark-release-recapture; techniques.; METHODS: In order to assess their participation in; swarms, irradiated and marked laboratory-reared male; mosquitoes were released 50, 100 or 200 m from the; known site of a large swarm on three consecutive nights.; Males were collected from this large swarm on subsequent; nights. Over the three days a total of 8,100 males were released.; Mean distance travelled (MDT), daily probability of; survival and estimated population size were calculated; from the recapture data. An effect of male age at the time; of release on these parameters was observed.; RESULTS: Five per cent of the males released over three; days were recaptured. In two-, three- and four-day-old; males, MDT was 118, 178 and 170 m, and the daily survival; probability 0.95, 0.90 and 0.75, respectively. From the; recapture data on the first day following each release, the; Lincoln index gives an estimation of 32,546 males in the; natural population.; DISCUSSION: Sterile An. arabiensis males released into; the field were able to find and participate in existing; swarms, and possibly even initiate swarms. The survival; probability decreased with the age of male on release but; the swarm participation and the distance travelled by older; males seemed higher than for younger males. The inclusion; of a pre-release period may thus be beneficial to male competitiveness; and increase the attractiveness of adult sexing

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

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.

Simulation of microcirculatory hemodynamics: estimation of boundary condition using particle swarm optimization.

Science.gov (United States)

Pan, Qing; Wang, Ruofan; Reglin, Bettina; Fang, Luping; Pries, Axel R; Ning, Gangmin

2014-01-01

Estimation of the boundary condition is a critical problem in simulating hemodynamics in microvascular networks. This paper proposed a boundary estimation strategy based on a particle swarm optimization (PSO) algorithm, which aims to minimize the number of vessels with inverted flow direction in comparison to the experimental observation. The algorithm took boundary values as the particle swarm and updated the position of the particles iteratively to approach the optimization target. The method was tested in a real rat mesenteric network. With random initial boundary values, the method achieved a minimized 9 segments with an inverted flow direction in the network with 546 vessels. Compared with reported literature, the current work has the advantage of a better fit with experimental observations and is more suitable for the boundary estimation problem in pulsatile hemodynamic models due to the experiment-based optimization target selection.

Assessing Human Judgment of Computationally Generated Swarming Behavior

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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 using multi-information characteristics of all personal-best information.

Science.gov (United States)

Huang, Song; Tian, Na; Wang, Yan; Ji, Zhicheng

2016-01-01

Convergence stagnation is the chief difficulty to solve hard optimization problems for most particle swarm optimization variants. To address this issue, a novel particle swarm optimization using multi-information characteristics of all personal-best information is developed in our research. In the modified algorithm, two positions are defined by personal-best positions and an improved cognition term with three positions of all personal-best information is used in velocity update equation to enhance the search capability. This strategy could make particles fly to a better direction by discovering useful information from all the personal-best positions. The validity of the proposed algorithm is assessed on twenty benchmark problems including unimodal, multimodal, rotated and shifted functions, and the results are compared with that obtained by some published variants of particle swarm optimization in the literature. Computational results demonstrate that the proposed algorithm finds several global optimum and high-quality solutions in most case with a fast convergence speed.

A REVIEW OF SWARMING UNMANNED AERIAL VEHICLES

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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

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 ...

New hybrid genetic particle swarm optimization algorithm to design multi-zone binary filter.

Science.gov (United States)

Lin, Jie; Zhao, Hongyang; Ma, Yuan; Tan, Jiubin; Jin, Peng

2016-05-16

The binary phase filters have been used to achieve an optical needle with small lateral size. Designing a binary phase filter is still a scientific challenge in such fields. In this paper, a hybrid genetic particle swarm optimization (HGPSO) algorithm is proposed to design the binary phase filter. The HGPSO algorithm includes self-adaptive parameters, recombination and mutation operations that originated from the genetic algorithm. Based on the benchmark test, the HGPSO algorithm has achieved global optimization and fast convergence. In an easy-to-perform optimizing procedure, the iteration number of HGPSO is decreased to about a quarter of the original particle swarm optimization process. A multi-zone binary phase filter is designed by using the HGPSO. The long depth of focus and high resolution are achieved simultaneously, where the depth of focus and focal spot transverse size are 6.05λ and 0.41λ, respectively. Therefore, the proposed HGPSO can be applied to the optimization of filter with multiple parameters.

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.

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.

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)

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...

Optimal Placement and Sizing of PV-STATCOM in Power Systems Using Empirical Data and Adaptive Particle Swarm Optimization

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Reza Sirjani

2018-03-01

Full Text Available Solar energy is a source of free, clean energy which avoids the destructive effects on the environment that have long been caused by power generation. Solar energy technology rivals fossil fuels, and its development has increased recently. Photovoltaic (PV solar farms can only produce active power during the day, while at night, they are completely idle. At the same time, though, active power should be supported by reactive power. Reactive power compensation in power systems improves power quality and stability. The use during the night of a PV solar farm inverter as a static synchronous compensator (or PV-STATCOM device has recently been proposed which can improve system performance and increase the utility of a PV solar farm. In this paper, a method for optimal PV-STATCOM placement and sizing is proposed using empirical data. Considering the objectives of power loss and cost minimization as well as voltage improvement, two sub-problems of placement and sizing, respectively, are solved by a power loss index and adaptive particle swarm optimization (APSO. Test results show that APSO not only performs better in finding optimal solutions but also converges faster compared with bee colony optimization (BCO and lightening search algorithm (LSA. Installation of a PV solar farm, STATCOM, and PV-STATCOM in a system are each evaluated in terms of efficiency and cost.

Light-Controlled Swarming and Assembly of Colloidal Particles

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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.

Resolution of the stochastic strategy spatial prisoner's dilemma by means of particle swarm optimization.

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Jianlei Zhang

Full Text Available We study the evolution of cooperation among selfish individuals in the stochastic strategy spatial prisoner's dilemma game. We equip players with the particle swarm optimization technique, and find that it may lead to highly cooperative states even if the temptations to defect are strong. The concept of particle swarm optimization was originally introduced within a simple model of social dynamics that can describe the formation of a swarm, i.e., analogous to a swarm of bees searching for a food source. Essentially, particle swarm optimization foresees changes in the velocity profile of each player, such that the best locations are targeted and eventually occupied. In our case, each player keeps track of the highest payoff attained within a local topological neighborhood and its individual highest payoff. Thus, players make use of their own memory that keeps score of the most profitable strategy in previous actions, as well as use of the knowledge gained by the swarm as a whole, to find the best available strategy for themselves and the society. Following extensive simulations of this setup, we find a significant increase in the level of cooperation for a wide range of parameters, and also a full resolution of the prisoner's dilemma. We also demonstrate extreme efficiency of the optimization algorithm when dealing with environments that strongly favor the proliferation of defection, which in turn suggests that swarming could be an important phenomenon by means of which cooperation can be sustained even under highly unfavorable conditions. We thus present an alternative way of understanding the evolution of cooperative behavior and its ubiquitous presence in nature, and we hope that this study will be inspirational for future efforts aimed in this direction.

Improved quantum-behaved particle swarm optimization with local search strategy

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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.

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

The most active fault system of Turkey is North Anatolian Fault Zone and caused two large earthquakes in 1999. These two earthquakes affected the eastern Marmara region destructively. Unbroken part of the North Anatolian Fault Zone crosses north of Armutlu Peninsula on east-west direction. This branch has been also located quite close to Istanbul known as a megacity with its high population, economic and social aspects. A new cluster of microseismic activity occurred in the direct vicinity southeastern of the Yalova Termal area. Activity started on August 2, 2014 with a series of micro events, and then on August 3, 2014 a local magnitude is 4.1 event occurred, more than 1000 in the followed until August 31, 2014. Thus we call this tentatively a swarm-like activity. Therefore, investigation of the micro-earthquake activity of the Armutlu Peninsula has become important to understand the relationship between the occurrence of micro-earthquakes and the tectonic structure of the region. For these reasons, Armutlu Network (ARNET), installed end of 2005 and equipped with currently 27 active seismic stations operating by Kocaeli University Earth and Space Sciences Research Center (ESSRC) and Helmholtz-Zentrum Potsdam Deutsches GeoForschungsZentrum (GFZ), is a very dense network tool able to record even micro-earthquakes in this region. In the 30 days period of August 02 to 31, 2014 Kandilli Observatory and Earthquake Research Institute (KOERI) announced 120 local earthquakes ranging magnitudes between 0.7 and 4.1, but ARNET provided more than 1000 earthquakes for analyzes at the same time period. In this study, earthquakes of the swarm area and vicinity regions determined by ARNET were investigated. The focal mechanism of the August 03, 2014 22:22:42 (GMT) earthquake with local magnitude (Ml) 4.0 is obtained by the moment tensor solution. According to the solution, it discriminates a normal faulting with dextral component. The obtained focal mechanism solution is

Hierarchical Swarm Model: A New Approach to Optimization

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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.

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)

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....

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.

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.

Hydrothermal response to a volcano-tectonic earthquake swarm, Lassen, California

Science.gov (United States)

Ingebritsen, Steven E.; Shelly, David R.; Hsieh, Paul A.; Clor, Laura; P.H. Seward,; Evans, William C.

2015-01-01

The increasing capability of seismic, geodetic, and hydrothermal observation networks allows recognition of volcanic unrest that could previously have gone undetected, creating an imperative to diagnose and interpret unrest episodes. A November 2014 earthquake swarm near Lassen Volcanic National Park, California, which included the largest earthquake in the area in more than 60 years, was accompanied by a rarely observed outburst of hydrothermal fluids. Although the earthquake swarm likely reflects upward migration of endogenous H2O-CO2 fluids in the source region, there is no evidence that such fluids emerged at the surface. Instead, shaking from the modest sized (moment magnitude 3.85) but proximal earthquake caused near-vent permeability increases that triggered increased outflow of hydrothermal fluids already present and equilibrated in a local hydrothermal aquifer. Long-term, multiparametric monitoring at Lassen and other well-instrumented volcanoes enhances interpretation of unrest and can provide a basis for detailed physical modeling.

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 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.

Particle swarm optimization for programming deep brain stimulation arrays.

Science.gov (United States)

Peña, Edgar; Zhang, Simeng; Deyo, Steve; Xiao, YiZi; Johnson, Matthew D

2017-02-01

Deep brain stimulation (DBS) therapy relies on both precise neurosurgical targeting and systematic optimization of stimulation settings to achieve beneficial clinical outcomes. One recent advance to improve targeting is the development of DBS arrays (DBSAs) with electrodes segmented both along and around the DBS lead. However, increasing the number of independent electrodes creates the logistical challenge of optimizing stimulation parameters efficiently. Solving such complex problems with multiple solutions and objectives is well known to occur in biology, in which complex collective behaviors emerge out of swarms of individual organisms engaged in learning through social interactions. Here, we developed a particle swarm optimization (PSO) algorithm to program DBSAs using a swarm of individual particles representing electrode configurations and stimulation amplitudes. Using a finite element model of motor thalamic DBS, we demonstrate how the PSO algorithm can efficiently optimize a multi-objective function that maximizes predictions of axonal activation in regions of interest (ROI, cerebellar-receiving area of motor thalamus), minimizes predictions of axonal activation in regions of avoidance (ROA, somatosensory thalamus), and minimizes power consumption. The algorithm solved the multi-objective problem by producing a Pareto front. ROI and ROA activation predictions were consistent across swarms (<1% median discrepancy in axon activation). The algorithm was able to accommodate for (1) lead displacement (1 mm) with relatively small ROI (⩽9.2%) and ROA (⩽1%) activation changes, irrespective of shift direction; (2) reduction in maximum per-electrode current (by 50% and 80%) with ROI activation decreasing by 5.6% and 16%, respectively; and (3) disabling electrodes (n  =  3 and 12) with ROI activation reduction by 1.8% and 14%, respectively. Additionally, comparison between PSO predictions and multi-compartment axon model simulations showed discrepancies

Particle Swarm Optimization for Programming Deep Brain Stimulation Arrays

Science.gov (United States)

Peña, Edgar; Zhang, Simeng; Deyo, Steve; Xiao, YiZi; Johnson, Matthew D.

2017-01-01

Objective Deep brain stimulation (DBS) therapy relies on both precise neurosurgical targeting and systematic optimization of stimulation settings to achieve beneficial clinical outcomes. One recent advance to improve targeting is the development of DBS arrays (DBSAs) with electrodes segmented both along and around the DBS lead. However, increasing the number of independent electrodes creates the logistical challenge of optimizing stimulation parameters efficiently. Approach Solving such complex problems with multiple solutions and objectives is well known to occur in biology, in which complex collective behaviors emerge out of swarms of individual organisms engaged in learning through social interactions. Here, we developed a particle swarm optimization (PSO) algorithm to program DBSAs using a swarm of individual particles representing electrode configurations and stimulation amplitudes. Using a finite element model of motor thalamic DBS, we demonstrate how the PSO algorithm can efficiently optimize a multi-objective function that maximizes predictions of axonal activation in regions of interest (ROI, cerebellar-receiving area of motor thalamus), minimizes predictions of axonal activation in regions of avoidance (ROA, somatosensory thalamus), and minimizes power consumption. Main Results The algorithm solved the multi-objective problem by producing a Pareto front. ROI and ROA activation predictions were consistent across swarms (<1% median discrepancy in axon activation). The algorithm was able to accommodate for (1) lead displacement (1 mm) with relatively small ROI (≤9.2%) and ROA (≤1%) activation changes, irrespective of shift direction; (2) reduction in maximum per-electrode current (by 50% and 80%) with ROI activation decreasing by 5.6% and 16%, respectively; and (3) disabling electrodes (n=3 and 12) with ROI activation reduction by 1.8% and 14%, respectively. Additionally, comparison between PSO predictions and multi-compartment axon model simulations

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...

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.

Derivative load voltage and particle swarm optimization to determine optimum sizing and placement of shunt capacitor in improving line losses

Directory of Open Access Journals (Sweden)

Mohamed Milad Baiek

2016-12-01

Full Text Available The purpose of this research is to study optimal size and placement of shunt capacitor in order to minimize line loss. Derivative load bus voltage was calculated to determine the sensitive load buses which further being optimum with the placement of shunt capacitor. Particle swarm optimization (PSO was demonstrated on the IEEE 14 bus power system to find optimum size of shunt capacitor in reducing line loss. The objective function was applied to determine the proper placement of capacitor and get satisfied solutions towards constraints. The simulation was run over Matlab under two scenarios namely base case and increasing 100% load. Derivative load bus voltage was simulated to determine the most sensitive load bus. PSO was carried out to determine the optimum sizing of shunt capacitor at the most sensitive bus. The results have been determined that the most sensitive bus was bus number 14 for the base case and increasing 100% load. The optimum sizing was 8.17 Mvar for the base case and 23.98 Mvar for increasing load about 100%. Line losses were able to reduce approximately 0.98% for the base case and increasing 100% load reduced about 3.16%. The proposed method was also proven as a better result compared with harmony search algorithm (HSA method. HSA method recorded loss reduction ratio about 0.44% for the base case and 2.67% when the load was increased by 100% while PSO calculated loss reduction ratio about 1.12% and 4.02% for the base case and increasing 100% load respectively. The result of this study will support the previous study and it is concluded that PSO was successfully able to solve some engineering problems as well as to find a solution in determining shunt capacitor sizing on the power system simply and accurately compared with other evolutionary optimization methods.

Hydrodynamics in a swarm of rising bubbles

International Nuclear Information System (INIS)

Riboux, G.

2007-04-01

In many applications, bubbles are used to agitate a liquid in order to enhance mixing and transfer. This work is devoted to the study of the hydrodynamics in a stable bubble column. Experimentally, we have determined the properties of the velocity fluctuations inside and behind a homogeneous swarm of rising bubbles for different bubble sizes and gas volume fractions α: self-similarity in α 0,4 , spectrum in k -3 and integral length scale controlled by buoyancy. Numerically, we have reproduced these properties by means of large-scale simulations, the bubbles being modeled by volume-forces. This confirms that the dynamics is controlled by wake interactions. (author)

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.

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

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...

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

Directory of Open Access Journals (Sweden)

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.

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.

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.

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...

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.

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)

Scaling and spatial complementarity of tectonic earthquake swarms

KAUST Repository

Passarelli, Luigi; Rivalta, Eleonora; Jonsson, Sigurjon; Hensch, Martin; Metzger, Sabrina; Jakobsdó ttir, Steinunn S.; Maccaferri, Francesco; Corbi, Fabio; Dahm, Torsten

2017-01-01

are still largely uncertain. Here we evaluate several TES that occurred during the past 20 years on a transform plate boundary in North Iceland. We show that the swarms complement each other spatially with later swarms discouraged from fault segments

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.

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.

Identification of strategy parameters for particle swarm optimizer through Taguchi method

Institute of Scientific and Technical Information of China (English)

KHOSLA Arun; KUMAR Shakti; AGGARWAL K.K.

2006-01-01

Particle swarm optimization (PSO), like other evolutionary algorithms is a population-based stochastic algorithm inspired from the metaphor of social interaction in birds, insects, wasps, etc. It has been used for finding promising solutions in complex search space through the interaction of particles in a swarm. It is a well recognized fact that the performance of evolutionary algorithms to a great extent depends on the choice of appropriate strategy/operating parameters like population size,crossover rate, mutation rate, crossover operator, etc. Generally, these parameters are selected through hit and trial process, which is very unsystematic and requires rigorous experimentation. This paper proposes a systematic based on Taguchi method reasoning scheme for rapidly identifying the strategy parameters for the PSO algorithm. The Taguchi method is a robust design approach using fractional factorial design to study a large number of parameters with small number of experiments. Computer simulations have been performed on two benchmark functions-Rosenbrock function and Griewank function-to validate the approach.

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.

Molecular Interaction and Cellular Location of RecA and CheW Proteins in Salmonella enterica during SOS Response and Their Implication in Swarming.

Science.gov (United States)

Irazoki, Oihane; Aranda, Jesús; Zimmermann, Timo; Campoy, Susana; Barbé, Jordi

2016-01-01

In addition to its role in DNA damage repair and recombination, the RecA protein, through its interaction with CheW, is involved in swarming motility, a form of flagella-dependent movement across surfaces. In order to better understand how SOS response modulates swarming, in this work the location of RecA and CheW proteins within the swarming cells has been studied by using super-resolution microscopy. Further, and after in silico docking studies, the specific RecA and CheW regions associated with the RecA-CheW interaction have also been confirmed by site-directed mutagenesis and immunoprecipitation techniques. Our results point out that the CheW distribution changes, from the cell poles to foci distributed in a helical pattern along the cell axis when SOS response is activated or RecA protein is overexpressed. In this situation, the CheW presents the same subcellular location as that of RecA, pointing out that the previously described RecA storage structures may be modulators of swarming motility. Data reported herein not only confirmed that the RecA-CheW pair is essential for swarming motility but it is directly involved in the CheW distribution change associated to SOS response activation. A model explaining not only the mechanism by which DNA damage modulates swarming but also how both the lack and the excess of RecA protein impair this motility is proposed.

Molecular interaction and cellular location of RecA and CheW proteins in Salmonella enterica during SOS response and their implication in swarming

Directory of Open Access Journals (Sweden)

Oihane Irazoki

2016-10-01

Full Text Available In addition to its role in DNA damage repair and recombination, the RecA protein, through its interaction with CheW, is involved in swarming motility, a form of flagella-dependent movement across surfaces. In order to better understand how SOS response modulates swarming, in this work the location of RecA and CheW proteins within the swarming cells has been studied by using super-resolution microscopy. Further, and after in silico docking studies, the specific RecA and CheW regions associated with the RecA-CheW interaction have also been confirmed by site-directed mutagenesis and immunoprecipitation techniques. Our results point out that the CheW distribution changes, from the cell poles to foci distributed in a helical pattern along the cell axis when SOS response is activated or RecA protein is overexpressed. In this situation, the CheW presents the same subcellular location as that of RecA, pointing out that the previously described RecA storage structures may be modulators of swarming motility. Data reported herein not only confirmed that the RecA-CheW pair is essential for swarming motility but it is directly involved in the CheW distribution change associated to SOS response activation. A model explaining not only the mechanism by which DNA damage modulates swarming but also how both the lack and the excess of RecA protein impair this motility is proposed.

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.

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.

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 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)

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.

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...

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

Engineering the evolution of self-organizing behaviors in swarm robotics: a case study.

Science.gov (United States)

Trianni, Vito; Nolfi, Stefano

2011-01-01

Evolutionary robotics (ER) is a powerful approach for the automatic synthesis of robot controllers, as it requires little a priori knowledge about the problem to be solved in order to obtain good solutions. This is particularly true for collective and swarm robotics, in which the desired behavior of the group is an indirect result of the control and communication rules followed by each individual. However, the experimenter must make several arbitrary choices in setting up the evolutionary process, in order to define the correct selective pressures that can lead to the desired results. In some cases, only a deep understanding of the obtained results can point to the critical aspects that constrain the system, which can be later modified in order to re-engineer the evolutionary process towards better solutions. In this article, we discuss the problem of engineering the evolutionary machinery that can lead to the desired result in the swarm robotics context. We also present a case study about self-organizing synchronization in a swarm of robots, in which some arbitrarily chosen properties of the communication system hinder the scalability of the behavior to large groups. We show that by modifying the communication system, artificial evolution can synthesize behaviors that scale properly with the group size.

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

Three-dimensional structure of a swarm of the salp Thalia democratica within a cold-core eddy off southeast Australia

Science.gov (United States)

Everett, J. D.; Baird, M. E.; Suthers, I. M.

2011-12-01

Swarms of the salp Thalia democratica periodically occur off southeast Australia following the austral spring bloom of phytoplankton. In October 2008 a filament of upwelled water was advected south by the adjacent East Australian Current and formed a 30 km diameter cold-core eddy (CCE). The three-dimensional structure of a subsurface swarm of T. democratica within the eddy was examined using both oblique and vertical hauls and an optical plankton counter (OPC) deployed on a towed body. The CCE displayed distinct uplift of the nutricline and elevated fluorescence. Net samples show the zooplankton community was dominated by T. democratica, comprising 73%-88% of zooplankton abundance. The size distribution of T. democratica measured from net samples was 0.5-5 mm and was used to interpret the OPC transects, which showed the swarm formed a 15 km diameter disc located 20-40 m deep in the center of the eddy. The maximum salp abundance was in the pycnocline and coincided with the subsurface fluorescence maximum. The mean abundance of T. democratica size particles within the disc was 5003 individuals m-3 (ind. m-3), contrasted with only 604 ind. m-3 at the outer edge of the eddy. The vertically concentrated and horizontally constrained disc-shaped salp swarm occurred at the interface of salp-bearing inner shelf water and nutrient-rich upwelled water in a CCE. The physical processes that formed the CCE on the inshore edge of the western boundary current led to the largest density of salps recorded.

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

Temporal Changes in Stress Drop, Frictional Strength, and Earthquake Size Distribution in the 2011 Yamagata-Fukushima, NE Japan, Earthquake Swarm, Caused by Fluid Migration

Science.gov (United States)

Yoshida, Keisuke; Saito, Tatsuhiko; Urata, Yumi; Asano, Youichi; Hasegawa, Akira

2017-12-01

In this study, we investigated temporal variations in stress drop and b-value in the earthquake swarm that occurred at the Yamagata-Fukushima border, NE Japan, after the 2011 Tohoku-Oki earthquake. In this swarm, frictional strengths were estimated to have changed with time due to fluid diffusion. We first estimated the source spectra for 1,800 earthquakes with 2.0 ≤ MJMA < 3.0, by correcting the site-amplification and attenuation effects determined using both S waves and coda waves. We then determined corner frequency assuming the omega-square model and estimated stress drop for 1,693 earthquakes. We found that the estimated stress drops tended to have values of 1-4 MPa and that stress drops significantly changed with time. In particular, the estimated stress drops were very small at the beginning, and increased with time for 50 days. Similar temporal changes were obtained for b-value; the b-value was very high (b 2) at the beginning, and decreased with time, becoming approximately constant (b 1) after 50 days. Patterns of temporal changes in stress drop and b-value were similar to the patterns for frictional strength and earthquake occurrence rate, suggesting that the change in frictional strength due to migrating fluid not only triggered the swarm activity but also affected earthquake and seismicity characteristics. The estimated high Q-1 value, as well as the hypocenter migration, supports the presence of fluid, and its role in the generation and physical characteristics of the swarm.

Simulation Study of Swarm Intelligence Based on Life Evolution Behavior

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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.

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.

A Constructive Data Classification Version of the Particle Swarm Optimization Algorithm

Directory of Open Access Journals (Sweden)

Alexandre Szabo

2013-01-01

Full Text Available The particle swarm optimization algorithm was originally introduced to solve continuous parameter optimization problems. It was soon modified to solve other types of optimization tasks and also to be applied to data analysis. In the latter case, however, there are few works in the literature that deal with the problem of dynamically building the architecture of the system. This paper introduces new particle swarm algorithms specifically designed to solve classification problems. The first proposal, named Particle Swarm Classifier (PSClass, is a derivation of a particle swarm clustering algorithm and its architecture, as in most classifiers, is pre-defined. The second proposal, named Constructive Particle Swarm Classifier (cPSClass, uses ideas from the immune system to automatically build the swarm. A sensitivity analysis of the growing procedure of cPSClass and an investigation into a proposed pruning procedure for this algorithm are performed. The proposals were applied to a wide range of databases from the literature and the results show that they are competitive in relation to other approaches, with the advantage of having a dynamically constructed architecture.

Theory of periodic swarming of bacteria: Application to Proteus mirabilis

Science.gov (United States)

Czirók, A.; Matsushita, M.; Vicsek, T.

2001-03-01

The periodic swarming of bacteria is one of the simplest examples for pattern formation produced by the self-organized collective behavior of a large number of organisms. In the spectacular colonies of Proteus mirabilis (the most common species exhibiting this type of growth), a series of concentric rings are developed as the bacteria multiply and swarm following a scenario that periodically repeats itself. We have developed a theoretical description for this process in order to obtain a deeper insight into some of the typical processes governing the phenomena in systems of many interacting living units. Our approach is based on simple assumptions directly related to the latest experimental observations on colony formation under various conditions. The corresponding one-dimensional model consists of two coupled differential equations investigated here both by numerical integrations and by analyzing the various expressions obtained from these equations using a few natural assumptions about the parameters of the model. We determine the phase diagram corresponding to systems exhibiting periodic swarming, and discuss in detail how the various stages of the colony development can be interpreted in our framework. We point out that all of our theoretical results are in excellent agreement with the complete set of available observations. Thus the present study represents one of the few examples where self-organized biological pattern formation is understood within a relatively simple theoretical approach, leading to results and predictions fully compatible with experiments.

Shear-wave polarization analysis of the seismic swarm following the July 9th 1998 Faial (Azores) earthquake

Science.gov (United States)

Dias, N. A.; Matias, L.; Tellez, J.; Senos, L.; Gaspar, J. L.

2003-04-01

The Azores Islands, located at a tectonic triple Junction, geodynamically are a highly active place. The seismicity in this region occurs mainly in the form of two types of seismic swarms with tectonic and/or volcanic origins, lasting from hours to years. In some cases the swarm follows a main stronger shock, while in others the more energetic event occurs sometime after the beginning of the swarm. In order to understand the complex phenomena of this region, a multidisciplinary approach is needed, involving geophysical, geological and geochemical studies such as the one being carried under the MASHA project (POCTI/CTA/39158/2001), On July 9th 1998 an Mw=6.2 earthquake stroked the island of Faial, in the central group of the Azores archipelago, followed by a seismic swarm still active today. We will present some preliminary results of the shear-wave polarization analysis of a selected dataset of events of this swarm. These correspond to the 112 best- constrained events, record during the first 2 weeks by the seismic network deployed on the 3 islands surrounding the area of the main shock. The objective was to analyse the behaviour of the S wave polarization and the eventual relationship with the presence of seismic anisotropy under the seismic stations, and to correlate this with the regional structure and origin of the Azores plateau. Two main tectonic features are observable on the islands, one primarily orientated SE-NW and the other crossing it roughly with the WNW-ESE direction. The polarization direction observed in the majority of the seismic stations is not stable, varying from SE-NW to WSW-ENE, and showing also the presence in same cases of shear-wave splitting, indicating the presence of anisotropy. Part of the polarization seems to be coherent with the direction of the local tectonic features, but its instability suggest a more complex seismic anisotropy than that proposed by the model EDA of Crampin. Furthermore, the dataset revealed some limitations to

An Improved Method for Reconfiguring and Optimizing Electrical Active Distribution Network Using Evolutionary Particle Swarm Optimization

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Nur Faziera Napis

2018-05-01

Full Text Available The presence of optimized distributed generation (DG with suitable distribution network reconfiguration (DNR in the electrical distribution network has an advantage for voltage support, power losses reduction, deferment of new transmission line and distribution structure and system stability improvement. However, installation of a DG unit at non-optimal size with non-optimal DNR may lead to higher power losses, power quality problem, voltage instability and incremental of operational cost. Thus, an appropriate DG and DNR planning are essential and are considered as an objective of this research. An effective heuristic optimization technique named as improved evolutionary particle swarm optimization (IEPSO is proposed in this research. The objective function is formulated to minimize the total power losses (TPL and to improve the voltage stability index (VSI. The voltage stability index is determined for three load demand levels namely light load, nominal load, and heavy load with proper optimal DNR and DG sizing. The performance of the proposed technique is compared with other optimization techniques, namely particle swarm optimization (PSO and iteration particle swarm optimization (IPSO. Four case studies on IEEE 33-bus and IEEE 69-bus distribution systems have been conducted to validate the effectiveness of the proposed IEPSO. The optimization results show that, the best achievement is done by IEPSO technique with power losses reduction up to 79.26%, and 58.41% improvement in the voltage stability index. Moreover, IEPSO has the fastest computational time for all load conditions as compared to other algorithms.

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...

A Parallel Adaptive Particle Swarm Optimization Algorithm for Economic/Environmental Power Dispatch

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Jinchao Li

2012-01-01

Full Text Available A parallel adaptive particle swarm optimization algorithm (PAPSO is proposed for economic/environmental power dispatch, which can overcome the premature characteristic, the slow-speed convergence in the late evolutionary phase, and lacking good direction in particles’ evolutionary process. A search population is randomly divided into several subpopulations. Then for each subpopulation, the optimal solution is searched synchronously using the proposed method, and thus parallel computing is realized. To avoid converging to a local optimum, a crossover operator is introduced to exchange the information among the subpopulations and the diversity of population is sustained simultaneously. Simulation results show that the proposed algorithm can effectively solve the economic/environmental operation problem of hydropower generating units. Performance comparisons show that the solution from the proposed method is better than those from the conventional particle swarm algorithm and other optimization algorithms.

SWARMS Early Trials Management for The SWARMs ECSEL-H2020 Project

Science.gov (United States)

Alcaraz, Daniel; Morales, Tania; Castro, Ayoze; Barrera, Carlos; Hernández, Joaquín; Llinás, Octavio

2017-04-01

The work presented on this paper is aimed to explain how the Early Trials of the Project SWARMS were managed in order to complete the first field demonstrations on real environment. SWARMs aims to reduce the operational cost in the use of maritime robots and vehicles, in order to increase the safety of tasks and reduce profesional divers risks. This will be achieved enabling the AUVs/ROVs to work in a cooperative mesh. The challenge is to design and develop an integrated platform (a set of Software/Hardware components), incorporated into the current generation of underwater vehicles in order to improve autonomy, cooperation, robustness, cost-effectiveness, and reliability of the offshore operations. The first demonstration of the project has been performed at PLOCAN (The Oceanic Platform of the Canary Islands) where these technologies were validated on its first stage. The Early Trials have represented the first in situ deployment and test of the novel technologies developed during the initial 14 months of the Project. Going into the sea supposed a huge challenge also in terms of management. The 32 partners of SWARMS had very different requirements (logistics, technical needs, software/computation needs, etc.), and a limited time frame to test and prove their individual developments. In order to fullfill the project objectives, all these tests were divided in 7 missions that were aimed to cover this early demonstration requiements. From PLOCAN, a management protocol was designed in order to cover all the partners needs and make an efficient resource asignment from the begining. These results will be extended to other two demonstrations of the project that forseen to be held in Romania (2017) and Norway (2018).

Inversion of particle size distribution by spectral extinction technique using the attractive and repulsive particle swarm optimization algorithm

Directory of Open Access Journals (Sweden)

Qi Hong

2015-01-01

Full Text Available The particle size distribution (PSD plays an important role in environmental pollution detection and human health protection, such as fog, haze and soot. In this study, the Attractive and Repulsive Particle Swarm Optimization (ARPSO algorithm and the basic PSO were applied to retrieve the PSD. The spectral extinction technique coupled with the Anomalous Diffraction Approximation (ADA and the Lambert-Beer Law were employed to investigate the retrieval of the PSD. Three commonly used monomodal PSDs, i.e. the Rosin-Rammer (R-R distribution, the normal (N-N distribution, the logarithmic normal (L-N distribution were studied in the dependent model. Then, an optimal wavelengths selection algorithm was proposed. To study the accuracy and robustness of the inverse results, some characteristic parameters were employed. The research revealed that the ARPSO showed more accurate and faster convergence rate than the basic PSO, even with random measurement error. Moreover, the investigation also demonstrated that the inverse results of four incident laser wavelengths showed more accurate and robust than those of two wavelengths. The research also found that if increasing the interval of the selected incident laser wavelengths, inverse results would show more accurate, even in the presence of random error.

Anti-predatory particle swarm optimization: Solution to nonconvex economic dispatch problems

Energy Technology Data Exchange (ETDEWEB)

Selvakumar, A. Immanuel [Department of Electrical and Electronics Engineering, Karunya Institute of Technology and Sciences, Coimbatore 641114, Tamilnadu (India); Thanushkodi, K. [Department of Electronics and Instrumentation Engineering, Government College of Technology, Coimbatore 641013, Tamilnadu (India)

2008-01-15

This paper proposes a new particle swarm optimization (PSO) strategy namely, anti-predatory particle swarm optimization (APSO) to solve nonconvex economic dispatch problems. In the classical PSO, the movement of a particle (bird) is governed by three behaviors: inertial, cognitive and social. The cognitive and social behaviors are the components of the foraging activity, which help the swarm of birds to locate food. Another activity that is observed in birds is the anti-predatory nature, which helps the swarm to escape from the predators. In this work, the anti-predatory activity is modeled and embedded in the classical PSO to form APSO. This inclusion enhances the exploration capability of the swarm. To validate the proposed APSO model, it is applied to two test systems having nonconvex solution spaces. Satisfactory results are obtained when compared with previous approaches. (author)

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.

Langevin dynamics encapsulate the microscopic and emergent macroscopic properties of midge swarms

Science.gov (United States)

2018-01-01

In contrast to bird flocks, fish schools and animal herds, midge swarms maintain cohesion but do not possess global order. High-speed imaging techniques are now revealing that these swarms have surprising properties. Here, I show that simple models found on the Langevin equation are consistent with this wealth of recent observations. The models predict correctly that large accelerations, exceeding 10 g, will be common and they predict correctly the coexistence of core condensed phases surrounded by dilute vapour phases. The models also provide new insights into the influence of environmental conditions on swarm dynamics. They predict that correlations between midges increase the strength of the effective force binding the swarm together. This may explain why such correlations are absent in laboratory swarms but present in natural swarms which contend with the wind and other disturbances. Finally, the models predict that swarms have fluid-like macroscopic mechanical properties and will slosh rather than slide back and forth after being abruptly displaced. This prediction offers a promising avenue for future experimentation that goes beyond current quasi-static testing which has revealed solid-like responses. PMID:29298958

Environment mapping and localization with an uncontrolled swarm of ultrasound sensor motes

NARCIS (Netherlands)

Duisterwinkel, E.; Demi, L.; Dubbelman, G.; Talnishnikh, E.; Wörtche, H.J.; Bergmans, J.W.M.

2014-01-01

A method is presented in which a (large) swarm of sensor motes perform simple ultrasonic ranging measurements. The method allows to localize the motes within the swarm, and at the same time, map the environment which the swarm has traversed. The motes float passively uncontrolled through the

Rapid movement and instability of an invasive hybrid swarm.

Science.gov (United States)

Glotzbecker, Gregory J; Walters, David M; Blum, Michael J

2016-07-01

Unstable hybrid swarms that arise following the introduction of non-native species can overwhelm native congeners, yet the stability of invasive hybrid swarms has not been well documented over time. Here, we examine genetic variation and clinal stability across a recently formed hybrid swarm involving native blacktail shiner (Cyprinella venusta) and non-native red shiner (C. lutrensis) in the Upper Coosa River basin, which is widely considered to be a global hot spot of aquatic biodiversity. Examination of phenotypic, multilocus genotypic, and mitochondrial haplotype variability between 2005 and 2011 revealed that the proportion of hybrids has increased over time, with more than a third of all sampled individuals exhibiting admixture in the final year of sampling. Comparisons of clines over time indicated that the hybrid swarm has been rapidly progressing upstream, but at a declining and slower pace than rates estimated from historical collection records. Clinal comparisons also showed that the hybrid swarm has been expanding and contracting over time. Additionally, we documented the presence of red shiner and hybrids farther downstream than prior studies have detected, which suggests that congeners in the Coosa River basin, including all remaining populations of the threatened blue shiner (Cyprinella caerulea), are at greater risk than previously thought.

Tectonic implications of a paleomagnetic direction obtained from a Miocene dike swarm in central Honshu, Japan

Science.gov (United States)

Hoshi, H.; Sugisaki, Y.

2017-12-01

Central Honshu of Japan is an ideal field for the study of crustal deformation related to arc-arc collision. In this study we obtained rock magnetic and paleomagnetic results from early Miocene igneous rocks in central Honshu in order to examine rotational deformation caused by the collision of the Izu-Bonin-Mariana (IBM) arc with central Honshu. In Takane of the Hida region, gabbro intrusions and older sedimentary rocks are intruded by numerous andesitic dikes that comprise a parallel dike swarm. The dikes formed under two different normal-faulting paleostress conditions, which were suggested using a method of clustering dike orientations. Cross-cutting relationships indicate that the two paleostress conditions existed during the same period. More than 240 oriented cores were taken at 38 sites in two localities for magnetic study. The andesites and gabbros generally have magnetite, and some andesites also contain pyrrhotite. The magnetite records easterly deflected remanent magnetization directions of dual polarities that pass the reversals test. Positive baked contact tests at two sites demonstrate that the easterly deflected direction is a thermoremanent magnetization acquired at the time of intrusion. The overall in situ (i.e., in geographic coordinates) mean direction for andesitic dikes is judged to be highly reliable, although there are two possible scenarios for explaining the easterly deflection: (1) clockwise rotation and (2) tilting to the northwest. We prefer the former scenario and conclude that 45° clockwise rotation occurred in Takane with respect to the North China Block of the Asian continent. This rotation must represent the clockwise rotation of entire Southwest Japan during the opening period of the Japan Sea. Very little difference is observed between the amount of the easterly deflection in Takane and those in the Tokai and Hokuriku regions, indicating no significant relative rotation. Thus, the crust beneath Takane has not suffered rotation

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.

A Survey of Formal Methods for Intelligent Swarms

Science.gov (United States)

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

2004-01-01

Swarms of intelligent autonomous spacecraft, involving complex behaviors and interactions, are being proposed for future space exploration missions. Such missions provide greater flexibility and offer the possibility of gathering more science data than traditional single spacecraft missions. The emergent properties of swarms make these missions powerful, but simultaneously far more difficult to design, and to assure that the proper behaviors will emerge. These missions are also considerably more complex than previous types of missions, and NASA, like other organizations, has little experience in developing or in verifying and validating these types of missions. A significant challenge when verifying and validating swarms of intelligent interacting agents is how to determine that the possible exponential interactions and emergent behaviors are producing the desired results. Assuring correct behavior and interactions of swarms will be critical to mission success. The Autonomous Nano Technology Swarm (ANTS) mission is an example of one of the swarm types of missions NASA is considering. The ANTS mission will use a swarm of picospacecraft that will fly from Earth orbit to the Asteroid Belt. Using an insect colony analogy, ANTS will be composed of specialized workers for asteroid exploration. Exploration would consist of cataloguing the mass, density, morphology, and chemical composition of the asteroids, including any anomalous concentrations of specific minerals. To perform this task, ANTS would carry miniaturized instruments, such as imagers, spectrometers, and detectors. Since ANTS and other similar missions are going to consist of autonomous spacecraft that may be out of contact with the earth for extended periods of time, and have low bandwidths due to weight constraints, it will be difficult to observe improper behavior and to correct any errors after launch. Providing V&V (verification and validation) for this type of mission is new to NASA, and represents the

Swarming modulatory effects of some amino acids on Proteus ...

African Journals Online (AJOL)

Swarming motility, a multicellular behaviour characterized by periodic concentric growth on solid media has severally been reported as a constraint in the clinical investigation of mixed-culture infections involving Proteus and as a requirement for virulence. While media are being formulated to restrain swarming in this ...

Structural optimization of Au–Pd bimetallic nanoparticles with improved particle swarm optimization method

International Nuclear Information System (INIS)

Shao Gui-Fang; Zhu Meng; Shangguan Ya-Li; Li Wen-Ran; Zhang Can; Wang Wei-Wei; Li Ling

2017-01-01

Due to the dependence of the chemical and physical properties of the bimetallic nanoparticles (NPs) on their structures, a fundamental understanding of their structural characteristics is crucial for their syntheses and wide applications. In this article, a systematical atomic-level investigation of Au–Pd bimetallic NPs is conducted by using the improved particle swarm optimization (IPSO) with quantum correction Sutton–Chen potentials (Q-SC) at different Au/Pd ratios and different sizes. In the IPSO, the simulated annealing is introduced into the classical particle swarm optimization (PSO) to improve the effectiveness and reliability. In addition, the influences of initial structure, particle size and composition on structural stability and structural features are also studied. The simulation results reveal that the initial structures have little effects on the stable structures, but influence the converging rate greatly, and the convergence rate of the mixing initial structure is clearly faster than those of the core-shell and phase structures. We find that the Au–Pd NPs prefer the structures with Au-rich in the outer layers while Pd-rich in the inner ones. Especially, when the Au/Pd ratio is 6:4, the structure of the nanoparticle (NP) presents a standardized Pd core Au shell structure. (paper)

Software Engineering and Swarm-Based Systems

Science.gov (United States)

Hinchey, Michael G.; Sterritt, Roy; Pena, Joaquin; Rouff, Christopher A.

2006-01-01

We discuss two software engineering aspects in the development of complex swarm-based systems. NASA researchers have been investigating various possible concept missions that would greatly advance future space exploration capabilities. The concept mission that we have focused on exploits the principles of autonomic computing as well as being based on the use of intelligent swarms, whereby a (potentially large) number of similar spacecraft collaborate to achieve mission goals. The intent is that such systems not only can be sent to explore remote and harsh environments but also are endowed with greater degrees of protection and longevity to achieve mission goals.

Four-dimensional parameter estimation of plane waves using swarming intelligence

International Nuclear Information System (INIS)

Zaman Fawad; Munir Fahad; Khan Zafar Ullah; Qureshi Ijaz Mansoor

2014-01-01

This paper proposes an efficient approach for four-dimensional (4D) parameter estimation of plane waves impinging on a 2-L shape array. The 4D parameters include amplitude, frequency and the two-dimensional (2D) direction of arrival, namely, azimuth and elevation angles. The proposed approach is based on memetic computation, in which the global optimizer, particle swarm optimization is hybridized with a rapid local search technique, pattern search. For this purpose, a new multi-objective fitness function is used. This fitness function is the combination of mean square error and the correlation between the normalized desired and estimated vectors. The proposed hybrid scheme is not only compared with individual performances of particle swarm optimization and pattern search, but also with the performance of the hybrid genetic algorithm and that of the traditional approach. A large number of Monte—Carlo simulations are carried out to validate the performance of the proposed scheme. It gives promising results in terms of estimation accuracy, convergence rate, proximity effect and robustness against noise. (interdisciplinary physics and related areas of science and technology)

Turbulence modulation induced by interaction between a bubble swarm and decaying turbulence in oscillating-grid turbulence

International Nuclear Information System (INIS)

Imaizumi, Ryota; Morikawa, Koichi; Higuchi, Masamori; Saito, Takayuki

2009-01-01

In this study, the interaction between a bubble swarm and homogeneous isotropic turbulence was experimentally investigated. The objective is to clarify the turbulence modulation induced by interaction between the bubble swarm and the homogeneous isotropic turbulence without mean flow. In order to generate simultaneously ideally homogeneous isotropic turbulence and a sufficiently controlled bubble swarm, we employed both oscillating grid and bubble generators equipped with audio speakers. First, the homogeneous isotropic turbulence was formed by operating the oscillating grid cylindrical acrylic pipe (height: 600 mm, inner diameter: 149 mm) filled with ion-exchanged and degassed water. Second, we stopped the oscillating-grid in arbitrary time after the homogeneous isotropic turbulence was achieved. A few moments later, the controlled bubble swarm (number of bubbles: 3, average equivalent diameter of bubble: 3 mm, bubble Reynolds number: 859, Weber number: 3.48) was launched into the decaying turbulence described above, using the bubble generators. The bubble formation, bubble size and bubble-launch timing are controlled arbitrarily and precisely by this device. In this study, we conducted the following experiments: 1) measurement of the motion of bubbles in rest water and oscillating grid turbulence via high-speed visualization, 2) measurement of the liquid phase motion around the bubbles in rest water via PIV system with LIF method, 3) measurement of the liquid phase motion around the bubbles in oscillating-grid turbulence via PIV system with LIF method. In the vitalization of the liquid-phase motion of both experiments, two high speed video cameras were employed in order to simultaneously film large- and small-scale interrogation areas. The liquid-phase ambient turbulence hastened the change of the bubble motion from zigzag mode to spiral mode. The interaction between the bubble swarm and liquid-phase turbulence increased decay-rate of the turbulence. (author)

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".

Phase Coexistence in Insect Swarms

Science.gov (United States)

Sinhuber, Michael; Ouellette, Nicholas T.

2017-10-01

Animal aggregations are visually striking, and as such are popular examples of collective behavior in the natural world. Quantitatively demonstrating the collective nature of such groups, however, remains surprisingly difficult. Inspired by thermodynamics, we applied topological data analysis to laboratory insect swarms and found evidence for emergent, material-like states. We show that the swarms consist of a core "condensed" phase surrounded by a dilute "vapor" phase. These two phases coexist in equilibrium, and maintain their distinct macroscopic properties even though individual insects pass freely between them. We further define a pressure and chemical potential to describe these phases, extending theories of active matter to aggregations of macroscopic animals and laying the groundwork for a thermodynamic description of collective animal groups.

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.

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 Robotics with Circular Formation Motion Including Obstacles Avoidance

Directory of Open Access Journals (Sweden)

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.

Particle swarm optimization algorithm for simultaneous optimal placement and sizing of shunt active power conditioner (APC) and shunt capacitor inharmonic distorted distribution system

Institute of Scientific and Technical Information of China (English)

Mohammadi Mohammad

2017-01-01

Due to development of distribution systems and increase in electricity demand, the use of capacitor banks increases. From the other point of view, nonlinear loads generate and inject considerable harmonic currents into power system. Under this condition if capacitor banks are not properly selected and placed in the power system, they could amplify and propagate these harmonics and deteriorate power quality to unacceptable levels. With attention of disadvantages of passive filters, such as occurring resonance, nowadays the usage of this type of harmonic compensator is restricted. On the other side, one of parallel multi-function compensating devices which are recently used in distribution system to mitigate voltage sag and harmonic distortion, performs power factor correction, and improves the overall power quality as active power conditioner (APC). Therefore, the utilization of APC in harmonic distorted system can affect and change the optimal location and size of shunt capacitor bank under harmonic distortion condition. This paper presents an optimization algorithm for improvement of power quality using simultaneous optimal placement and sizing of APC and shunt capacitor banks in radial distribution networks in the presence of voltage and current harmonics. The algorithm is based on particle swarm optimization (PSO). The objective function includes the cost of power losses, energy losses and those of the capacitor banks and APCs.

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

Level-2 product generation for the Swarm satellite constellation mission

DEFF Research Database (Denmark)

Olsen, Poul Erik Holmdahl; Tøffner-Clausen, Lars; Olsen, Nils

In order to take advantage of the unique constellation aspect of ESA's Swarm constellation mission, considerably advanced data analysis tools have been developed. The Swarm ESL/SCARF (Satellite Constellation Application and Research Facility), a consortium of several research institutions, derives...

A Distributed Particle Swarm Optimization Zlgorithmfor Flexible Job-hop Scheduling Problem

Directory of Open Access Journals (Sweden)

LIU Sheng--hui

2017-06-01

Full Text Available According to the characteristics of the Flexible job shop scheduling problem the minimum makespan as measures we proposed a distributed particle swarm optimization algorithm aiming to solve flexible job shop scheduling problem. The algorithm adopts the method of distributed ideas to solve problems and we are established for two multi agent particle swarm optimization model in this algorithm it can solve the traditional particle swarm optimization algorithm when making decisions in real time according to the emergencies. Finally some benthmark problems were experimented and the results are compared with the traditional algorithm. Experimental results proved that the developed distributed PSO is enough effective and efficient to solve the FJSP and it also verified the reasonableness of the multi}gent particle swarm optimization model.

Sizing an isolated wind-solar-fuel cell generation system based on the particle swarm optimization method; Dimensionamiento de un sistema de generacion aislado eolico-solar-celda de combustible basado en el metodo de optimizacion de enjambre de particulas

Energy Technology Data Exchange (ETDEWEB)

Sanchez-Huerta, V; Ramirez-Arredondo, Juan M. [Universidad de Quintana Roo, Chetumal, Quintana Roo (Mexico)]. E-mail: vsanchez@gdl.cinvestav.mx; Arriaga-Hurtado, L. G. [CIDETEQ, Queretaro (Mexico)

2009-09-15

Sizing an electric energy system requires an analysis of investment, maintenance and operating costs. In the case of a generation system that uses renewable sources, optimal capacity becomes more complex compared to a conventional system, because of the randomness of renewable resources (wind, solar) and the still high costs of wind and photovoltage generator modules. This work presents the optimal sizing of a wind-solar-fuel cell generation system, minimizing the costs of the system while satisfying the energy demands of an isolated charge. The optimization method used is based on an evolutionary programming technique known as particle swarms (PSO-particle swarm optimization). The generation of energy with a hybrid system is discussed, based on the profile of insolation and wind availability at the site, with the objective of satisfying a specific electric demand. [Spanish] El dimensionamiento de un sistema de generacion de energia electrica requiere un analisis de los costos de inversion, mantenimiento y operacion. En el caso de un sistema de generacion que utiliza fuentes renovables la capacidad optima resulta mas compleja con respecto a un sistema convencional, debido a la aleatoriedad de los recursos renovables (eolico, solar), y a los aun altos costos de generadores eolicos y modulos fotovoltaicos. En este trabajo se presenta el dimensionamiento optimo de un sistema de generacion eolico-solar-celda de combustible minimizando los costos del sistema que satisfaga la energia demandada por una carga aislada. El metodo de optimizacion utilizado esta basado en una tecnica de programacion evolutiva conocida como enjambre de particulas (PSO por sus siglas en ingles: particle swarm optimization). Se plantea la generacion de energia del sistema hibrido con base a la insolacion y el perfil del viento disponible en sitio, con objeto de satisfacer una demanda electrica determinada.

behaved particle swarm optimization (QPSO)

African Journals Online (AJOL)

Administrator

2011-06-13

Jun 13, 2011 ... experiment results of L-glutamic acid fermentation process showed that our ... Key words: Soft-sensing model, quantum-behaved particle swarm optimization ... information about such biochemical variables is, in most practical ...

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).

Direct Interval Forecasting of Wind Power

DEFF Research Database (Denmark)

Wan, Can; Xu, Zhao; Pinson, Pierre

2013-01-01

This letter proposes a novel approach to directly formulate the prediction intervals of wind power generation based on extreme learning machine and particle swarm optimization, where prediction intervals are generated through direct optimization of both the coverage probability and sharpness...

Propulsion Trade Studies for Spacecraft Swarm Mission Design

Science.gov (United States)

Dono, Andres; Plice, Laura; Mueting, Joel; Conn, Tracie; Ho, Michael

2018-01-01

Spacecraft swarms constitute a challenge from an orbital mechanics standpoint. Traditional mission design involves the application of methodical processes where predefined maneuvers for an individual spacecraft are planned in advance. This approach does not scale to spacecraft swarms consisting of many satellites orbiting in close proximity; non-deterministic maneuvers cannot be preplanned due to the large number of units and the uncertainties associated with their differential deployment and orbital motion. For autonomous small sat swarms in LEO, we investigate two approaches for controlling the relative motion of a swarm. The first method involves modified miniature phasing maneuvers, where maneuvers are prescribed that cancel the differential delta V of each CubeSat's deployment vector. The second method relies on artificial potential functions (APFs) to contain the spacecraft within a volumetric boundary and avoid collisions. Performance results and required delta V budgets are summarized, indicating that each method has advantages and drawbacks for particular applications. The mini phasing maneuvers are more predictable and sustainable. The APF approach provides a more responsive and distributed performance, but at considerable propellant cost. After considering current state of the art CubeSat propulsion systems, we conclude that the first approach is feasible, but the modified APF method of requires too much control authority to be enabled by current propulsion systems.

Exopolysaccharides play a role in the swarming of the benthic bacterium Pseudoalteromonas sp. SM9913

Directory of Open Access Journals (Sweden)

Ang eLiu

2016-04-01

Full Text Available Most marine bacteria secrete exopolysaccharide (EPS, which is important for bacterial survival in the marine environment. However, it is still unclear whether the self-secreted EPS is involved in marine bacterial motility. Here we studied the role of EPS in the lateral flagella-driven swarming motility of benthic bacterium Pseudoalteromonas sp. SM9913 (SM9913 by a comparison of wild SM9913 and ΔepsT, an EPS synthesis defective mutant. Reduction of EPS production in ΔepsT did not affect the growth rate or the swimming motility, but significantly decreased the swarming motility on a swarming plate, suggesting that the EPS may play a role in SM9913 swarming. However, the expression and assembly of lateral flagella in ΔepsT were not affected. Instead, ΔepsT had a different swarming behavior from wild SM9913. The swarming of ΔepsT did not have an obvious rapid swarming period, and its rate became much lower than that of wild SM9913 after 35 h incubation. An addition of surfactin or SM9913 EPS on the surface of the swarming plate could rescue the swarming level. These results indicate that the self-secreted EPS is required for the swarming of SM9913. This study widens our understanding of the function of the EPS of benthic bacteria.

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.

Recent advances in swarm intelligence and evolutionary computation

CERN Document Server

2015-01-01

This timely review volume summarizes the state-of-the-art developments in nature-inspired algorithms and applications with the emphasis on swarm intelligence and bio-inspired computation. Topics include the analysis and overview of swarm intelligence and evolutionary computation, hybrid metaheuristic algorithms, bat algorithm, discrete cuckoo search, firefly algorithm, particle swarm optimization, and harmony search as well as convergent hybridization. Application case studies have focused on the dehydration of fruits and vegetables by the firefly algorithm and goal programming, feature selection by the binary flower pollination algorithm, job shop scheduling, single row facility layout optimization, training of feed-forward neural networks, damage and stiffness identification, synthesis of cross-ambiguity functions by the bat algorithm, web document clustering, truss analysis, water distribution networks, sustainable building designs and others. As a timely review, this book can serve as an ideal reference f...

Loss of FliL alters Proteus mirabilis surface sensing and temperature-dependent swarming.

Science.gov (United States)

Lee, Yi-Ying; Belas, Robert

2015-01-01

Proteus mirabilis is a dimorphic motile bacterium well known for its flagellum-dependent swarming motility over surfaces. In liquid, P. mirabilis cells are 1.5- to 2.0-μm swimmer cells with 4 to 6 flagella. When P. mirabilis encounters a solid surface, where flagellar rotation is limited, swimmer cells differentiate into elongated (10- to 80-μm), highly flagellated swarmer cells. In order for P. mirabilis to swarm, it first needs to detect a surface. The ubiquitous but functionally enigmatic flagellar basal body protein FliL is involved in P. mirabilis surface sensing. Previous studies have suggested that FliL is essential for swarming through its involvement in viscosity-dependent monitoring of flagellar rotation. In this study, we constructed and characterized ΔfliL mutants of P. mirabilis and Escherichia coli. Unexpectedly and unlike other fliL mutants, both P. mirabilis and E. coli ΔfliL cells swarm (Swr(+)). Further analysis revealed that P. mirabilis ΔfliL cells also exhibit an alteration in their ability to sense a surface: e.g., ΔfliL P. mirabilis cells swarm precociously over surfaces with low viscosity that normally impede wild-type swarming. Precocious swarming is due to an increase in the number of elongated swarmer cells in the population. Loss of fliL also results in an inhibition of swarming at <30°C. E. coli ΔfliL cells also exhibit temperature-sensitive swarming. These results suggest an involvement of FliL in the energetics and function of the flagellar motor. Copyright © 2015, American Society for Microbiology. All Rights Reserved.

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

2014 Mainshock-Aftershock Activity Versus Earthquake Swarms in West Bohemia, Czech Republic

Science.gov (United States)

Jakoubková, Hana; Horálek, Josef; Fischer, Tomáš

2018-01-01

A singular sequence of three episodes of ML3.5, 4.4 and 3.6 mainshock-aftershock occurred in the West Bohemia/Vogtland earthquake-swarm region during 2014. We analysed this activity using the WEBNET data and compared it with the swarms of 1997, 2000, 2008 and 2011 from the perspective of cumulative seismic moment, statistical characteristics, space-time distribution of events, and prevailing focal mechanisms. For this purpose, we improved the scaling relation between seismic moment M0 and local magnitude ML by WEBNET. The total seismic moment released during 2014 episodes (M_{0tot}≈ 1.58× 10^{15} Nm) corresponded to a single ML4.6+ event and was comparable to M_{0tot} of the swarms of 2000, 2008 and 2011. We inferred that the ML4.8 earthquake is the maximum expected event in Nový Kostel (NK), the main focal zone. Despite the different character of the 2014 sequence and the earthquake swarms, the magnitude-frequency distributions (MFDs) show the b-values ≈ 1 and probability density functions (PDFs) of the interevent times indicate the similar event rate of the individual swarms and 2014 activity. Only the a-value (event-productivity) in the MFD of the 2014 sequence is significantly lower than those of the swarms. A notable finding is a significant acceleration of the seismic moment release in each subsequent activity starting from the 2000 swarm to the 2014 sequence, which may indicate an alteration from the swarm-like to the mainshocks-aftershock character of the seismicity. The three mainshocks are located on a newly activated fault segment/asperity (D in out notation) of the NK zone situated in the transition area among fault segments A, B, C, which hosted the 2000, 2008 and 2011 swarms. The segment D appears to be predisposed to an oblique-thrust faulting while strike-slip faulting is typical of segments A, B and C. In conclusion, we propose a basic segment scheme of the NK zone which should be improved gradually.

Applying Sequential Particle Swarm Optimization Algorithm to Improve Power Generation Quality

Directory of Open Access Journals (Sweden)

Abdulhafid Sallama

2014-10-01

Full Text Available Swarm Optimization approach is a heuristic search method whose mechanics are inspired by the swarming or collaborative behaviour of biological populations. It is used to solve constrained, unconstrained, continuous and discrete problems. Swarm intelligence systems are widely used and very effective in solving standard and large-scale optimization, provided that the problem does not require multi solutions. In this paper, particle swarm optimisation technique is used to optimise fuzzy logic controller (FLC for stabilising a power generation and distribution network that consists of four generators. The system is subject to different types of faults (single and multi-phase. Simulation studies show that the optimised FLC performs well in stabilising the network after it recovers from a fault. The controller is compared to multi-band and standard controllers.

Optimized Placement of Wind Turbines in Large-Scale Offshore Wind Farm using Particle Swarm Optimization Algorithm

DEFF Research Database (Denmark)

Hou, Peng; Hu, Weihao; Soltani, Mohsen

2015-01-01

With the increasing size of wind farm, the impact of the wake effect on wind farm energy yields become more and more evident. The arrangement of the wind turbines’ (WT) locations will influence the capital investment and contribute to the wake losses which incur the reduction of energy production....... As a consequence, the optimized placement of the wind turbines may be done by considering the wake effect as well as the components cost within the wind farm. In this paper, a mathematical model which includes the variation of both wind direction and wake deficit is proposed. The problem is formulated by using...... Levelized Production Cost (LPC) as the objective function. The optimization procedure is performed by Particle Swarm Optimization (PSO) algorithm with the purpose of maximizing the energy yields while minimizing the total investment. The simulation results indicate that the proposed method is effective...

Mechanism of the 1996-97 non-eruptive volcano-tectonic earthquake swarm at Iliamna Volcano, Alaska

Science.gov (United States)

Roman, D.C.; Power, J.A.

2011-01-01

A significant number of volcano-tectonic(VT) earthquake swarms, some of which are accompanied by ground deformation and/or volcanic gas emissions, do not culminate in an eruption.These swarms are often thought to represent stalled intrusions of magma into the mid- or shallow-level crust.Real-time assessment of the likelihood that a VTswarm will culminate in an eruption is one of the key challenges of volcano monitoring, and retrospective analysis of non-eruptive swarms provides an important framework for future assessments. Here we explore models for a non-eruptive VT earthquake swarm located beneath Iliamna Volcano, Alaska, in May 1996-June 1997 through calculation and inversion of fault-plane solutions for swarm and background periods, and through Coulomb stress modeling of faulting types and hypocenter locations observed during the swarm. Through a comparison of models of deep and shallow intrusions to swarm observations,we aim to test the hypothesis that the 1996-97 swarm represented a shallow intrusion, or "failed" eruption.Observations of the 1996-97 swarm are found to be consistent with several scenarios including both shallow and deep intrusion, most likely involving a relatively small volume of intruded magma and/or a low degree of magma pressurization corresponding to a relatively low likelihood of eruption. ?? 2011 Springer-Verlag.

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

The Swarm Initial Field Model for the 2014 Geomagnetic Field

Science.gov (United States)

Olsen, Nils; Hulot, Gauthier; Lesur, Vincent; Finlay, Christopher C.; Beggan, Ciaran; Chulliat, Arnaud; Sabaka, Terence J.; Floberghagen, Rune; Friis-Christensen, Eigil; Haagmans, Roger

2015-01-01

Data from the first year of ESA's Swarm constellation mission are used to derive the Swarm Initial Field Model (SIFM), a new model of the Earth's magnetic field and its time variation. In addition to the conventional magnetic field observations provided by each of the three Swarm satellites, explicit advantage is taken of the constellation aspect by including east-west magnetic intensity gradient information from the lower satellite pair. Along-track differences in magnetic intensity provide further information concerning the north-south gradient. The SIFM static field shows excellent agreement (up to at least degree 60) with recent field models derived from CHAMP data, providing an initial validation of the quality of the Swarm magnetic measurements. Use of gradient data improves the determination of both the static field and its secular variation, with the mean misfit for east-west intensity differences between the lower satellite pair being only 0.12 nT.

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

KAUST Repository

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

2017-01-01

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

Magma Reservoirs Feeding Giant Radiating Dike Swarms: Insights from Venus

Science.gov (United States)

Grosfils, E. B.; Ernst, R. E.

2003-01-01

Evidence of lateral dike propagation from shallow magma reservoirs is quite common on the terrestrial planets, and examination of the giant radiating dike swarm population on Venus continues to provide new insight into the way these complex magmatic systems form and evolve. For example, it is becoming clear that many swarms are an amalgamation of multiple discrete phases of dike intrusion. This is not surprising in and of itself, as on Earth there is clear evidence that formation of both magma reservoirs and individual giant radiating dikes often involves periodic magma injection. Similarly, giant radiating swarms on Earth can contain temporally discrete subswarms defined on the basis of geometry, crosscutting relationships, and geochemical or paleomagnetic signatures. The Venus data are important, however, because erosion, sedimentation, plate tectonic disruption, etc. on Earth have destroyed most giant radiating dike swarm's source regions, and thus we remain uncertain about the geometry and temporal evolution of the magma sources from which the dikes are fed. Are the reservoirs which feed the dikes large or small, and what are the implications for how the dikes themselves form? Does each subswarm originate from a single, periodically reactivated reservoir, or do subswarms emerge from multiple discrete geographic foci? If the latter, are these discrete foci located at the margins of a single large magma body, or do multiple smaller reservoirs define the character of the magmatic center as a whole? Similarly, does the locus of magmatic activity change with time, or are all the foci active simultaneously? Careful study of giant radiating dike swarms on Venus is yielding the data necessary to address these questions and constrain future modeling efforts. Here, using giant radiating dike swarms from the Nemesis Tessera (V14) and Carson (V43) quadrangles as examples, we illustrate some of the dike swarm focal region diversity observed on Venus and briefly explore some

Adaptive Fuzzy-Lyapunov Controller Using Biologically Inspired Swarm Intelligence

Directory of Open Access Journals (Sweden)

Alejandro Carrasco Elizalde

2008-01-01

Full Text Available The collective behaviour of swarms produces smarter actions than those achieved by a single individual. Colonies of ants, flocks of birds and fish schools are examples of swarms interacting with their environment to achieve a common goal. This cooperative biological intelligence is the inspiration for an adaptive fuzzy controller developed in this paper. Swarm intelligence is used to adjust the parameters of the membership functions used in the adaptive fuzzy controller. The rules of the controller are designed using a computing-with-words approach called Fuzzy-Lyapunov synthesis to improve the stability and robustness of an adaptive fuzzy controller. Computing-with-words provides a powerful tool to manipulate numbers and symbols, like words in a natural language.

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.

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.

Multi-objective swarm intelligence theoretical advances and applications

CERN Document Server

Jagadev, Alok; Panda, Mrutyunjaya

2015-01-01

The aim of this book is to understand the state-of-the-art theoretical and practical advances of swarm intelligence. It comprises seven contemporary relevant chapters. In chapter 1, a review of Bacteria Foraging Optimization (BFO) techniques for both single and multiple criterions problem is presented. A survey on swarm intelligence for multiple and many objectives optimization is presented in chapter 2 along with a topical study on EEG signal analysis. Without compromising the extensive simulation study, a comparative study of variants of MOPSO is provided in chapter 3. Intractable problems like subset and job scheduling problems are discussed in chapters 4 and 7 by different hybrid swarm intelligence techniques. An attempt to study image enhancement by ant colony optimization is made in chapter 5. Finally, chapter 7 covers the aspect of uncertainty in data by hybrid PSO.       

A Novel Distributed Quantum-Behaved Particle Swarm Optimization

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Yangyang Li

2017-01-01

Full Text Available Quantum-behaved particle swarm optimization (QPSO is an improved version of particle swarm optimization (PSO and has shown superior performance on many optimization problems. But for now, it may not always satisfy the situations. Nowadays, problems become larger and more complex, and most serial optimization algorithms cannot deal with the problem or need plenty of computing cost. Fortunately, as an effective model in dealing with problems with big data which need huge computation, MapReduce has been widely used in many areas. In this paper, we implement QPSO on MapReduce model and propose MapReduce quantum-behaved particle swarm optimization (MRQPSO which achieves parallel and distributed QPSO. Comparisons are made between MRQPSO and QPSO on some test problems and nonlinear equation systems. The results show that MRQPSO could complete computing task with less time. Meanwhile, from the view of optimization performance, MRQPSO outperforms QPSO in many cases.

Swarming and complex pattern formation in Paenibacillus vortex studied by imaging and tracking cells

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Jacob Eshel

2008-02-01

Full Text Available Abstract Background Swarming motility allows microorganisms to move rapidly over surfaces. The Gram-positive bacterium Paenibacillus vortex exhibits advanced cooperative motility on agar plates resulting in intricate colonial patterns with geometries that are highly sensitive to the environment. The cellular mechanisms that underpin the complex multicellular organization of such a simple organism are not well understood. Results Swarming by P. vortex was studied by real-time light microscopy, by in situ scanning electron microscopy and by tracking the spread of antibiotic-resistant cells within antibiotic-sensitive colonies. When swarming, P. vortex was found to be peritrichously flagellated. Swarming by the curved cells of P. vortex occurred on an extremely wide range of media and agar concentrations (0.3 to 2.2% w/v. At high agar concentrations (> 1% w/v rotating colonies formed that could be detached from the main mass of cells by withdrawal of cells into the latter. On lower percentage agars, cells moved in an extended network composed of interconnected "snakes" with short-term collision avoidance and sensitivity to extracts from swarming cells. P. vortex formed single Petri dish-wide "supercolonies" with a colony-wide exchange of motile cells. Swarming cells were coupled by rapidly forming, reversible and non-rigid connections to form a loose raft, apparently connected via flagella. Inhibitors of swarming (p-Nitrophenylglycerol and Congo Red were identified. Mitomycin C was used to trigger filamentation without inhibiting growth or swarming; this facilitated dissection of the detail of swarming. Mitomycin C treatment resulted in malcoordinated swarming and abortive side branch formation and a strong tendency by a subpopulation of the cells to form minimal rotating aggregates of only a few cells. Conclusion P. vortex creates complex macroscopic colonies within which there is considerable reflux and movement and interaction of cells. Cell

Particle Swarm-Based Translation Control for Immersed Tunnel Element in the Hong Kong-Zhuhai-Macao Bridge Project

Science.gov (United States)

Li, Jun-jun; Yang, Xiao-jun; Xiao, Ying-jie; Xu, Bo-wei; Wu, Hua-feng

2018-03-01

Immersed tunnel is an important part of the Hong Kong-Zhuhai-Macao Bridge (HZMB) project. In immersed tunnel floating, translation which includes straight and transverse movements is the main working mode. To decide the magnitude and direction of the towing force for each tug, a particle swarm-based translation control method is presented for non-power immersed tunnel element. A sort of linear weighted logarithmic function is exploited to avoid weak subgoals. In simulation, the particle swarm-based control method is evaluated and compared with traditional empirical method in the case of the HZMB project. Simulation results show that the presented method delivers performance improvement in terms of the enhanced surplus towing force.

Reserve-Constrained Multiarea Environmental/Economic Dispatch Using Enhanced Particle Swarm Optimization

OpenAIRE

Wang, Lingfeng; Singh, Chanan

2007-01-01

Source: Swarm Intelligence: Focus on Ant and Particle Swarm Optimization, Book edited by: Felix T. S. Chan and Manoj Kumar Tiwari, ISBN 978-3-902613-09-7, pp. 532, December 2007, Itech Education and Publishing, Vienna, Austria

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.

Time-variable gravity fields and ocean mass change from 37 months of kinematic Swarm orbits

Science.gov (United States)

Lück, Christina; Kusche, Jürgen; Rietbroek, Roelof; Löcher, Anno

2018-03-01

Measuring the spatiotemporal variation of ocean mass allows for partitioning of volumetric sea level change, sampled by radar altimeters, into mass-driven and steric parts. The latter is related to ocean heat change and the current Earth's energy imbalance. Since 2002, the Gravity Recovery and Climate Experiment (GRACE) mission has provided monthly snapshots of the Earth's time-variable gravity field, from which one can derive ocean mass variability. However, GRACE has reached the end of its lifetime with data degradation and several gaps occurred during the last years, and there will be a prolonged gap until the launch of the follow-on mission GRACE-FO. Therefore, efforts focus on generating a long and consistent ocean mass time series by analyzing kinematic orbits from other low-flying satellites, i.e. extending the GRACE time series. Here we utilize data from the European Space Agency's (ESA) Swarm Earth Explorer satellites to derive and investigate ocean mass variations. For this aim, we use the integral equation approach with short arcs (Mayer-Gürr, 2006) to compute more than 500 time-variable gravity fields with different parameterizations from kinematic orbits. We investigate the potential to bridge the gap between the GRACE and the GRACE-FO mission and to substitute missing monthly solutions with Swarm results of significantly lower resolution. Our monthly Swarm solutions have a root mean square error (RMSE) of 4.0 mm with respect to GRACE, whereas directly estimating constant, trend, annual, and semiannual (CTAS) signal terms leads to an RMSE of only 1.7 mm. Concerning monthly gaps, our CTAS Swarm solution appears better than interpolating existing GRACE data in 13.5 % of all cases, when artificially removing one solution. In the case of an 18-month artificial gap, 80.0 % of all CTAS Swarm solutions were found closer to the observed GRACE data compared to interpolated GRACE data. Furthermore, we show that precise modeling of non-gravitational forces

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.

A Dynamic Multistage Hybrid Swarm Intelligence Optimization Algorithm for Function Optimization

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Daqing Wu

2012-01-01

Full Text Available A novel dynamic multistage hybrid swarm intelligence optimization algorithm is introduced, which is abbreviated as DM-PSO-ABC. The DM-PSO-ABC combined the exploration capabilities of the dynamic multiswarm particle swarm optimizer (PSO and the stochastic exploitation of the cooperative artificial bee colony algorithm (CABC for solving the function optimization. In the proposed hybrid algorithm, the whole process is divided into three stages. In the first stage, a dynamic multiswarm PSO is constructed to maintain the population diversity. In the second stage, the parallel, positive feedback of CABC was implemented in each small swarm. In the third stage, we make use of the particle swarm optimization global model, which has a faster convergence speed to enhance the global convergence in solving the whole problem. To verify the effectiveness and efficiency of the proposed hybrid algorithm, various scale benchmark problems are tested to demonstrate the potential of the proposed multistage hybrid swarm intelligence optimization algorithm. The results show that DM-PSO-ABC is better in the search precision, and convergence property and has strong ability to escape from the local suboptima when compared with several other peer algorithms.

The Dienes phenomenon: competition and territoriality in Swarming Proteus mirabilis

NARCIS (Netherlands)

Budding, A. E.; Ingham, C. J.; Bitter, W.; Vandenbroucke-Grauls, C. M.; Schneeberger, P. M.

2009-01-01

When two different strains of swarming Proteus mirabilis encounter one another on an agar plate, swarming ceases and a visible line of demarcation forms. This boundary region is known as the Dienes line and is associated with the formation of rounded cells. While the Dienes line appears to be the

Swarm robotics and complex behaviour of continuum material

Science.gov (United States)

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.

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

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.

In-flight scalar calibration and characterisation of the Swarm magnetometry package

DEFF Research Database (Denmark)

Tøffner-Clausen, Lars; Lesur, Vincent; Olsen, Nils

2016-01-01

of magnetometers is demonstrated, confirming the high performance of these instruments. The results presented here, including the characterisation of a Sun-driven disturbance field, form the basis of the correction of the magnetic vector measurements from Swarm which is applied to the Swarm Level 1b magnetic data.......We present the in-flight scalar calibration and characterisation of the Swarm magnetometry package consisting of the absolute scalar magnetometer, the vector magnetometer, and the spacecraft structure supporting the instruments. A significant improvement in the scalar residuals between the pairs...

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.

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.

Epidemic Synchronization in Robotic Swarms

DEFF Research Database (Denmark)

Schiøler, Henrik; Nielsen, Jens Frederik Dalsgaard; Ngo, Trung Dung

2009-01-01

Clock synchronization in swarms of networked mobile robots is studied in a probabilistic, epidemic framework. In this setting communication and synchonization is considered to be a randomized process, taking place at unplanned instants of geographical rendezvous between robots. In combination wit...

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.

Extending Particle Swarm Optimisers with Self-Organized Criticality

DEFF Research Database (Denmark)

Løvbjerg, Morten; Krink, Thiemo

2002-01-01

Particle swarm optimisers (PSOs) show potential in function optimisation, but still have room for improvement. Self-organized criticality (SOC) can help control the PSO and add diversity. Extending the PSO with SOC seems promising reaching faster convergence and better solutions.......Particle swarm optimisers (PSOs) show potential in function optimisation, but still have room for improvement. Self-organized criticality (SOC) can help control the PSO and add diversity. Extending the PSO with SOC seems promising reaching faster convergence and better solutions....

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

An Improved Quantum-Behaved Particle Swarm Optimization Algorithm with Elitist Breeding for Unconstrained Optimization.

Science.gov (United States)

Yang, Zhen-Lun; Wu, Angus; Min, Hua-Qing

2015-01-01

An improved quantum-behaved particle swarm optimization with elitist breeding (EB-QPSO) for unconstrained optimization is presented and empirically studied in this paper. In EB-QPSO, the novel elitist breeding strategy acts on the elitists of the swarm to escape from the likely local optima and guide the swarm to perform more efficient search. During the iterative optimization process of EB-QPSO, when criteria met, the personal best of each particle and the global best of the swarm are used to generate new diverse individuals through the transposon operators. The new generated individuals with better fitness are selected to be the new personal best particles and global best particle to guide the swarm for further solution exploration. A comprehensive simulation study is conducted on a set of twelve benchmark functions. Compared with five state-of-the-art quantum-behaved particle swarm optimization algorithms, the proposed EB-QPSO performs more competitively in all of the benchmark functions in terms of better global search capability and faster convergence rate.

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...

A Novel Particle Swarm Optimization Algorithm for Global Optimization.

Science.gov (United States)

Wang, Chun-Feng; Liu, Kui

2016-01-01

Particle Swarm Optimization (PSO) is a recently developed optimization method, which has attracted interest of researchers in various areas due to its simplicity and effectiveness, and many variants have been proposed. In this paper, a novel Particle Swarm Optimization algorithm is presented, in which the information of the best neighbor of each particle and the best particle of the entire population in the current iteration is considered. Meanwhile, to avoid premature, an abandoned mechanism is used. Furthermore, for improving the global convergence speed of our algorithm, a chaotic search is adopted in the best solution of the current iteration. To verify the performance of our algorithm, standard test functions have been employed. The experimental results show that the algorithm is much more robust and efficient than some existing Particle Swarm Optimization algorithms.

Cultural-based particle swarm for dynamic optimisation problems

Science.gov (United States)

Daneshyari, Moayed; Yen, Gary G.

2012-07-01

Many practical optimisation problems are with the existence of uncertainties, among which a significant number belong to the dynamic optimisation problem (DOP) category in which the fitness function changes through time. In this study, we propose the cultural-based particle swarm optimisation (PSO) to solve DOP problems. A cultural framework is adopted incorporating the required information from the PSO into five sections of the belief space, namely situational, temporal, domain, normative and spatial knowledge. The stored information will be adopted to detect the changes in the environment and assists response to the change through a diversity-based repulsion among particles and migration among swarms in the population space, and also helps in selecting the leading particles in three different levels, personal, swarm and global levels. Comparison of the proposed heuristics over several difficult dynamic benchmark problems demonstrates the better or equal performance with respect to most of other selected state-of-the-art dynamic PSO heuristics.

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

Swarm prevention and spring treatment against Varroa destructor in honey bee colonies (Apis mellifera)

NARCIS (Netherlands)

Cornelissen, B.; Gerritsen, L.J.M.

2006-01-01

In 2004 and 2005 experiments were carried out to test the efficacy and efficiency of Varroa control combined with swarm prevention methods in spring. Honey bee colonies were split in an artificial swarm and a brood carrier. Hereafter the swarms were treated with oxalic acid and the brood carriers

A practical approach for solving multi-objective reliability redundancy allocation problems using extended bare-bones particle swarm optimization

International Nuclear Information System (INIS)

Zhang, Enze; Wu, Yifei; Chen, Qingwei

2014-01-01

This paper proposes a practical approach, combining bare-bones particle swarm optimization and sensitivity-based clustering for solving multi-objective reliability redundancy allocation problems (RAPs). A two-stage process is performed to identify promising solutions. Specifically, a new bare-bones multi-objective particle swarm optimization algorithm (BBMOPSO) is developed and applied in the first stage to identify a Pareto-optimal set. This algorithm mainly differs from other multi-objective particle swarm optimization algorithms in the parameter-free particle updating strategy, which is especially suitable for handling the complexity and nonlinearity of RAPs. Moreover, by utilizing an approach based on the adaptive grid to update the global particle leaders, a mutation operator to improve the exploration ability and an effective constraint handling strategy, the integrated BBMOPSO algorithm can generate excellent approximation of the true Pareto-optimal front for RAPs. This is followed by a data clustering technique based on difference sensitivity in the second stage to prune the obtained Pareto-optimal set and obtain a small, workable sized set of promising solutions for system implementation. Two illustrative examples are presented to show the feasibility and effectiveness of the proposed approach

The Study of Fuzzy Proportional Integral Controllers Based on Improved Particle Swarm Optimization for Permanent Magnet Direct Drive Wind Turbine Converters

Directory of Open Access Journals (Sweden)

Yancai Xiao

2016-05-01

Full Text Available In order to meet the requirements of high precision and fast response of permanent magnet direct drive (PMDD wind turbines, this paper proposes a fuzzy proportional integral (PI controller associated with a new control strategy for wind turbine converters. The purpose of the control strategy is to achieve the global optimization for the quantization factors, ke and kec, and scale factors, kup and kui, of the fuzzy PI controller by an improved particle swarm optimization (PSO method. Thus the advantages of the rapidity of the improved PSO and the robustness of the fuzzy controller can be fully applied in the control process. By conducting simulations for 2 MW PMDD wind turbines with Matlab/Simulink, the performance of the fuzzy PI controller based on the improved PSO is demonstrated to be obviously better than that of the PI controller or the fuzzy PI controller without using the improved PSO under the situation when the wind speed changes suddenly.

UAV Swarming? So What are Those Swarms, What are the Implications, and How Do We Handle Them?

National Research Council Canada - National Science Library

Clough, Bruce

2002-01-01

... not. The aerospace research community is working hard at developing UAV control technology that requires as little human supervision as possible, and concepts using swarms are receiving serious attention...

Changes in Earth's core-generated magnetic field, as observed by Swarm

DEFF Research Database (Denmark)

Finlay, Chris; Olsen, Nils; Gillet, Nicolas

By far the largest part of the Earth's magnetic field is generated by motions taking place within our planet's liquid metal outer core. Variations of this core-generated field thus provide us with a unique means of probing the dynamics taking place in the deepest reaches of the Earth....... In this contribution, we will present the core-generated magnetic field, and its recent time changes, as seen by ESA's Earth explorer mission Swarm. We will present a new time-dependent geomagnetic field model, called CHAOS-6, derived from satellite data collected by the Swarm constellation, as well as data from...... the previous missions CHAMP and Oersted together with ground observatory data. Advantage is taken of the constellation aspect of the Swarm mission by ingesting field differences along track and across track between the lower pair of Swarm satellites. Evaluating the global field model at the outer boundary...

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.

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

Directory of Open Access Journals (Sweden)

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.

Visualization of Biosurfactant Film Flow in a Bacillus subtilis Swarm Colony on an Agar Plate.

Science.gov (United States)

Kim, Kyunghoon; Kim, Jung Kyung

2015-08-26

Collective bacterial dynamics plays a crucial role in colony development. Although many research groups have studied the behavior of fluidic swarm colonies, the detailed mechanics of its motion remains elusive. Here, we developed a visualization method using submicron fluorescent beads for investigating the flow field in a thin layer of fluid that covers a Bacillus subtilis swarm colony growing on an agar plate. The beads were initially embedded in the agar plate and subsequently distributed spontaneously at the upper surface of the expanding colony. We conducted long-term live cell imaging of the B. subtilis colony using the fluorescent tracers, and obtained high-resolution velocity maps of microscale vortices in the swarm colony using particle image velocimetry. A distinct periodic fluctuation in the average speed and vorticity of flow in swarm colony was observed at the inner region of the colony, and correlated with the switch between bacterial swarming and growth phases. At the advancing edge of the colony, both the magnitudes of velocity and vorticity of flow in swarm colony were inversely correlated with the spreading speed of the swarm edge. The advanced imaging tool developed in this study would facilitate further understanding of the effect of micro vortices in swarm colony on the collective dynamics of bacteria.

A swarm-trained k-nearest prototypes adaptive classifier with automatic feature selection for interval data.

Science.gov (United States)

Silva Filho, Telmo M; Souza, Renata M C R; Prudêncio, Ricardo B C

2016-08-01

Some complex data types are capable of modeling data variability and imprecision. These data types are studied in the symbolic data analysis field. One such data type is interval data, which represents ranges of values and is more versatile than classic point data for many domains. This paper proposes a new prototype-based classifier for interval data, trained by a swarm optimization method. Our work has two main contributions: a swarm method which is capable of performing both automatic selection of features and pruning of unused prototypes and a generalized weighted squared Euclidean distance for interval data. By discarding unnecessary features and prototypes, the proposed algorithm deals with typical limitations of prototype-based methods, such as the problem of prototype initialization. The proposed distance is useful for learning classes in interval datasets with different shapes, sizes and structures. When compared to other prototype-based methods, the proposed method achieves lower error rates in both synthetic and real interval datasets. Copyright © 2016 Elsevier Ltd. All rights reserved.

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)

Exploitation of Self Organization in UAV Swarms for Optimization in Combat Environments

National Research Council Canada - National Science Library

Nowak, Dustin J

2008-01-01

...) swarms using autonomous self-organized cooperative control. This development required the design of a new abstract UAV swarm control model which flows from an abstract Markov structure, a Partially Observable Markov Decision Process...

Pseudomonad Swarming Motility Is Restricted to a Narrow Range of High Matric Water Potentials

DEFF Research Database (Denmark)

Dechesne, Arnaud; Smets, Barth F.

2012-01-01

Using a novel experimental system that allows control of the matric potential of an agar slab, we explored the hydration conditions under which swarming motility is possible. If there is recognition that this physical parameter is a key determinant of swarming, it is usually neither controlled nor...... measured rigorously but only manipulated through proxies, namely, the agar concentration and the drying time of "soft" agar plates (swarming plates). We contend that this not only obscures the biophysical mechanisms underlying swarming but also impedes a full assessment of its clinical and environmental...

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.

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

Directory of Open Access Journals (Sweden)

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.

Large Igneous Provinces, Their Giant Mafic Dyke Swarms, and Links to Metallogeny

Science.gov (United States)

Jowitt, S.; Ernst, R. E.

2017-12-01

The relationships between large igneous provinces (LIPs), their giant dyke swarms and differing metallogenic systems can be condensed into five distinct although partially overlapping classifications: (1) LIP magmas that directly generate mineral deposits such as orthomagmatic Ni-Cu-PGE sulfides. Many carbonatites (Nb, Ta REE deposits) and kimberlites (diamonds) are also often LIP related. On the other hand, LIP-related thermal pulses (from a mantle plume) can sometimes destroy diamond potential in the overlying lithosphere. A key locus for Ni-Cu-PGE mineralization is within a few hundred km of the plume center region and plume centers are best located using giant radiating dyke swarms. Dyke subswarms with chalcophile element depletions can also be tracked "upstream" toward the plume center to identify exploration targets. (2) LIP magmas that provide energy, fluids, and/or metals for ore types such as hydrothermal volcanogenic massive sulfide (VMS) and iron oxide-copper-gold (IOCG) deposits. Heat loss from the margins of dykes and sills can also generate local enrichments in key metals (e.g. Co) within the surrounding sedimentary rocks. (3) LIP rocks (particularly sills and dykes) can act barriers to fluid flow and/or as reaction zones that control mineralizing events, act as structural traps within hydrocarbon systems, and form impermeable barriers that control water flow and hence aquifer formation (4) surficial effects, such as the formation of Ni-Co laterites and Al bauxites from tropical weathering of LIP mafic-ultramafic rocks (including volcanics fed by radiating dykes as well as the dykes themselves). This category also includes LIP-related anoxia events that generate hydrocarbon source rocks; and (5) indirect links between LIPs and ore deposits, where continental breakup-related LIP events define a `barcode' record (usually dominated by dyke swarms) that can be used to correlate and reconstruct Precambrian supercontinents. This fifth classification type

Tectonic setting of the Wooded Island earthquake swarm, eastern Washington

Science.gov (United States)

Blakely, Richard J.; Sherrod, Brian L.; Weaver, Craig S.; Rohay, Alan C.; Wells, Ray E.

2012-01-01

Magnetic anomalies provide insights into the tectonic implications of a swarm of ~1500 shallow (~1 km deep) earthquakes that occurred in 2009 on the Hanford site,Washington. Epicenters were concentrated in a 2 km2 area nearWooded Island in the Columbia River. The largest earthquake (M 3.0) had first motions consistent with slip on a northwest-striking reverse fault. The swarm was accompanied by 35 mm of vertical surface deformation, seen in satellite interferometry (InSAR), interpreted to be caused by ~50 mm of slip on a northwest-striking reverse fault and associated bedding-plane fault in the underlying Columbia River Basalt Group (CRBG). A magnetic anomaly over exposed CRBG at Yakima Ridge 40 km northwest of Wooded Island extends southeastward beyond the ridge to the Columbia River, suggesting that the Yakima Ridge anticline and its associated thrust fault extend southeastward in the subsurface. In map view, the concealed anticline passes through the earthquake swarm and lies parallel to reverse faults determined from first motions and InSAR data. A forward model of the magnetic anomaly near Wooded Island is consistent with uplift of concealed CRBG, with the top surface swarm and the thrust and bedding-plane faults modeled from interferometry all fall within the northeastern limb of the faulted anticline. Although fluids may be responsible for triggering the Wooded Island earthquake swarm, the seismic and aseismic deformation are consistent with regional-scale tectonic compression across the concealed Yakima Ridge anticline.

Earthquake swarms and the semidiurnal solid earth tide

Energy Technology Data Exchange (ETDEWEB)

Klein, F W

1976-01-01

Several correlations between peak earthquake activity during swarms and the phase and stress orientation of the calculated solid earth tide are described. The events correlating with the tide are clusters of swarm earthquakes. Swarm clusters from many sequences recorded over several years are used. Significant tidal correlations (which have less than a 5% chance of being observed if earthquakes were random) are found in the Reykjanes Peninsula in Iceland, the central Mid-Atlantic Ridge, the Imperial Valley and northern Gulf of California, and larger (m/sub b/ greater than or equal to 5.0) aftershocks of the 1965 Rat Islands earthquake. In addition, sets of larger single earthquakes on Atlantic and north-east Pacific fracture zones are significantly correlated with the calculated solid tide. No tidal correlation, however, could be found for the Matsushiro Japan swarm of 1965 to 1967. The earthquake-tide correlations other than those of the Reykjanes Peninsula and Mid-Atlantic Ridge can be interpreted as triggering caused by enhancement of the tectonic stress by tidal stress, i.e. the alignment of fault and tidal principal stresses. All tidal correlations except in the Aleutians are associated with oceanic rifts or their landward extensions. If lithospheric plates are decoupled at active rifts, then tidal stresses channeled along the lithospheric stress guide may be concentrated at ridge-type plate boundaries. Tidal triggering of earthquakes at rifts may reflect this possible amplification of tidal strains in the weakened lithosphere at ridges. 25 figures, 2 tables.

Generating size-controlled embryoid bodies using laser direct-write

International Nuclear Information System (INIS)

Dias, A D; Corr, D T; Unser, A M; Xie, Y; Chrisey, D B

2014-01-01

Embryonic stem cells (ESCs) have the potential to self-renew and differentiate into any specialized cell type. One common method to differentiate ESCs in vitro is through embryoid bodies (EBs), three-dimensional cellular aggregates that spontaneously self-assemble and generally express markers for the three germ layers, endoderm, ectoderm, and mesoderm. It has been previously shown that both EB size and 2D colony size each influence differentiation. We hypothesized that we could control the size of the EB formed by mouse ESCs (mESCs) by using a cell printing method, laser direct-write (LDW), to control both the size of the initial printed colony and the local cell density in printed colonies. After printing mESCs at various printed colony sizes and printing densities, two-way ANOVAs indicated that the EB diameter was influenced by printing density after three days (p = 0.0002), while there was no effect of the printed colony diameter on the EB diameter at the same timepoint (p = 0.74). There was no significant interaction between these two factors. Tukey's honestly significant difference test showed that high-density colonies formed significantly larger EBs, suggesting that printed mESCs quickly aggregate with nearby cells. Thus, EBs can be engineered to a desired size by controlling printing density, which will influence the design of future differentiation studies. Herein, we highlight the capacity of LDW to control the local cell density and colony size independently, at prescribed spatial locations, potentially leading to better stem cell maintenance and directed differentiation. (paper)

Use of the Comprehensive Inversion method for Swarm satellite data analysis

DEFF Research Database (Denmark)

Sabaka, T. J.; Tøffner-Clausen, Lars; Olsen, Nils

2013-01-01

An advanced algorithm, known as the “Comprehensive Inversion” (CI), is presented for the analysis of Swarm measurements to generate a consistent set of Level-2 data products to be delivered by the Swarm “Satellite Constellation Application and Research Facility” (SCARF) to the European Space Agency...

Time-variable gravity fields and ocean mass change from 37 months of kinematic Swarm orbits

Directory of Open Access Journals (Sweden)

C. Lück

2018-03-01

Full Text Available Measuring the spatiotemporal variation of ocean mass allows for partitioning of volumetric sea level change, sampled by radar altimeters, into mass-driven and steric parts. The latter is related to ocean heat change and the current Earth's energy imbalance. Since 2002, the Gravity Recovery and Climate Experiment (GRACE mission has provided monthly snapshots of the Earth's time-variable gravity field, from which one can derive ocean mass variability. However, GRACE has reached the end of its lifetime with data degradation and several gaps occurred during the last years, and there will be a prolonged gap until the launch of the follow-on mission GRACE-FO. Therefore, efforts focus on generating a long and consistent ocean mass time series by analyzing kinematic orbits from other low-flying satellites, i.e. extending the GRACE time series. Here we utilize data from the European Space Agency's (ESA Swarm Earth Explorer satellites to derive and investigate ocean mass variations. For this aim, we use the integral equation approach with short arcs (Mayer-Gürr, 2006 to compute more than 500 time-variable gravity fields with different parameterizations from kinematic orbits. We investigate the potential to bridge the gap between the GRACE and the GRACE-FO mission and to substitute missing monthly solutions with Swarm results of significantly lower resolution. Our monthly Swarm solutions have a root mean square error (RMSE of 4.0 mm with respect to GRACE, whereas directly estimating constant, trend, annual, and semiannual (CTAS signal terms leads to an RMSE of only 1.7 mm. Concerning monthly gaps, our CTAS Swarm solution appears better than interpolating existing GRACE data in 13.5 % of all cases, when artificially removing one solution. In the case of an 18-month artificial gap, 80.0 % of all CTAS Swarm solutions were found closer to the observed GRACE data compared to interpolated GRACE data. Furthermore, we show that precise modeling of non

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

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).

Novel Particle Swarm Optimization and Its Application in Calibrating the Underwater Transponder Coordinates

OpenAIRE

Zheping Yan; Chao Deng; Benyin Li; Jiajia Zhou

2014-01-01

A novel improved particle swarm algorithm named competition particle swarm optimization (CPSO) is proposed to calibrate the Underwater Transponder coordinates. To improve the performance of the algorithm, TVAC algorithm is introduced into CPSO to present an extension competition particle swarm optimization (ECPSO). The proposed method is tested with a set of 10 standard optimization benchmark problems and the results are compared with those obtained through existing PSO algorithms, basic par...

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.

A dynamic inertia weight particle swarm optimization algorithm

International Nuclear Information System (INIS)

Jiao Bin; Lian Zhigang; Gu Xingsheng

2008-01-01

Particle swarm optimization (PSO) algorithm has been developing rapidly and has been applied widely since it was introduced, as it is easily understood and realized. This paper presents an improved particle swarm optimization algorithm (IPSO) to improve the performance of standard PSO, which uses the dynamic inertia weight that decreases according to iterative generation increasing. It is tested with a set of 6 benchmark functions with 30, 50 and 150 different dimensions and compared with standard PSO. Experimental results indicate that the IPSO improves the search performance on the benchmark functions significantly

Optimal PMU Placement By Improved Particle Swarm Optimization

DEFF Research Database (Denmark)

Rather, Zakir Hussain; Liu, Leo; Chen, Zhe

2013-01-01

This paper presents an improved method of binary particle swarm optimization (IBPSO) technique for optimal phasor measurement unit (PMU) placement in a power network for complete system observability. Various effective improvements have been proposed to enhance the efficiency and convergence rate...... of conventional particle swarm optimization method. The proposed method of IBPSO ensures optimal PMU placement with and without consideration of zero injection measurements. The proposed method has been applied to standard test systems like 17 bus, IEEE 24-bus, IEEE 30-bus, New England 39-bus, IEEE 57-bus system...

Neuro-Fuzzy DC Motor Speed Control Using Particle Swarm Optimization

Directory of Open Access Journals (Sweden)

Boumediene ALLAOUA

2009-12-01

Full Text Available This paper presents an application of Adaptive Neuro-Fuzzy Inference System (ANFIS control for DC motor speed optimized with swarm collective intelligence. First, the controller is designed according to Fuzzy rules such that the systems are fundamentally robust. Secondly, an adaptive Neuro-Fuzzy controller of the DC motor speed is then designed and simulated; the ANFIS has the advantage of expert knowledge of the Fuzzy inference system and the learning capability of neural networks. Finally, the ANFIS is optimized by Swarm Intelligence. Digital simulation results demonstrate that the deigned ANFIS-Swarm speed controller realize a good dynamic behavior of the DC motor, a perfect speed tracking with no overshoot, give better performance and high robustness than those obtained by the ANFIS alone.

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

Swarm of bees and particles algorithms in the problem of gradual failure reliability assurance

Directory of Open Access Journals (Sweden)

M. F. Anop

2015-01-01

Full Text Available Probability-statistical framework of reliability theory uses models based on the chance failures analysis. These models are not functional and do not reflect relation of reliability characteristics to the object performance. At the same time, a significant part of the technical systems failures are gradual failures caused by degradation of the internal parameters of the system under the influence of various external factors.The paper shows how to provide the required level of reliability at the design stage using a functional model of a technical object. Paper describes the method for solving this problem under incomplete initial information, when there is no information about the patterns of technological deviations and degradation parameters, and the considered system model is a \\black box" one.To this end, we formulate the problem of optimal parametric synthesis. It lies in the choice of the nominal values of the system parameters to satisfy the requirements for its operation and take into account the unavoidable deviations of the parameters from their design values during operation. As an optimization criterion in this case we propose to use a deterministic geometric criterion \\reliability reserve", which is the minimum distance measured along the coordinate directions from the nominal parameter value to the acceptability region boundary rather than statistical values.The paper presents the results of the application of heuristic swarm intelligence methods to solve the formulated optimization problem. Efficiency of particle swarm algorithms and swarm of bees one compared with undirected random search algorithm in solving a number of test optimal parametric synthesis problems in three areas: reliability, convergence rate and operating time. The study suggests that the use of a swarm of bees method for solving the problem of the technical systems gradual failure reliability ensuring is preferred because of the greater flexibility of the

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.

Design of optimal wind farm configuration using a binary particle swarm optimization at Huasai district, Southern Thailand

International Nuclear Information System (INIS)

Pookpunt, Sittichoke; Ongsakul, Weerakorn

2016-01-01

Highlights: • Real wind data form a wind distributed map use in wind farm optimization algorithm. • Practical cost benefit is evaluated in the objective function. • Learning curve is developed to determine a wind farm cost model. • BPSO-TVAC simultaneously optimizes wind farm spacing, position, sizing, hub height. • Optimal placement shows improvement of operating income over conventional layout. - Abstract: This paper proposes the design of optimal wind farm configuration using a new wind probability distribution map at Huasai district, the east coast of Southern Thailand. The new wind probability distribution map integrates both frequency of wind speed and direction data at a monitoring site. The linear wake effect model is used to determine the wind speed at downstream turbines for the total power extraction from a wind farm array. The component cost model and learning curve is used to express the initial investment cost, levelized cost and the annual energy production cost of a wind farm, depending on the number of wind turbines, the installed size, hub height and wake loss within a wind farm. Based on Thailand wind energy selling price consisting of the fixed wind premium on top of base tariff, the profit depends on revenue of selling electricity and cost of energy. In this paper, Binary Particle Swarm Optimization with Time-Varying Acceleration Coefficients (BPSO-TVAC) is proposed to maximize profit subject to turbine position, turbine size, hub height, annual energy production, investment budget, land lease cost, operation and maintenance cost and levelized replacement cost constraints. Test results indicate that BPSO-TVAC optimally locate wind turbines directly facing the high frequent wind speed and direction, leading to a higher profit than the conventional wind farm layout.

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

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

Science.gov (United States)

Lister, Gordon

2017-04-01

facilitated by hydrothermal activity related to a seamount, or by magma intrusion. However, the swarm is located where the transpressional regime of the Sumatran strike-slip fault system changes to that of the 'microplate-bounding' transtensional wrench involved in the Andaman Sea spreading centre. The swarm thus may be the result of the confluence of two tectonic modes of afterslip on the main rupture, with arc-normal compression to the south, and arc-normal extension to the north. The orientations of the controlling faults can be related to the right-lateral Sumatran strike-slip system, and to oceanic transforms in the spreading system. Faults parallel to the Andaman Sea spreading system axis reactivated as left-lateral strike-slip faults during the period of afterslip. Analysis of the orientation groups shows that the swarm involved synchronous but geometrically incompatible movements on opposing but conjugate fault plane sets with trends that are consistent with Mohr-Coulomb failure, even though the orientation groups delineated require slip in many different directions on these planes. The fault planes allow inference of regional deviatoric stress axes with the principal compressive stress parallel to the prior distortion inferred using satellite geodesy.

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.

Monte Carlo simulation of electron swarms in H2

International Nuclear Information System (INIS)

Hunter, S.R.

1977-01-01

A Monte Carlo simulation of the motion of an electron swarm in molecular hydrogen has been studied in the range E/N 1.4-170 Td. The simulation was performed for 400-600 electrons at several values of E/N for two different sets of inelastic collision cross sections at high E/N. Results were obtained for the longitudinal diffusion coefficient Dsub(L), lateral diffusion coefficient D, swarm drift velocity W, average swarm energy and ionization and excitation production coefficients, and these were compared with experimental data where available. It is found that the results differ significantly from the experimental values and this is attributed to the isotropic scattering model used in this work. However, the results lend support to the experimental technique used recently by Blevin et al. to determine these transport parameters, and in particular confirm their results that Dsub(L) > D at high values of E/N. (Author)

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.

Optimal capacitor placement and sizing using combined fuzzy ...

African Journals Online (AJOL)

Then the sizing of the capacitors is modeled as an optimization problem and the objective function (loss minimization) is solved using Hybrid Particle Swarm Optimization (HPSO) technique. A case study with an IEEE 34 bus distribution feeder is presented to illustrate the applicability of the algorithm. A comparison is made ...

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

Alfvénic Dynamics and Fine Structuring of Discrete Auroral Arcs: Swarm and e-POP Observations

Science.gov (United States)

Miles, D.; Mann, I. R.; Pakhotin, I.; Burchill, J. K.; Howarth, A. D.; Knudsen, D. J.; Wallis, D. D.; Yau, A. W.; Lysak, R. L.

2017-12-01

The electrodynamics associated with dual discrete arc aurora with anti-parallel flow along the arcs were observed nearly simultaneously by the enhanced Polar Outflow Probe (e-POP) and the Swarm A and C spacecraft. Auroral imaging from e-POP reveal 1-10 km structuring of the arcs, which move and evolve on second timescales and confound the traditional single-spacecraft field-aligned current algorithms. High-cadence magnetic data from e-POP shows 1-10 Hz, presumably Alfvénic perturbations co-incident with and at the same scale size as the observed dynamic auroral fine structures. High-cadence electric and magnetic field data from Swarm A reveals non-stationary electrodynamics involving reflected and interfering Alfvén waves and signatures of modulation consistent with trapping in the Ionospheric Alfvén Resonator (IAR). Together, these observations suggest a role for Alfven waves, perhaps also the IAR, in discrete arc dynamics on 0.2 - 10s timescales and 1-10 km spatial scales.

A Synchronous-Asynchronous Particle Swarm Optimisation Algorithm

Science.gov (United States)

Ab Aziz, Nor Azlina; Mubin, Marizan; Mohamad, Mohd Saberi; Ab Aziz, Kamarulzaman

2014-01-01

In the original particle swarm optimisation (PSO) algorithm, the particles' velocities and positions are updated after the whole swarm performance is evaluated. This algorithm is also known as synchronous PSO (S-PSO). The strength of this update method is in the exploitation of the information. Asynchronous update PSO (A-PSO) has been proposed as an alternative to S-PSO. A particle in A-PSO updates its velocity and position as soon as its own performance has been evaluated. Hence, particles are updated using partial information, leading to stronger exploration. In this paper, we attempt to improve PSO by merging both update methods to utilise the strengths of both methods. The proposed synchronous-asynchronous PSO (SA-PSO) algorithm divides the particles into smaller groups. The best member of a group and the swarm's best are chosen to lead the search. Members within a group are updated synchronously, while the groups themselves are asynchronously updated. Five well-known unimodal functions, four multimodal functions, and a real world optimisation problem are used to study the performance of SA-PSO, which is compared with the performances of S-PSO and A-PSO. The results are statistically analysed and show that the proposed SA-PSO has performed consistently well. PMID:25121109

The structure of the Okavango giant mafic dyke swarm in the Karoo magmatic province of North Botswana

Science.gov (United States)

Le Gall, B.; Tshoso, G.; Tiercelin, J. J.; Dyment, J.; Aubourg, C.; Feraud, G.; Jourdan, F.; Bertrand, H.

2003-04-01

Field structural measurements combined to magnetic dataset (including both aero- and ground magnetic records) allow a systematic investigation of the structure of the Okavango giant (2000 x 100 km) mafic dyke swarm in N Botswana. The results are discussed about a 55 km-long projected section lying perpendicular to the densest zone of the swarm and cutting through Proterozoic granito-gneissic host-rocks. A total dyke population of 423 (magnetic records) or 171 (field data) individual intrusions is identified and consists principally of basalts and dolerites. New high-precision dating (Jourdan et al., this congress) demonstrates the composite nature of the Okavango swarm that includes Karoo dykes (70%) and additional (30%) Proterozoic intrusions. The two dyke populations lie with a similar strike and show no discriminant petro-structural features in the field. These new results make it difficult 1) discriminating Karoo versus Proterozoic dyke groups within the total population derived from magnetics, and 2) defining their respective structural characteristics. About the Karoo dyke population (360 intrusions), field structural observations help to constrain the statistical analysis of some of its geometrical parameters, such as the strike (N110°E), dip (vertical), lenght (ca. 5 km), thickness (18-20 m), spacing, or direction of dyke opening. The dyke-induced crustal dilatation is estimated to 6-10% across the 55 km-long reference section. Structural observations also emphazise the control exerted by preexisting basement fabrics (brittle joints and dykes) on Karoo dyke emplacement. Synmagmatic deformation is restricted to wall-parallel tensile joint networks with no evidence for extensional faulting. The Karoo part of the Okavango giant dyke swam is inferred to have been emplaced under an unidirectional extensional stress field (N70°E). Furthermore, analyzing the anisotropy of magnetic susceptibility of a number of dykes (Tshoso et al., this congress) indicates an

Investigating the polar electrojet using Swarm satellite magnetic data

DEFF Research Database (Denmark)

Aakjær, Cecilie Drost; Olsen, Nils; Finlay, Chris

The aim of this study is to investigate the magnetic perturbations caused by the polar electrojets, which are described by means of a model consisting of a series of infinite line currents placed at the height of the ionosphere along QD latitudes. The method is applied to Swarm magnetic scalar...... of the polar electrojets as well as their temporal evolution. In addition, applying the method to data taken by the Swarm satellites Alpha and Beta allows investigating longitudinal differences of the electrojets....

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...

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

Dynamics and Controls of Swarms of Femtosatellites

Data.gov (United States)

National Aeronautics and Space Administration — The proposed research activity is focused on the development of fuel and computationally efficient guidance and control algorithms for spacecraft swarms. The...

Implementasi Algoritma Particle Swarm untuk Menyelesaikan Sistem Persamaan Nonlinear

Directory of Open Access Journals (Sweden)

Ardiana Rosita

2012-09-01

Full Text Available Penyelesaian sistem persamaan nonlinear merupakan salah satu permasalahan yang sulit pada komputasi numerik dan berbagai aplikasi teknik. Beberapa metode telah dikembangkan untuk menyelesaikan sistem persamaan ini dan metode Newton merupakan metode yang paling sering digunakan. Namun metode ini memerlukan perkiraan solusi awal dan memilih perkiraan solusi awal yang baik untuk sebagian besar sistem persamaan nonlinear tidaklah mudah. Pada makalah ini, algoritma Particle Swarm yang diusulkan oleh Jaberipour dan kawan-kawan[1] diimplementasikan. Algoritma ini merupakan pengembangan dari algoritma Particle Swarm Optimization (PSO. Algoritma ini meyelesaikan sistem persamaan nonlinear yang sebelumnya telah diubah menjadi permasalahan optimasi. Uji coba dilakukan terhadap beberapa fungsi dan sistem persamaan nonlinear untuk menguji kinerja dan efisiensi algoritma. Berdasarkan hasil uji coba, beberapa fungsi dan sistem persamaan nonlinear telah konvergen pada iterasi ke 10 sampai 20 dan terdapat fungsi yang konvergen pada iterasi ke 200. Selain itu, solusi yang dihasilkan algoritma Particle Swarm mendekati solusi eksak.

Monte Carlo simulation of electron swarms in H2

International Nuclear Information System (INIS)

Hunter, S.R.

1976-05-01

A Monte-Carlo simulation of the motion of an electron swarm in molecular hydrogen was studied in the range E/N = 1.4-170 Td (1 Td = 10 -17 V/cms 2 ). The simulation was performed for 400-600 electrons at several values of E/N for two different sets of inelastic collision cross sections at high values of E/N. The longitudinal diffusion coefficient Dsub(L), lateral diffusion coefficient D, swarm drift velocity W, average swarm energy epsilon, and the ionization and excitation production coefficients were obtained and compared with experimental results where these are available. It was found that the results obtained differ significantly from the experimental values and this is attributed to the isotopic scattering model used in this work. However, the results lend support to the experimental technique reported by Blevin et al used to determine these transport parameters, and in particular confirm their result that Dsub(L) > D at high values of E/N. (author)

Earthquake statistics, spatiotemporal distribution of foci and source mechanisms - a key to understanding of the West Bohemia/Vogtland earthquake swarms

Science.gov (United States)

Horálek, Josef; Čermáková, Hana; Fischer, Tomáš

2016-04-01

Earthquake swarms are sequences of numerous events closely clustered in space and time and do not have a single dominant mainshock. A few of the largest events in a swarm reach similar magnitudes and usually occur throughout the course of the earthquake sequence. These attributes differentiate earthquake swarms from ordinary mainshock-aftershock sequences. Earthquake swarms occur worldwide, in diverse geological units. The swarms typically accompany volcanic activity at margins of the tectonic plate but also occur in intracontinental areas where strain from tectonic-plate movement is small. The origin of earthquake swarms is still unclear. The swarms typically occur at the plate margins but also in intracontinental areas. West Bohemia-Vogtland represents one of the most active intraplate earthquake-swarm areas in Europe. It is characterised by a frequent reoccurrence of ML 2.8 swarm events are located in a few dense clusters which implies step by step rupturing of one or a few asperities during the individual swarms. The source mechanism patters (moment-tensor description, MT) of the individual swarms indicate several families of the mechanisms, which fit well geometry of respective fault segments. MTs of the most events signify pure shears except for the 1997-swarm events the MTs of which indicates a combine sources including both shear and tensile components. The origin of earthquake swarms is still unclear. Nevertheless, we infer that the individual earthquake swarms in West Bohemia-Vogtland are mixture of the mainshock-aftershock sequences which correspond to step by step rupturing of one or a few asperities. The swarms occur on short fault segments with heterogeneous stress and strength, which may be affected by pressurized crustal fluids reducing normal component of the tectonic stress and lower friction. This way critically loaded faults are brought to failure and the swarm activity is driven by the differential local stress.

Epidemic Synchronization in Robotic Swarms

DEFF Research Database (Denmark)

Schiøler, Henrik; Nielsen, Jens Frederik Dalsgaard; Ngo, Trung Dung

2009-01-01

Clock synchronization in swarms of networked mobile robots is studied in a probabilistic, epidemic framework. In this setting communication and synchonization is considered to be a randomized process, taking place at unplanned instants of geographical rendezvous between robots. In combination...... as an infinite-dimensional optimal controlproblem. Illustrative numerical examples are given and commented....

ACTIVITY AND Vp/Vs RATIO OF VOLCANO-TECTONIC SEISMIC SWARM ZONES AT NEVADO DEL RUIZ VOLCANO, COLOMBIA

Directory of Open Access Journals (Sweden)

Londoño B. John Makario

2010-06-01

Full Text Available An analysis of the seismic activity for volcano-tectonic earthquake (VT swarms zones at Nevado del Ruiz Volcano (NRV was carried out for the interval 1985- 2002, which is the most seismic active period at NRV until now (2010. The swarm-like seismicity of NRV was frequently concentrated in very well defined clusters around the volcano. The seismic swarm zone located at the active crater was the most active during the entire time. The seismic swarm zone located to the west of the volcano suggested some relationship with the volcanic crises. It was active before and after the two eruptions occurred in November 1985 and September 1989. It is believed that this seismic activity may be used as a monitoring tool of volcanic activity. For each seismic swarm zone the Vp/Vs ratio was also calculated by grouping of earthquakes and stations. It was found that each seismic swarm zone had a distinct Vp/Vs ratio with respect to the others, except for the crater and west swarm zones, which had the same value. The average Vp/Vs ratios for the seismic swarm zones located at the active crater and to the west of the volcano are about 6-7% lower than that for the north swarm zone, and about 3% lower than that for the south swarm zone. We suggest that the reduction of the Vp/Vs ratio is due to degassing phenomena inside the central and western earthquake swarm zones, or due to the presence of microcracks inside the volcano. This supposition is in agreement with other studies of geophysics, geochemistry and drilling surveys carried out at NRV.

Cranberry derivatives enhance biofilm formation and transiently impair swarming motility of the uropathogen Proteus mirabilis HI4320.

Science.gov (United States)

O'May, Che; Amzallag, Olivier; Bechir, Karim; Tufenkji, Nathalie

2016-06-01

Proteus mirabilis is a major cause of catheter-associated urinary tract infection (CAUTI), emphasizing that novel strategies for targeting this bacterium are needed. Potential targets are P. mirabilis surface-associated swarming motility and the propensity of these bacteria to form biofilms that may lead to catheter blockage. We previously showed that the addition of cranberry powder (CP) to lysogeny broth (LB) medium resulted in impaired P. mirabilis swarming motility over short time periods (up to 16 h). Herein, we significantly expanded on those findings by exploring (i) the effects of cranberry derivatives on biofilm formation of P. mirabilis, (ii) whether swarming inhibition occurred transiently or over longer periods more relevant to real infections (∼3 days), (iii) whether swarming was also blocked by commercially available cranberry juices, (iv) whether CP or cranberry juices exhibited effects under natural urine conditions, and (v) the effects of cranberry on medium pH, which is an indirect indicator of urease activity. At short time scales (24 h), CP and commercially available pure cranberry juice impaired swarming motility and repelled actively swarming bacteria in LB medium. Over longer time periods more representative of infections (∼3 days), the capacity of the cranberry material to impair swarming diminished and bacteria would start to migrate across the surface, albeit by exhibiting a different motility phenotype to the regular "bull's-eye" swarming phenotype of P. mirabilis. This bacterium did not swarm on urine agar or LB agar supplemented with urea, suggesting that any potential application of anti-swarming compounds may be better suited to settings external to the urine environment. Anti-swarming effects were confounded by the ability of cranberry products to enhance biofilm formation in both LB and urine conditions. These findings provide key insights into the long-term strategy of targeting P. mirabilis CAUTIs.

Nest spacing and architecture, and swarming of males of Dinoponera quadriceps (Hymenoptera, Formicidae in a remnant of the Atlantic Forest in Northeast Brazil

Directory of Open Access Journals (Sweden)

A. Vasconcellos

Full Text Available Dinoponera quadriceps is a queenless neotropical ponerinae ant. Nest spacing and abundance were investigated in a remnant of the Atlantic forest in Northeast Brazil. Males were captured with a light trap between August 1994 and July 1996. Nest density varied from 15 to 40 ha-1. An overdispersion of nests suggests that the intraspecific competition may be an important factor regulating their spatial arrangement. Territory size was correlated with worker population size of the colonies. The nests had up to 16 chambers, with variations in their architecture closely related to habitat diversification. Populations varied from 12 to 97 adult workers per nest, with a mean density of 1,618 workers ha-1 and a live biomass of 461 g ha-1 (n = 13 nests. Males swarm continually throughout almost all months of the year, suggesting that production and swarming are more influenced by mechanisms that regulate the sexual activity of workers than by climatic factors.

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.

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...

On the reliability of spacecraft swarms

NARCIS (Netherlands)

Engelen, S.; Gill, E.K.A.; Verhoeven, C.J.M.

2012-01-01

Satellite swarms, consisting of a large number of identical, miniaturized and simple satellites, are claimed to provide an implementation for specific space missions which require high reliability. However, a consistent model of how reliability and availability on mission level is linked to cost-

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.

SCARF - The Swarm Satellite Constellation Application and Research Facility

DEFF Research Database (Denmark)

Olsen, Nils

2014-01-01

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...... conductivity, thermospheric mass density and winds, field-aligned currents, an ionospheric plasma bubble index, the ionospheric total electron content and the dayside equatorial zonal electrical field will be calculated. This service is expected to be operational for a period of at least 5 years. The present...

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

The Ionospheric Bubble Index deduced from magnetic field and plasma observations onboard Swarm

DEFF Research Database (Denmark)

Park, Jaeheung; Noja, Max; Stolle, Claudia

2013-01-01

. This product called L2-IBI is generated from magnetic field and plasma observations onboard Swarm, and gives information as to whether a Swarm magnetic field observation is affected by EPBs. We validate the performance of the L2-IBI product by using magnetic field and plasma measurements from the CHAMP...... satellite, which provided observations similar to those of the Swarm. The L2-IBI product is of interest not only for ionospheric studies, but also for geomagnetic field modeling; modelers can de-select magnetic data which are affected by EPBs or other unphysical artifacts....

A Comparison of Selected Modifications of the Particle Swarm Optimization Algorithm

Directory of Open Access Journals (Sweden)

Michala Jakubcová

2014-01-01

Full Text Available We compare 27 modifications of the original particle swarm optimization (PSO algorithm. The analysis evaluated nine basic PSO types, which differ according to the swarm evolution as controlled by various inertia weights and constriction factor. Each of the basic PSO modifications was analyzed using three different distributed strategies. In the first strategy, the entire swarm population is considered as one unit (OC-PSO, the second strategy periodically partitions the population into equally large complexes according to the particle’s functional value (SCE-PSO, and the final strategy periodically splits the swarm population into complexes using random permutation (SCERand-PSO. All variants are tested using 11 benchmark functions that were prepared for the special session on real-parameter optimization of CEC 2005. It was found that the best modification of the PSO algorithm is a variant with adaptive inertia weight. The best distribution strategy is SCE-PSO, which gives better results than do OC-PSO and SCERand-PSO for seven functions. The sphere function showed no significant difference between SCE-PSO and SCERand-PSO. It follows that a shuffling mechanism improves the optimization process.

Large and Dense Swarms: Simulation of a Shortest Path Alarm Propagation

Directory of Open Access Journals (Sweden)

Claudia Snels

2015-07-01

Full Text Available This paper deals with the transmission of alarm messages in large and dense underwater swarms of Autonomous Underwater Vehicles (AUVs and describes the verification process of the derived algorithm results by means of two simulation tools realized by the authors. A collision-free communication protocol has been developed, tailored to a case where a single AUV needs to send a message to a specific subset of swarm members regarding a perceived danger. The protocol includes a handshaking procedure that creates a silence region before the transmission of the message obtained through specific acoustic tones out of the normal transmission frequencies or through optical signals. This region will include all members of the swarm involved in the alarm message and their neighbours, preventing collisions between them. The AUV sending messages to a target area computes a delay function on appropriate arcs and runs a Dijkstra-like algorithm obtaining a multicast tree. After an explanation of the whole building of this collision-free multicast tree, a simulation has been carried out assuming different scenarios relevant to swarm density, signal power of the modem and the geometrical configuration of the nodes.

Deploying swarm intelligence in medical imaging identifying metastasis, micro-calcifications and brain image segmentation.

Science.gov (United States)

al-Rifaie, Mohammad Majid; Aber, Ahmed; Hemanth, Duraiswamy Jude

2015-12-01

This study proposes an umbrella deployment of swarm intelligence algorithm, such as stochastic diffusion search for medical imaging applications. After summarising the results of some previous works which shows how the algorithm assists in the identification of metastasis in bone scans and microcalcifications on mammographs, for the first time, the use of the algorithm in assessing the CT images of the aorta is demonstrated along with its performance in detecting the nasogastric tube in chest X-ray. The swarm intelligence algorithm presented in this study is adapted to address these particular tasks and its functionality is investigated by running the swarms on sample CT images and X-rays whose status have been determined by senior radiologists. In addition, a hybrid swarm intelligence-learning vector quantisation (LVQ) approach is proposed in the context of magnetic resonance (MR) brain image segmentation. The particle swarm optimisation is used to train the LVQ which eliminates the iteration-dependent nature of LVQ. The proposed methodology is used to detect the tumour regions in the abnormal MR brain images.

Glowworm swarm optimization theory, algorithms, and applications

CERN Document Server

Kaipa, Krishnanand N

2017-01-01

This book provides a comprehensive account of the glowworm swarm optimization (GSO) algorithm, including details of the underlying ideas, theoretical foundations, algorithm development, various applications, and MATLAB programs for the basic GSO algorithm. It also discusses several research problems at different levels of sophistication that can be attempted by interested researchers. The generality of the GSO algorithm is evident in its application to diverse problems ranging from optimization to robotics. Examples include computation of multiple optima, annual crop planning, cooperative exploration, distributed search, multiple source localization, contaminant boundary mapping, wireless sensor networks, clustering, knapsack, numerical integration, solving fixed point equations, solving systems of nonlinear equations, and engineering design optimization. The book is a valuable resource for researchers as well as graduate and undergraduate students in the area of swarm intelligence and computational intellige...

Using Seismic Interferometry to Investigate Seismic Swarms

Science.gov (United States)

Matzel, E.; Morency, C.; Templeton, D. C.

2017-12-01

Seismicity provides a direct means of measuring the physical characteristics of active tectonic features such as fault zones. Hundreds of small earthquakes often occur along a fault during a seismic swarm. This seismicity helps define the tectonically active region. When processed using novel geophysical techniques, we can isolate the energy sensitive to the fault, itself. Here we focus on two methods of seismic interferometry, ambient noise correlation (ANC) and the virtual seismometer method (VSM). ANC is based on the observation that the Earth's background noise includes coherent energy, which can be recovered by observing over long time periods and allowing the incoherent energy to cancel out. The cross correlation of ambient noise between a pair of stations results in a waveform that is identical to the seismogram that would result if an impulsive source located at one of the stations was recorded at the other, the Green function (GF). The calculation of the GF is often stable after a few weeks of continuous data correlation, any perturbations to the GF after that point are directly related to changes in the subsurface and can be used for 4D monitoring.VSM is a style of seismic interferometry that provides fast, precise, high frequency estimates of the Green's function (GF) between earthquakes. VSM illuminates the subsurface precisely where the pressures are changing and has the potential to image the evolution of seismicity over time, including changes in the style of faulting. With hundreds of earthquakes, we can calculate thousands of waveforms. At the same time, VSM collapses the computational domain, often by 2-3 orders of magnitude. This allows us to do high frequency 3D modeling in the fault region. Using data from a swarm of earthquakes near the Salton Sea, we demonstrate the power of these techniques, illustrating our ability to scale from the far field, where sources are well separated, to the near field where their locations fall within each other

Heat transfer modelling of two-phase bubbles swarm condensing in three - phase direct - contact condenser

Directory of Open Access Journals (Sweden)

Mahood Hameed B.

2016-01-01

Full Text Available An analytical model for the convective heat transfer coefficient and the two-phase bubble size of a three-phase direct contact heat exchanger was developed. Until the present, there has only been a theoretical model available that deals with a single two-phase bubble and a bubble train condensation in an immiscible liquid. However, to understand the actual heat transfer process within the three-phase direct contact condenser, characteristic models are required. A quasi - steady energy equation in a spherical coordinate system with a potential flow assumption and a cell model configuration has been simplified and solved analytically. The convective heat transfer in terms of Nu number has been derived, and it was found to be a function to Pe number and a system void fraction. In addition, the two-phase bubble size relates to the system void fraction and has been developed by solving a simple energy balance equation and using the derived convective heat transfer coefficient expression. Furthermore, the model correlates well with previous experimental data and theoretical results.

Anaerobic Respiration Using a Complete Oxidative TCA Cycle Drives Multicellular Swarming in Proteus mirabilis

Science.gov (United States)

Alteri, Christopher J.; Himpsl, Stephanie D.; Engstrom, Michael D.; Mobley, Harry L. T.

2012-01-01

ABSTRACT Proteus mirabilis rapidly migrates across surfaces using a periodic developmental process of differentiation alternating between short swimmer cells and elongated hyperflagellated swarmer cells. To undergo this vigorous flagellum-mediated motility, bacteria must generate a substantial proton gradient across their cytoplasmic membranes by using available energy pathways. We sought to identify the link between energy pathways and swarming differentiation by examining the behavior of defined central metabolism mutants. Mutations in the tricarboxylic acid (TCA) cycle (fumC and sdhB mutants) caused altered patterns of swarming periodicity, suggesting an aerobic pathway. Surprisingly, the wild-type strain swarmed on agar containing sodium azide, which poisons aerobic respiration; the fumC TCA cycle mutant, however, was unable to swarm on azide. To identify other contributing energy pathways, we screened transposon mutants for loss of swarming on sodium azide and found insertions in the following genes that involved fumarate metabolism or respiration: hybB, encoding hydrogenase; fumC, encoding fumarase; argH, encoding argininosuccinate lyase (generates fumarate); and a quinone hydroxylase gene. These findings validated the screen and suggested involvement of anaerobic electron transport chain components. Abnormal swarming periodicity of fumC and sdhB mutants was associated with the excretion of reduced acidic fermentation end products. Bacteria lacking SdhB were rescued to wild-type pH and periodicity by providing fumarate, independent of carbon source but dependent on oxygen, while fumC mutants were rescued by glycerol, independent of fumarate only under anaerobic conditions. These findings link multicellular swarming patterns with fumarate metabolism and membrane electron transport using a previously unappreciated configuration of both aerobic and anaerobic respiratory chain components. PMID:23111869

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

Directory of Open Access Journals (Sweden)

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.

Trojan asteroids - Populations, dynamical structure and origin of the L4 and L5 swarms

International Nuclear Information System (INIS)

Shoemaker, E.M.; Shoemaker, C.S.; Wolfe, R.F.

1989-01-01

The origin of Trojan asteroids, their populations, and dynamical structures are examined. Data available of Trojan asteroids reveal that the total population of Trojans of greater than 15-km diam is roughly half that estimated for the main-belt asteroids. Two-thirds of the known Trojans are in the L4 swarm. Bright Trojans are as numerous in the L5 swarm as in L4 swarm, but faint L5 Trojans are only half as numerous. Similarities of characteristic orbital parameters among certain Trojans indicate the presence of five and possibly as many as eight collisional groups in the L4 swarm. It is suggested that the magnitude distribution of L4 Trojans is probably a result of strong collisional evolution. It is suggested that the present Trojans are chiefly fragments of Jupiter planetesimals that were captured during an episode of heavy flux near Jupiter during the dispersal of the planetesimal swarm. 40 refs

“A Swarm in July”: Beekeeping Perspectives on the Old English Wið Ymbe Charm

Directory of Open Access Journals (Sweden)

Lori Ann Garner

2011-10-01

Full Text Available This exploration of an Old English charm against a swarm of bees (_wið ymbe_ augments and complements prior work on this enigmatic text by bringing knowledgeable and experienced beekeepers _directly_ into the discussion. Based on insights gained through sharing the text with them and inviting their reactions, this essay offers a highly collaborative and genuinely interdisciplinary interpretation of both the charm’s ritual instructions and the poetic incantation.

Westward tilt of low-latitude plasma blobs as observed by the Swarm constellation

DEFF Research Database (Denmark)

Park, Jaeheung; Luehr, Hermann; Michaelis, Ingo

2015-01-01

In this study we investigate the three-dimensional structure of low-latitude plasma blobs using multi-instrument and multisatellite observations of the Swarm constellation. During the early commissioning phase the Swarm satellites were flying at the same altitude with zonal separation of about 0...

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.

From random process to chaotic behavior in swarms of UAVs

OpenAIRE

Rosalie , Martin; Danoy , Grégoire; Chaumette , Serge; Bouvry , Pascal

2016-01-01

International audience; Unmanned Aerial Vehicles (UAVs) applications have seen an important increase in the last decade for both military and civilian applications ranging from fire and high seas rescue to military surveillance and target detection. While this technology is now mature for a single UAV, new methods are needed to operate UAVs in swarms, also referred to as fleets. This work focuses on the mobility management of one single autonomous swarm of UAVs which mission is to cover a giv...

A new inertia weight control strategy for particle swarm optimization

Science.gov (United States)

Zhu, Xianming; Wang, Hongbo

2018-04-01

Particle Swarm Optimization is a member of swarm intelligence algorithms, which is inspired by the behavior of bird flocks. The inertia weight, one of the most important parameters of PSO, is crucial for PSO, for it balances the performance of exploration and exploitation of the algorithm. This paper proposes a new inertia weight control strategy and PSO with this new strategy is tested by four benchmark functions. The results shows that the new strategy provides the PSO with better performance.

Persistent directional selection on body size and a resolution to the paradox of stasis.

Science.gov (United States)

Rollinson, Njal; Rowe, Locke

2015-09-01

Directional selection on size is common but often fails to result in microevolution in the wild. Similarly, macroevolutionary rates in size are low relative to the observed strength of selection in nature. We show that many estimates of selection on size have been measured on juveniles, not adults. Further, parents influence juvenile size by adjusting investment per offspring. In light of these observations, we help resolve this paradox by suggesting that the observed upward selection on size is balanced by selection against investment per offspring, resulting in little or no net selection gradient on size. We find that trade-offs between fecundity and juvenile size are common, consistent with the notion of selection against investment per offspring. We also find that median directional selection on size is positive for juveniles but no net directional selection exists for adult size. This is expected because parent-offspring conflict exists over size, and juvenile size is more strongly affected by investment per offspring than adult size. These findings provide qualitative support for the hypothesis that upward selection on size is balanced by selection against investment per offspring, where parent-offspring conflict over size is embodied in the opposing signs of the two selection gradients. © 2015 The Author(s). Evolution © 2015 The Society for the Study of Evolution.

Using global magnetospheric models for simulation and interpretation of Swarm external field measurements

DEFF Research Database (Denmark)

Moretto, T.; Vennerstrøm, Susanne; Olsen, Nils

2006-01-01

simulated external contributions relevant for internal field modeling. These have proven very valuable for the design and planning of the up-coming multi-satellite Swarm mission. In addition, a real event simulation was carried out for a moderately active time interval when observations from the Orsted...... it consistently underestimates the dayside region 2 currents and overestimates the horizontal ionospheric closure currents in the dayside polar cap. Furthermore, with this example we illustrate the great benefit of utilizing the global model for the interpretation of Swarm external field observations and......, likewise, the potential of using Swarm measurements to test and improve the global model....

Study of Electron Swarm in High Pressure Hydrogen Gas Filled RF Cavities

International Nuclear Information System (INIS)

Yonehara, K.; Chung, M.; Jansson, A.; Moretti, A.; Popovic, M.; Tollestrup, A.; Alsharo'a, M.; Johnson, R.P.; Notani, M.; Oka, T.; Wang, H.

2010-01-01

A high pressure hydrogen gas filled RF cavity has been proposed for use in the muon collection system for a muon collider. It allows for high electric field gradients in RF cavities located in strong magnetic fields, a condition frequently encountered in a muon cooling channel. In addition, an intense muon beam will generate an electron swarm via the ionization process in the cavity. A large amount of RF power will be consumed into the swarm. We show the results from our studies of the HV RF breakdown in a cavity without a beam and present some results on the resulting electron swarm dynamics. This is preliminary to actual beam tests which will take place late in 2010.

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.

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 ...

1-D DC Resistivity Modeling and Interpretation in Anisotropic Media Using Particle Swarm Optimization

Science.gov (United States)

Pekşen, Ertan; Yas, Türker; Kıyak, Alper

2014-09-01

We examine the one-dimensional direct current method in anisotropic earth formation. We derive an analytic expression of a simple, two-layered anisotropic earth model. Further, we also consider a horizontally layered anisotropic earth response with respect to the digital filter method, which yields a quasi-analytic solution over anisotropic media. These analytic and quasi-analytic solutions are useful tests for numerical codes. A two-dimensional finite difference earth model in anisotropic media is presented in order to generate a synthetic data set for a simple one-dimensional earth. Further, we propose a particle swarm optimization method for estimating the model parameters of a layered anisotropic earth model such as horizontal and vertical resistivities, and thickness. The particle swarm optimization is a naturally inspired meta-heuristic algorithm. The proposed method finds model parameters quite successfully based on synthetic and field data. However, adding 5 % Gaussian noise to the synthetic data increases the ambiguity of the value of the model parameters. For this reason, the results should be controlled by a number of statistical tests. In this study, we use probability density function within 95 % confidence interval, parameter variation of each iteration and frequency distribution of the model parameters to reduce the ambiguity. The result is promising and the proposed method can be used for evaluating one-dimensional direct current data in anisotropic media.

Initiation of Swarming Motility by Proteus mirabilis Occurs in Response to Specific Cues Present in Urine and Requires Excess l-Glutamine

Science.gov (United States)

Armbruster, Chelsie E.; Hodges, Steven A.

2013-01-01

Proteus mirabilis, a leading cause of catheter-associated urinary tract infection (CaUTI), differentiates into swarm cells that migrate across catheter surfaces and medium solidified with 1.5% agar. While many genes and nutrient requirements involved in the swarming process have been identified, few studies have addressed the signals that promote initiation of swarming following initial contact with a surface. In this study, we show that P. mirabilis CaUTI isolates initiate swarming in response to specific nutrients and environmental cues. Thirty-three compounds, including amino acids, polyamines, fatty acids, and tricarboxylic acid (TCA) cycle intermediates, were tested for the ability to promote swarming when added to normally nonpermissive media. l-Arginine, l-glutamine, dl-histidine, malate, and dl-ornithine promoted swarming on several types of media without enhancing swimming motility or growth rate. Testing of isogenic mutants revealed that swarming in response to the cues required putrescine biosynthesis and pathways involved in amino acid metabolism. Furthermore, excess glutamine was found to be a strict requirement for swarming on normal swarm agar in addition to being a swarming cue under normally nonpermissive conditions. We thus conclude that initiation of swarming occurs in response to specific cues and that manipulating concentrations of key nutrient cues can signal whether or not a particular environment is permissive for swarming. PMID:23316040

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...

Long-term geomagnetic changes observed in association with earthquake swarm activities in the Izu Peninsula, Japan

Energy Technology Data Exchange (ETDEWEB)

Oshiman, N. [Kyoto University Kyoto (Japan). Disaster Prevention Research Institute; Sasai, Y.; Ishikawa, Y.; Koyama, S. [Tokyo Univ., Tokyo (Japan). Earthquake Research Institute; Honkura, Y. [Tokyo Univ., Tokyo (Japan). Dept. of Earth and Planetary Sciences

2001-04-01

Anomalous crustal uplift has continued since 1976 in the Izu Peninsula, Japan. Earthquake swarms have also occurred intermittently off the coast of Ito since 1978. Observations of the total intensity of the geomagnetic field in the peninsula started in 1976 to detect anomalous changes in association with those crustal activities. In particular, a dense continuous observation network using proton magnetometers was established in the northeastern part of the peninsula, immediately after the sea-floor eruption off the coast of Ito in 1989. No remarkable swarm activities were observed there from 1990 to 1992. However, after the occurrence of a small swarm in January 1993, five large swarm activities were observed. At some observation sites, it was observed a remarkable long-term trend in the total geomagnetic field in association with the change in the distribution pattern in the seismicity of the earthquake swarms.

FIRST VLTI-MIDI DIRECT DETERMINATIONS OF ASTEROID SIZES

International Nuclear Information System (INIS)

Delbo, M.; Ligori, S.; Cellino, A.; Matter, A.; Berthier, J.

2009-01-01

We have obtained the first successful interferometric measurements of asteroid sizes and shapes by means of the Very Large Telescope Interferometer-Mid-Infrared Interferometric Instrument (VLTI-MIDI). The VLTI can spatially resolve asteroids in a range of sizes and heliocentric distances that are not accessible to other techniques such as adaptive optics and radar. We have observed, as a typical bench mark, the asteroid (951) Gaspra, visited in the past by the Galileo space probe, and we derive a size in good agreement with the ground truth coming from the in situ measurements by the Galileo mission. Moreover, we have also observed the asteroid (234) Barbara, known to exhibit unusual polarimetric properties, and we found evidence of a potential binary nature. In particular, our data are best fit by a system of two bodies of 37 and 21 km in diameter, separated by a center-to-center distance of ∼24 km (projected along the direction of the baseline at the epoch of our observations).

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.

A chaos wolf optimization algorithm with self-adaptive variable step-size

Science.gov (United States)

Zhu, Yong; Jiang, Wanlu; Kong, Xiangdong; Quan, Lingxiao; Zhang, Yongshun

2017-10-01

To explore the problem of parameter optimization for complex nonlinear function, a chaos wolf optimization algorithm (CWOA) with self-adaptive variable step-size was proposed. The algorithm was based on the swarm intelligence of wolf pack, which fully simulated the predation behavior and prey distribution way of wolves. It possessed three intelligent behaviors such as migration, summons and siege. And the competition rule as "winner-take-all" and the update mechanism as "survival of the fittest" were also the characteristics of the algorithm. Moreover, it combined the strategies of self-adaptive variable step-size search and chaos optimization. The CWOA was utilized in parameter optimization of twelve typical and complex nonlinear functions. And the obtained results were compared with many existing algorithms, including the classical genetic algorithm, the particle swarm optimization algorithm and the leader wolf pack search algorithm. The investigation results indicate that CWOA possess preferable optimization ability. There are advantages in optimization accuracy and convergence rate. Furthermore, it demonstrates high robustness and global searching ability.

Kinetic phenomena in charged particle transport in gases, swarm parameters and cross section data

International Nuclear Information System (INIS)

Petrovic, Z Lj; Suvakov, M; Nikitovic, Z; Dujko, S; Sasic, O; Jovanovic, J; Malovic, G; Stojanovic, V

2007-01-01

In this review we discuss the current status of the physics of charged particle swarms, mainly electrons. The whole field is analysed mainly through its relationship to plasma modelling and illustrated by some recent examples developed mainly by our group. The measurements of the swarm coefficients and the availability of the data are briefly discussed. More time is devoted to the development of complete electron-molecule cross section sets along with recent examples such as NO, CF 4 and HBr. We extend the discussion to the availability of ion and fast neutral data and how swarm experiments may serve to provide new data. As a point where new insight into the kinetics of charge particle transport is provided, the role of kinetic phenomena is discussed and recent examples are listed. We focus here on giving two examples on how non-conservative processes make dramatic effects in transport, the negative absolute mobility and the negative differential conductivity for positrons in argon. Finally we discuss the applicability of swarm data in plasma modelling and the relationship to other fields where swarm experiments and analysis make significant contributions. (topical review)

Chaos Quantum-Behaved Cat Swarm Optimization Algorithm and Its Application in the PV MPPT

Directory of Open Access Journals (Sweden)

Xiaohua Nie

2017-01-01

Full Text Available Cat Swarm Optimization (CSO algorithm was put forward in 2006. Despite a faster convergence speed compared with Particle Swarm Optimization (PSO algorithm, the application of CSO is greatly limited by the drawback of “premature convergence,” that is, the possibility of trapping in local optimum when dealing with nonlinear optimization problem with a large number of local extreme values. In order to surmount the shortcomings of CSO, Chaos Quantum-behaved Cat Swarm Optimization (CQCSO algorithm is proposed in this paper. Firstly, Quantum-behaved Cat Swarm Optimization (QCSO algorithm improves the accuracy of the CSO algorithm, because it is easy to fall into the local optimum in the later stage. Chaos Quantum-behaved Cat Swarm Optimization (CQCSO algorithm is proposed by introducing tent map for jumping out of local optimum in this paper. Secondly, CQCSO has been applied in the simulation of five different test functions, showing higher accuracy and less time consumption than CSO and QCSO. Finally, photovoltaic MPPT model and experimental platform are established and global maximum power point tracking control strategy is achieved by CQCSO algorithm, the effectiveness and efficiency of which have been verified by both simulation and experiment.

Chaos Quantum-Behaved Cat Swarm Optimization Algorithm and Its Application in the PV MPPT.

Science.gov (United States)

Nie, Xiaohua; Wang, Wei; Nie, Haoyao

2017-01-01

Cat Swarm Optimization (CSO) algorithm was put forward in 2006. Despite a faster convergence speed compared with Particle Swarm Optimization (PSO) algorithm, the application of CSO is greatly limited by the drawback of "premature convergence," that is, the possibility of trapping in local optimum when dealing with nonlinear optimization problem with a large number of local extreme values. In order to surmount the shortcomings of CSO, Chaos Quantum-behaved Cat Swarm Optimization (CQCSO) algorithm is proposed in this paper. Firstly, Quantum-behaved Cat Swarm Optimization (QCSO) algorithm improves the accuracy of the CSO algorithm, because it is easy to fall into the local optimum in the later stage. Chaos Quantum-behaved Cat Swarm Optimization (CQCSO) algorithm is proposed by introducing tent map for jumping out of local optimum in this paper. Secondly, CQCSO has been applied in the simulation of five different test functions, showing higher accuracy and less time consumption than CSO and QCSO. Finally, photovoltaic MPPT model and experimental platform are established and global maximum power point tracking control strategy is achieved by CQCSO algorithm, the effectiveness and efficiency of which have been verified by both simulation and experiment.

Han's model parameters for microalgae grown under intermittent illumination: Determined using particle swarm optimization.

Science.gov (United States)

Pozzobon, Victor; Perre, Patrick

2018-01-21

This work provides a model and the associated set of parameters allowing for microalgae population growth computation under intermittent lightning. Han's model is coupled with a simple microalgae growth model to yield a relationship between illumination and population growth. The model parameters were obtained by fitting a dataset available in literature using Particle Swarm Optimization method. In their work, authors grew microalgae in excess of nutrients under flashing conditions. Light/dark cycles used for these experimentations are quite close to those found in photobioreactor, i.e. ranging from several seconds to one minute. In this work, in addition to producing the set of parameters, Particle Swarm Optimization robustness was assessed. To do so, two different swarm initialization techniques were used, i.e. uniform and random distribution throughout the search-space. Both yielded the same results. In addition, swarm distribution analysis reveals that the swarm converges to a unique minimum. Thus, the produced set of parameters can be trustfully used to link light intensity to population growth rate. Furthermore, the set is capable to describe photodamages effects on population growth. Hence, accounting for light overexposure effect on algal growth. Copyright © 2017 Elsevier Ltd. All rights reserved.

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

Directory of Open Access Journals (Sweden)

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.

Geology and tectonic magmatic of emplacement of a longitudinal dyke swarm of Nico Perez(Minas) URUGUAY

International Nuclear Information System (INIS)

Gonzalez, P.; Poire, D.; Canalicchio, J.; Garcia Repetto, F.

2004-01-01

The Mina Verdun Group (Precambrian) was deposited prior to the subvolcanic emplacement of a longitudinal dyke swarm of basaltic to andesitic composition (Minas Subvolcanic Swarm of the Mina Verdun quarry - Nico Perez Terrane, Minas, Uruguay). The swarm and its country rocks predated a tectono-metamorphic event that produced fragileductile shear zones associated with very low- to low-grade dislocation metamorphism. We interpreted a K-Ar whole rock datum of 485,2 ± 12,5 Ma (andesitic dyke) as a minimum cooling age in relation with late- to post-swarm emplacement deuteric alteration stage. Another K-Ar whole rock datum of 108,5 ± 2,9 Ma on a basaltic dyke was assumed here as a degasification stage, while its geological meaning is still matter of debate. The Minas Subvolcanic Dyke Swarm was intruded at high crustal levels, suggesting that the Minas region was affected by a period of extensional tectonics [es

Swarm Tactics and the Doctrinal Void: Lessons from the Chechen Wars

Science.gov (United States)

2008-06-01

classify as a vapor swarm, the Finnish guerrillas “Using their quick-firing Suomi submachine guns, the skiers appeared out of nowhere, poured a...our knowledge that we hope to answer, provide a departure point for further existing work, and set the foundation for analysis and validation of the...scholarly literature on the Soviet Afghan War, three works stand out as contributing to our body of knowledge on swarming. Tactics of the Crescent

Locating multiple optima using particle swarm optimization

CSIR Research Space (South Africa)

Brits, R

2007-01-01

Full Text Available in [37]). Faure-sequences are distributed with high uniformity within a n-dimensional unit cube. Other pseudo-random uniform number generators, such as Sobol-sequences [33], may also be used. Main swarm training: In the nbest algorithm, overlapping...

Study of particle swarm optimization particle trajectories

CSIR Research Space (South Africa)

Van den Bergh, F

2006-01-01

Full Text Available . These theoretical studies concentrate mainly on simplified PSO systems. This paper overviews current theoretical studies, and extend these studies to investigate particle trajectories for general swarms to include the influence of the inertia term. The paper also...

SOLVING ENGINEERING OPTIMIZATION PROBLEMS WITH THE SWARM INTELLIGENCE METHODS

Directory of Open Access Journals (Sweden)

V. Panteleev Andrei

2017-01-01

Full Text Available An important stage in problem solving process for aerospace and aerostructures designing is calculating their main charac- teristics optimization. The results of the four constrained optimization problems related to the design of various technical systems: such as determining the best parameters of welded beams, pressure vessel, gear, spring are presented. The purpose of each task is to minimize the cost and weight of the construction. The object functions in optimization practical problem are nonlinear functions with a lot of variables and a complex layer surface indentations. That is why using classical approach for extremum seeking is not efficient. Here comes the necessity of using such methods of optimization that allow to find a near optimal solution in acceptable amount of time with the minimum waste of computer power. Such methods include the methods of Swarm Intelligence: spiral dy- namics algorithm, stochastic diffusion search, hybrid seeker optimization algorithm. The Swarm Intelligence methods are designed in such a way that a swarm consisting of agents carries out the search for extremum. In search for the point of extremum, the parti- cles exchange information and consider their experience as well as the experience of population leader and the neighbors in some area. To solve the listed problems there has been designed a program complex, which efficiency is illustrated by the solutions of four applied problems. Each of the considered applied optimization problems is solved with all the three chosen methods. The ob- tained numerical results can be compared with the ones found in a swarm with a particle method. The author gives recommenda- tions on how to choose methods parameters and penalty function value, which consider inequality constraints.

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

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.

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.

How Honey Bee Colonies Survive in the Wild: Testing the Importance of Small Nests and Frequent Swarming.

Directory of Open Access Journals (Sweden)

J Carter Loftus

Full Text Available The ectoparasitic mite, Varroa destructor, and the viruses that it transmits, kill the colonies of European honey bees (Apis mellifera kept by beekeepers unless the bees are treated with miticides. Nevertheless, there exist populations of wild colonies of European honey bees that are persisting without being treated with miticides. We hypothesized that the persistence of these wild colonies is due in part to their habits of nesting in small cavities and swarming frequently. We tested this hypothesis by establishing two groups of colonies living either in small hives (42 L without swarm-control treatments or in large hives (up to 168 L with swarm-control treatments. We followed the colonies for two years and compared the two groups with respect to swarming frequency, Varroa infesttion rate, disease incidence, and colony survival. Colonies in small hives swarmed more often, had lower Varroa infestation rates, had less disease, and had higher survival compared to colonies in large hives. These results indicate that the smaller nest cavities and more frequent swarming of wild colonies contribute to their persistence without mite treatments.

How Honey Bee Colonies Survive in the Wild: Testing the Importance of Small Nests and Frequent Swarming

Science.gov (United States)

Loftus, J. Carter; Smith, Michael L.; Seeley, Thomas D.

2016-01-01

The ectoparasitic mite, Varroa destructor, and the viruses that it transmits, kill the colonies of European honey bees (Apis mellifera) kept by beekeepers unless the bees are treated with miticides. Nevertheless, there exist populations of wild colonies of European honey bees that are persisting without being treated with miticides. We hypothesized that the persistence of these wild colonies is due in part to their habits of nesting in small cavities and swarming frequently. We tested this hypothesis by establishing two groups of colonies living either in small hives (42 L) without swarm-control treatments or in large hives (up to 168 L) with swarm-control treatments. We followed the colonies for two years and compared the two groups with respect to swarming frequency, Varroa infesttion rate, disease incidence, and colony survival. Colonies in small hives swarmed more often, had lower Varroa infestation rates, had less disease, and had higher survival compared to colonies in large hives. These results indicate that the smaller nest cavities and more frequent swarming of wild colonies contribute to their persistence without mite treatments. PMID:26968000

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

Directory of Open Access Journals (Sweden)

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.

Extreme Learning Machine and Particle Swarm Optimization in optimizing CNC turning operation

Science.gov (United States)

Janahiraman, Tiagrajah V.; Ahmad, Nooraziah; Hani Nordin, Farah

2018-04-01

The CNC machine is controlled by manipulating cutting parameters that could directly influence the process performance. Many optimization methods has been applied to obtain the optimal cutting parameters for the desired performance function. Nonetheless, the industry still uses the traditional technique to obtain those values. Lack of knowledge on optimization techniques is the main reason for this issue to be prolonged. Therefore, the simple yet easy to implement, Optimal Cutting Parameters Selection System is introduced to help the manufacturer to easily understand and determine the best optimal parameters for their turning operation. This new system consists of two stages which are modelling and optimization. In modelling of input-output and in-process parameters, the hybrid of Extreme Learning Machine and Particle Swarm Optimization is applied. This modelling technique tend to converge faster than other artificial intelligent technique and give accurate result. For the optimization stage, again the Particle Swarm Optimization is used to get the optimal cutting parameters based on the performance function preferred by the manufacturer. Overall, the system can reduce the gap between academic world and the industry by introducing a simple yet easy to implement optimization technique. This novel optimization technique can give accurate result besides being the fastest technique.

Differentiation of Serratia liquefaciens into swarm cells is controlled by the expression of the flhD master operon

DEFF Research Database (Denmark)

Eberl, L; Winson, MK; Sternberg, C

1996-01-01

The velocity with which a swarming colony of Serratia liquefaciens colonizes the surface of a suitable solid substratum was controlled by modulating the expression of the flhD master operon. In liquid medium, the stimulation of flhD expression resulted in filamentous, multinucleate......, and hyperflagellated cells that were indistinguishable from swarm cells isolated from the edge of a swarm colony. Thus, expression of the flhD master operon appears to play a central role in the process of swarm cell differentiation....

Drone swarm with free-space optical communication to detect and make deep decisions about physical problems for area surveillance

Science.gov (United States)

Mazher, Wamidh Jalil; Ibrahim, Hadeel T.; Ucan, Osman N.; Bayat, Oguz

2018-03-01

This paper aims to design a drone swarm network by employing free-space optical (FSO) communication for detecting and deep decision making of topological problems (e.g., oil pipeline leak), where deep decision making requires the highest image resolution. Drones have been widely used for monitoring and detecting problems in industrial applications during which the drone sends images from the on-air camera video stream using radio frequency (RF) signals. To obtain higher-resolution images, higher bandwidth (BW) is required. The current study proposed the use of the FSO communication system to facilitate higher BW for higher image resolution. Moreover, the number of drones required to survey a large physical area exceeded the capabilities of RF technologies. Our configuration of the drones is V-shaped swarm with one leading drone called mother drone (DM). The optical decode-and-forward (DF) technique is used to send the optical payloads of all drones in V-shaped swarm to the single ground station through DM. Furthermore, it is found that the transmitted optical power (Pt) is required for each drone based on the threshold outage probability of FSO link failure among the onboard optical-DF drones. The bit error rate of optical payload is calculated based on optical-DF onboard processing. Finally, the number of drones required for different image resolutions based on the size of the considered topological area is optimized.

Tree-ring 14C links seismic swarm to CO2 spike at Yellowstone, USA

Science.gov (United States)

Evans, William C.; Bergfeld, D.; McGeehin, J.P.; King, J.C.; Heasler, H.

2010-01-01

Mechanisms to explain swarms of shallow seismicity and inflation-deflation cycles at Yellowstone caldera (western United States) commonly invoke episodic escape of magma-derived brines or gases from the ductile zone, but no correlative changes in the surface efflux of magmatic constituents have ever been documented. Our analysis of individual growth rings in a tree core from the Mud Volcano thermal area within the caldera links a sharp ~25% drop in 14C to a local seismic swarm in 1978. The implied fivefold increase in CO2 emissions clearly associates swarm seismicity with upflow of magma-derived fluid and shows that pulses of magmatic CO2 can rapidly traverse the 5-kmthick brittle zone, even through Yellowstone's enormous hydrothermal reservoir. The 1978 event predates annual deformation surveys, but recognized connections between subsequent seismic swarms and changes in deformation suggest that CO2 might drive both processes. ?? 2010 Geological Society of America.

The 2011 West Bohemia (Central Europe) earthquake swarm compared with the previous swarms of 2000 and 2008

Czech Academy of Sciences Publication Activity Database

Čermáková, Hana; Horálek, Josef

2015-01-01

Roč. 19, č. 4 (2015), s. 899-913 ISSN 1383-4649 R&D Projects: GA ČR GAP210/12/2336; GA MŠk LM2010008 Institutional support: RVO:67985530 Keywords : West Bohemia/Vogtland * local seismicity * earthquake swarm Subject RIV: DC - Siesmology, Volcanology, Earth Structure Impact factor: 1.550, year: 2015

Electronic energy distribution function at high electron swarm energies in neon

International Nuclear Information System (INIS)

Brown, K.L.; Fletcher, J.

1995-01-01

Electron swarms moving through a gas under the influence of an applied electric field have been extensively investigated. Swarms at high energies, as measured by the ratio of the applied field to the gas number density, E/N, which are predominant in many applications have, in general, been neglected. Discharges at E/N in the range 300 0 < 133 Pa using a differentially pumped vacuum system in which the swarm electrons are extracted from the discharge and energy analysed in both a parallel plate retarded potential analyser and a cylindrical electrostatic analyser. Both pre-breakdown and post-breakdown discharges have been studied. Initial results indicate that as the discharge traverses breakdown no sudden change in the nature of the discharge occurs and that the discharge can be described by both a Monte Carlo simulation and by a Boltzmann treatment given by Phelps et al. (1987). 18 refs., 8 figs

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.

New paleomagnetic results on ˜ ˜2367 Ma Dharwar giant dyke swarm, Dharwar craton, southern India: implications for Paleoproterozoic continental reconstruction

Science.gov (United States)

Babu, N. Ramesh; Venkateshwarlu, M.; Shankar, Ravi; Nagaraju, E.; Parashuramulu, V.

2018-02-01

Here we report new paleomagnetic results and precise paleopole position of the extensional study on ˜ 2367 Ma mafic giant radiating dyke swarm in the Dharwar craton, southern India. We have sampled 29 sites on 12 dykes from NE-SW Karimnagar-Hyderabad dykes and Dhone-Gooty sector dykes, eastern Dharwar craton to provide unambiguous paleomagnetism evidence on the spectacular radiating dyke swarm and thereby strengthening the presence of single magmatic event at ˜ 2367 Ma. A total of 158 samples were subjected to detailed alternating field and thermal demagnetization techniques and the results are presented here along with previously reported data on the same dyke swarm. The remanent magnetic directions are showing two components, viz., seven sites representing four dykes show component (A) with mean declination of 94{{}°} and mean inclination of - 70{{}°} (k=87, α_{95}=10{{}°}) and corresponding paleopole at 16{{}°}N, 41{{}°}E (dp=15{{}°} and dm=17{{}°}) and 22 sites representing 8 dykes yielded a component (B) with mean declination of 41{{}°} and mean inclination of - 21{{}°} (k=41, α_{95}=9{{}°}) with a paleopole at 41{{}°}N, 200{{}°}E (dp=5{{}°} and dm=10{{}°}). Component (A) results are similar to the previously reported directions from the ˜ 2367 Ma dyke swarm, which have been confirmed fairly reliably to be of primary origin. The component (B) directions appear to be strongly overprinted by the 2080 Ma event. The grand mean for the primary component (A) combined with earlier reported studies gives mean declination of 97{{}°} and mean inclination of - 79{{}°} (k=55, α_{95}=3{{}°}) with a paleopole at 15{{}°}N, 57{{}°}E (dp=5{{}°}, dm=6{{}°}). Paleogeographical position for the Dharwar craton at ˜ 2367 Ma suggests that there may be a chance to possible spatial link between Dharwar dykes of Dharwar craton (India), Widgemooltha and Erayinia dykes of Yilgarn craton (Australia), Sebanga Poort Dykes of Zimbabwe craton (Africa) and Karelian

A Challenging Trio in Space 'Routine' Operations of the Swarm Satellite Constellation

Science.gov (United States)

Diekmann, Frank-Jurgen; Clerigo, Ignacio; Albini, Giuseppe; Maleville, Laurent; Neto, Alessandro; Patterson, David; Nino, Ana Piris; Sieg, Detlef

2016-08-01

Swarm is the first ESA Earth Observation Mission with three satellites flying in a semi-controlled constellation. The trio is operated from ESA's satellite control centre ESOC in Darmstadt, Germany. The Swarm Flight Operations Segment consists of the typical elements of a satellite control system at ESOC, but had to be carefully tailored for this innovative mission. The main challenge was the multi-satellite system of Swarm, which necessitated the development of a Mission Control System with a multi-domain functionality, both in hardware and software and covering real-time and backup domains. This was driven by the need for extreme flexibility for constellation operations and parallel activities.The three months of commissioning in 2014 were characterized by a very tight and dynamically changing schedule of activities. All operational issues could be solved during that time, including the challenging orbit acquisition phase to achieve the final constellation.Although the formal spacecraft commissioning phase was concluded in spring 2014, the investigations for some payload instruments continue even today. The Electrical Field Instruments are for instance still being tested in order to characterize and improve science data quality. Various test phases also became necessary for the Accelerometers on the Swarm satellites. In order to improve the performance of the GPS Receivers for better scientific exploitation and to minimize the failures due to loss of synchronization, a number of parameter changes were commanded via on-board patches.Finally, to minimize the impact on operations, a new strategy had to be implemented to handle single/multi bit errors in the on-board mass Memories, defining when to ignore and when to restore the memory via a re-initialisation.The poster presentation summarizes the Swarm specific ground segment elements of the FOS and explains some of the extended payload commissioning operations, turning Swarm into a most demanding and challenging

Genes that influence swarming motility and biofilm formation in Variovorax paradoxus EPS.

Directory of Open Access Journals (Sweden)

Michael J Pehl

Full Text Available Variovorax paradoxus is an aerobic soil bacterium associated with important biodegradative processes in nature. We use V. paradoxus EPS to study multicellular behaviors on surfaces.We recovered flanking sequence from 123 clones in a Tn5 mutant library, with insertions in 29 different genes, selected based on observed surface behavior phenotypes. We identified three genes, Varpa_4665, Varpa_4680, and Varpa_5900, for further examination. These genes were cloned into pBBR1MCS2 and used to complement the insertion mutants. We also analyzed expression of Varpa_4680 and Varpa_5900 under different growth conditions by qPCR.The 29 genes we identified had diverse predicted functions, many in exopolysaccharide synthesis. Varpa_4680, the most commonly recovered insertion site, encodes a putative N-acetyl-L-fucosamine transferase similar to WbuB. Expression of this gene in trans complemented the mutant fully. Several unique insertions were identified in Varpa_5900, which is one of three predicted pilY1 homologs in the EPS genome. No insertions in the two other putative pilY1 homologs present in the genome were identified. Expression of Varpa_5900 altered the structure of the wild type swarm, as did disruption of the chromosomal gene. The swarming phenotype was complemented by expression of Varpa_5900 from a plasmid, but biofilm formation was not restored. Both Varpa_4680 and Varpa_5900 transcripts were downregulated in biofilms and upregulated during swarming when compared to log phase culture. We identified a putative two component system (Varpa_4664-4665 encoding a response regulator (shkR and a sensor histidine kinase (shkS, respectively. Biofilm formation increased and swarming was strongly delayed in the Varpa_4665 (shkS mutant. Complementation of shkS restored the biofilm phenotype but swarming was still delayed. Expression of shkR in trans suppressed biofilm formation in either genetic background, and partially restored swarming in the mutant

Mobility Aware Energy Efficient Clustering for MANET: A Bio-Inspired Approach with Particle Swarm Optimization

Directory of Open Access Journals (Sweden)

Naghma Khatoon

2017-01-01

Full Text Available Mobility awareness and energy efficiency are two indispensable optimization problems in mobile ad hoc networks (MANETs where nodes move unpredictably in any direction with restricted battery life, resulting in frequent change in topology. These constraints are widely studied to increase the lifetime of such networks. This paper focuses on the problems of mobility as well as energy efficiency to develop a clustering algorithm inspired by multiagent stochastic parallel search technique of particle swarm optimization. The election of cluster heads takes care of mobility and remaining energy as well as the degree of connectivity for selecting nodes to serve as cluster heads for longer duration of time. The cluster formation is presented by taking multiobjective fitness function using particle swarm optimization. The proposed work is experimented extensively in the NS-2 network simulator and compared with the other existing algorithms. The results show the effectiveness of our proposed algorithm in terms of network lifetime, average number of clusters formed, average number of reclustering required, energy consumption, and packet delivery ratio.

Nonlinear dynamics optimization with particle swarm and genetic algorithms for SPEAR3 emittance upgrade

International Nuclear Information System (INIS)

Huang, Xiaobiao; Safranek, James

2014-01-01

Nonlinear dynamics optimization is carried out for a low emittance upgrade lattice of SPEAR3 in order to improve its dynamic aperture and Touschek lifetime. Two multi-objective optimization algorithms, a genetic algorithm and a particle swarm algorithm, are used for this study. The performance of the two algorithms are compared. The result shows that the particle swarm algorithm converges significantly faster to similar or better solutions than the genetic algorithm and it does not require seeding of good solutions in the initial population. These advantages of the particle swarm algorithm may make it more suitable for many accelerator optimization applications

Nonlinear dynamics optimization with particle swarm and genetic algorithms for SPEAR3 emittance upgrade

Energy Technology Data Exchange (ETDEWEB)

Huang, Xiaobiao, E-mail: xiahuang@slac.stanford.edu; Safranek, James

2014-09-01

Nonlinear dynamics optimization is carried out for a low emittance upgrade lattice of SPEAR3 in order to improve its dynamic aperture and Touschek lifetime. Two multi-objective optimization algorithms, a genetic algorithm and a particle swarm algorithm, are used for this study. The performance of the two algorithms are compared. The result shows that the particle swarm algorithm converges significantly faster to similar or better solutions than the genetic algorithm and it does not require seeding of good solutions in the initial population. These advantages of the particle swarm algorithm may make it more suitable for many accelerator optimization applications.

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...

Differentiation of Serratia liquefaciens into swarm cells is controlled by the expression of the flhD master operon

DEFF Research Database (Denmark)

Eberl, L; Winson, MK; Sternberg, C

1996-01-01

The velocity with which a swarming colony of Serratia liquefaciens colonizes the surface of a suitable solid substratum was controlled by modulating the expression of the flhD master operon. In liquid medium, the stimulation of flhD expression resulted in filamentous, multinucleate, and hyperflag......The velocity with which a swarming colony of Serratia liquefaciens colonizes the surface of a suitable solid substratum was controlled by modulating the expression of the flhD master operon. In liquid medium, the stimulation of flhD expression resulted in filamentous, multinucleate......, and hyperflagellated cells that were indistinguishable from swarm cells isolated from the edge of a swarm colony. Thus, expression of the flhD master operon appears to play a central role in the process of swarm cell differentiation....

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.

Artificial pheromone for path selection by a foraging swarm of robots.

Science.gov (United States)

Campo, Alexandre; Gutiérrez, Alvaro; Nouyan, Shervin; Pinciroli, Carlo; Longchamp, Valentin; Garnier, Simon; Dorigo, Marco

2010-11-01

Foraging robots involved in a search and retrieval task may create paths to navigate faster in their environment. In this context, a swarm of robots that has found several resources and created different paths may benefit strongly from path selection. Path selection enhances the foraging behavior by allowing the swarm to focus on the most profitable resource with the possibility for unused robots to stop participating in the path maintenance and to switch to another task. In order to achieve path selection, we implement virtual ants that lay artificial pheromone inside a network of robots. Virtual ants are local messages transmitted by robots; they travel along chains of robots and deposit artificial pheromone on the robots that are literally forming the chain and indicating the path. The concentration of artificial pheromone on the robots allows them to decide whether they are part of a selected path. We parameterize the mechanism with a mathematical model and provide an experimental validation using a swarm of 20 real robots. We show that our mechanism favors the selection of the closest resource is able to select a new path if a selected resource becomes unavailable and selects a newly detected and better resource when possible. As robots use very simple messages and behaviors, the system would be particularly well suited for swarms of microrobots with minimal abilities.

Comparative Analysis Of The Development Of Swarming Communities Of Bacillus Subtilis In Case Of Pta And ComXP Mutant Strains

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Kassem Hamze

2015-08-01

Full Text Available Abstract Swarming Experiments were carried on with Bacillus subtilis strains to identify the activity of certain genes in the swarming ability and surfactin production. We will examine the effect of comXP as well as pta mutations on the capability of swarming. In different experiments we showed that strain OMG 903 that carries mutation in comXP managed to produce surfactin but showed attenuated defective and random swarming pattern strain OMG 928 that carries mutation in pta gene managed to produce surfactin and showed normal swarming pattern meanwhile double mutation in comXP and pta in strain OMG 929 lead to the absence of surfactin production and didnt manage Thesetoswarmdatashowed. that a threshold of surfactin production is necessary for a normal swarming pattern.

The effect of non-uniformities on the measured transport parameters of electron swarms in hydrogen

International Nuclear Information System (INIS)

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

1978-05-01

Measurements of transport parameters of pulsed electron swarms moving through a low pressure gas by observation of the photon flux resulting from electron-molecule collisions have been recently reported. One of the possible sources of error in this kind of experiment is the variation of mean electron energy through the swarm. This effect is considered here along with the resulting variation of ionization and excitation frequency through the swarm. The validity of the experimental method is considered in the light of the above factors

Foundations of Swarm Intelligence: From Principles to Practice

National Research Council Canada - National Science Library

Fleischer, Mark

2003-01-01

Swarm Intelligence (SI) is a relatively new paradigm being applied in a host of research settings to improve the management and control of large numbers of interacting entities such as communication, computer and sensor...

A meta-analysis of human-system interfaces in unmanned aerial vehicle (UAV) swarm management.

Science.gov (United States)

Hocraffer, Amy; Nam, Chang S

2017-01-01

A meta-analysis was conducted to systematically evaluate the current state of research on human-system interfaces for users controlling semi-autonomous swarms composed of groups of drones or unmanned aerial vehicles (UAVs). UAV swarms pose several human factors challenges, such as high cognitive demands, non-intuitive behavior, and serious consequences for errors. This article presents findings from a meta-analysis of 27 UAV swarm management papers focused on the human-system interface and human factors concerns, providing an overview of the advantages, challenges, and limitations of current UAV management interfaces, as well as information on how these interfaces are currently evaluated. In general allowing user and mission-specific customization to user interfaces and raising the swarm's level of autonomy to reduce operator cognitive workload are beneficial and improve situation awareness (SA). It is clear more research is needed in this rapidly evolving field. Copyright © 2016 Elsevier Ltd. All rights reserved.

Celestial Navigation Fix Based on Particle Swarm Optimization

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Tsou Ming-Cheng

2015-09-01

Full Text Available A technique for solving celestial fix problems is proposed in this study. This method is based on Particle Swarm Optimization from the field of swarm intelligence, utilizing its superior optimization and searching abilities to obtain the most probable astronomical vessel position. In addition to being applicable to two-body fix, multi-body fix, and high-altitude observation problems, it is also less reliant on the initial dead reckoning position. Moreover, by introducing spatial data processing and display functions in a Geographical Information System, calculation results and chart work used in Circle of Position graphical positioning can both be integrated. As a result, in addition to avoiding tedious and complicated computational and graphical procedures, this work has more flexibility and is more robust when compared to other analytical approaches.

Assessment of flhDC mRNA levels in Serratia liquefaciens swarm cells

DEFF Research Database (Denmark)

Tolker-Nielsen, Tim; Christensen, Allan Beck; Holmstrøm, K.

2000-01-01

We reported previously that artificial overexpression of the flhDC operon in liquid-grown Serratia liquefaciens resulted in the formation of filamentous, multinucleated, and hyperflagellated cells that were indistinguishable from surface-induced swarm cells (L. Eberl, G. Christiansen, S. Molin, a......, vegetative cells. This suggests that surface-induced S. liquefaciens swarm cell differentiation, although dependent on flhDC gene expression, does not occur through elevated flhDC mRNA levels....

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

Climatology of GPS signal loss observed by Swarm satellites

Directory of Open Access Journals (Sweden)

C. Xiong

2018-04-01

Full Text Available By using 3-year global positioning system (GPS measurements from December 2013 to November 2016, we provide in this study a detailed survey on the climatology of the GPS signal loss of Swarm onboard receivers. Our results show that the GPS signal losses prefer to occur at both low latitudes between ±5 and ±20° magnetic latitude (MLAT and high latitudes above 60° MLAT in both hemispheres. These events at all latitudes are observed mainly during equinoxes and December solstice months, while totally absent during June solstice months. At low latitudes the GPS signal losses are caused by the equatorial plasma irregularities shortly after sunset, and at high latitude they are also highly related to the large density gradients associated with ionospheric irregularities. Additionally, the high-latitude events are more often observed in the Southern Hemisphere, occurring mainly at the cusp region and along nightside auroral latitudes. The signal losses mainly happen for those GPS rays with elevation angles less than 20°, and more commonly occur when the line of sight between GPS and Swarm satellites is aligned with the shell structure of plasma irregularities. Our results also confirm that the capability of the Swarm receiver has been improved after the bandwidth of the phase-locked loop (PLL widened, but the updates cannot radically avoid the interruption in tracking GPS satellites caused by the ionospheric plasma irregularities. Additionally, after the PLL bandwidth increased larger than 0.5 Hz, some unexpected signal losses are observed even at middle latitudes, which are not related to the ionospheric plasma irregularities. Our results suggest that rather than 1.0 Hz, a PLL bandwidth of 0.5 Hz is a more suitable value for the Swarm receiver.

Climatology of GPS signal loss observed by Swarm satellites

Science.gov (United States)

Xiong, Chao; Stolle, Claudia; Park, Jaeheung

2018-04-01

By using 3-year global positioning system (GPS) measurements from December 2013 to November 2016, we provide in this study a detailed survey on the climatology of the GPS signal loss of Swarm onboard receivers. Our results show that the GPS signal losses prefer to occur at both low latitudes between ±5 and ±20° magnetic latitude (MLAT) and high latitudes above 60° MLAT in both hemispheres. These events at all latitudes are observed mainly during equinoxes and December solstice months, while totally absent during June solstice months. At low latitudes the GPS signal losses are caused by the equatorial plasma irregularities shortly after sunset, and at high latitude they are also highly related to the large density gradients associated with ionospheric irregularities. Additionally, the high-latitude events are more often observed in the Southern Hemisphere, occurring mainly at the cusp region and along nightside auroral latitudes. The signal losses mainly happen for those GPS rays with elevation angles less than 20°, and more commonly occur when the line of sight between GPS and Swarm satellites is aligned with the shell structure of plasma irregularities. Our results also confirm that the capability of the Swarm receiver has been improved after the bandwidth of the phase-locked loop (PLL) widened, but the updates cannot radically avoid the interruption in tracking GPS satellites caused by the ionospheric plasma irregularities. Additionally, after the PLL bandwidth increased larger than 0.5 Hz, some unexpected signal losses are observed even at middle latitudes, which are not related to the ionospheric plasma irregularities. Our results suggest that rather than 1.0 Hz, a PLL bandwidth of 0.5 Hz is a more suitable value for the Swarm receiver.

Characterization of Novel Factors Involved in Swimming and Swarming Motility in Salmonella enterica Serovar Typhimurium.

Directory of Open Access Journals (Sweden)

Julia Andrea Deditius

Full Text Available Salmonella enterica utilizes flagellar motility to swim through liquid environments and on surfaces. The biosynthesis of the flagellum is regulated on various levels, including transcriptional and posttranscriptional mechanisms. Here, we investigated the motility phenotype of 24 selected single gene deletions that were previously described to display swimming and swarming motility effects. Mutations in flgE, fliH, ydiV, rfaG, yjcC, STM1267 and STM3363 showed an altered motility phenotype. Deletions of flgE and fliH displayed a non-motile phenotype in both swimming and swarming motility assays as expected. The deletions of STM1267, STM3363, ydiV, rfaG and yjcC were further analyzed in detail for flagellar and fimbrial gene expression and filament formation. A ΔydiV mutant showed increased swimming motility, but a decrease in swarming motility, which coincided with derepression of curli fimbriae. A deletion of yjcC, encoding for an EAL domain-containing protein, increased swimming motility independent on flagellar gene expression. A ΔSTM1267 mutant displayed a hypermotile phenotype on swarm agar plates and was found to have increased numbers of flagella. In contrast, a knockout of STM3363 did also display an increase in swarming motility, but did not alter flagella numbers. Finally, a deletion of the LPS biosynthesis-related protein RfaG reduced swimming and swarming motility, associated with a decrease in transcription from flagellar class II and class III promoters and a lack of flagellar filaments.

InSAR observations of aseismic slip associated with an earthquake swarm in the Columbia River flood basalts

Science.gov (United States)

Wicks, Charles; Thelen, W.; Weaver, C.; Gomberg, J.; Rohay, A.; Bodin, P.

2011-01-01

In 2009 a swarm of small shallow earthquakes occurred within the basalt flows of the Columbia River Basalt Group (CRBG). The swarm occurred within a dense seismic network in the U.S. Department of Energys Hanford Site. Data from the seismic network along with interferometric synthetic aperture radar (InSAR) data from the European Space Agencys (ESA) ENVISAT satellite provide insight into the nature of the swarm. By modeling the InSAR deformation data we constructed a model that consists of a shallow thrust fault and a near horizontal fault. We suggest that the near horizontal lying fault is a bedding-plane fault located between basalt flows. The geodetic moment of the modeled fault system is about eight times the cumulative seismic moment of the swarm. Precise location estimates of the swarm earthquakes indicate that the area of highest slip on the thrust fault, ???70mm of slip less than ???0.5km depth, was not located within the swarm cluster. Most of the slip on the faults appears to have progressed aseismically and we suggest that interbed sediments play a central role in the slip process. Copyright 2011 by the American Geophysical Union.

Kinematics of the 2015 San Ramon, California earthquake swarm: Implications for fault zone structure and driving mechanisms

Science.gov (United States)

Xue, Lian; Bürgmann, Roland; Shelly, David R.; Johnson, Christopher W.; Taira, Taka'aki

2018-05-01

Earthquake swarms represent a sudden increase in seismicity that may indicate a heterogeneous fault-zone, the involvement of crustal fluids and/or slow fault slip. Swarms sometimes precede major earthquake ruptures. An earthquake swarm occurred in October 2015 near San Ramon, California in an extensional right step-over region between the northern Calaveras Fault and the Concord-Mt. Diablo fault zone, which has hosted ten major swarms since 1970. The 2015 San Ramon swarm is examined here from 11 October through 18 November using template matching analysis. The relocated seismicity catalog contains ∼4000 events with magnitudes between - 0.2 swarm illuminated three sub-parallel, southwest striking and northwest dipping fault segments of km-scale dimension and thickness of up to 200 m. The segments contain coexisting populations of different focal-mechanisms, suggesting a complex fault zone structure with several sets of en échelon fault orientations. The migration of events along the three planar structures indicates a complex fluid and faulting interaction processes. We searched for correlations between seismic activity and tidal stresses and found some suggestive features, but nothing that we can be confident is statistically significant.

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

Directory of Open Access Journals (Sweden)

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.

Swarm Intelligence for Optimizing Hybridized Smoothing Filter in Image Edge Enhancement

Science.gov (United States)

Rao, B. Tirumala; Dehuri, S.; Dileep, M.; Vindhya, A.

In this modern era, image transmission and processing plays a major role. It would be impossible to retrieve information from satellite and medical images without the help of image processing techniques. Edge enhancement is an image processing step that enhances the edge contrast of an image or video in an attempt to improve its acutance. Edges are the representations of the discontinuities of image intensity functions. For processing these discontinuities in an image, a good edge enhancement technique is essential. The proposed work uses a new idea for edge enhancement using hybridized smoothening filters and we introduce a promising technique of obtaining best hybrid filter using swarm algorithms (Artificial Bee Colony (ABC), Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO)) to search for an optimal sequence of filters from among a set of rather simple, representative image processing filters. This paper deals with the analysis of the swarm intelligence techniques through the combination of hybrid filters generated by these algorithms for image edge enhancement.

Swarm Level 2 Comprehensive Inversion, 2016 Production

DEFF Research Database (Denmark)

Tøffner-Clausen, Lars; Sabaka, Terence; Olsen, Nils

In the framework of the ESA Earth Observation Magnetic Mapping Mission Swarm, the Expert Support Laboratories (ESL) provides high quality Level 2 Products describing a.o. the magnetic fields of the Earth. This poster provides details of the Level 2 Products from the Comprehensive Inversion chain...

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.

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

Parallel and Cooperative Particle Swarm Optimizer for Multimodal Problems

Directory of Open Access Journals (Sweden)

Geng Zhang

2015-01-01

Full Text Available Although the original particle swarm optimizer (PSO method and its related variant methods show some effectiveness for solving optimization problems, it may easily get trapped into local optimum especially when solving complex multimodal problems. Aiming to solve this issue, this paper puts forward a novel method called parallel and cooperative particle swarm optimizer (PCPSO. In case that the interacting of the elements in D-dimensional function vector X=[x1,x2,…,xd,…,xD] is independent, cooperative particle swarm optimizer (CPSO is used. Based on this, the PCPSO is presented to solve real problems. Since the dimension cannot be split into several lower dimensional search spaces in real problems because of the interacting of the elements, PCPSO exploits the cooperation of two parallel CPSO algorithms by orthogonal experimental design (OED learning. Firstly, the CPSO algorithm is used to generate two locally optimal vectors separately; then the OED is used to learn the merits of these two vectors and creates a better combination of them to generate further search. Experimental studies on a set of test functions show that PCPSO exhibits better robustness and converges much closer to the global optimum than several other peer algorithms.

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.

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.

Comparing genetic algorithm and particle swarm optimization for solving capacitated vehicle routing problem

Science.gov (United States)

Iswari, T.; Asih, A. M. S.

2018-04-01

In the logistics system, transportation plays an important role to connect every element in the supply chain, but it can produces the greatest cost. Therefore, it is important to make the transportation costs as minimum as possible. Reducing the transportation cost can be done in several ways. One of the ways to minimizing the transportation cost is by optimizing the routing of its vehicles. It refers to Vehicle Routing Problem (VRP). The most common type of VRP is Capacitated Vehicle Routing Problem (CVRP). In CVRP, the vehicles have their own capacity and the total demands from the customer should not exceed the capacity of the vehicle. CVRP belongs to the class of NP-hard problems. These NP-hard problems make it more complex to solve such that exact algorithms become highly time-consuming with the increases in problem sizes. Thus, for large-scale problem instances, as typically found in industrial applications, finding an optimal solution is not practicable. Therefore, this paper uses two kinds of metaheuristics approach to solving CVRP. Those are Genetic Algorithm and Particle Swarm Optimization. This paper compares the results of both algorithms and see the performance of each algorithm. The results show that both algorithms perform well in solving CVRP but still needs to be improved. From algorithm testing and numerical example, Genetic Algorithm yields a better solution than Particle Swarm Optimization in total distance travelled.

An Improved Particle Swarm Optimization for Solving Bilevel Multiobjective Programming Problem

Directory of Open Access Journals (Sweden)

Tao Zhang

2012-01-01

Full Text Available An improved particle swarm optimization (PSO algorithm is proposed for solving bilevel multiobjective programming problem (BLMPP. For such problems, the proposed algorithm directly simulates the decision process of bilevel programming, which is different from most traditional algorithms designed for specific versions or based on specific assumptions. The BLMPP is transformed to solve multiobjective optimization problems in the upper level and the lower level interactively by an improved PSO. And a set of approximate Pareto optimal solutions for BLMPP is obtained using the elite strategy. This interactive procedure is repeated until the accurate Pareto optimal solutions of the original problem are found. Finally, some numerical examples are given to illustrate the feasibility of the proposed algorithm.

Optimal knockout strategies in genome-scale metabolic networks using particle swarm optimization.

Science.gov (United States)

Nair, Govind; Jungreuthmayer, Christian; Zanghellini, Jürgen

2017-02-01

Knockout strategies, particularly the concept of constrained minimal cut sets (cMCSs), are an important part of the arsenal of tools used in manipulating metabolic networks. Given a specific design, cMCSs can be calculated even in genome-scale networks. We would however like to find not only the optimal intervention strategy for a given design but the best possible design too. Our solution (PSOMCS) is to use particle swarm optimization (PSO) along with the direct calculation of cMCSs from the stoichiometric matrix to obtain optimal designs satisfying multiple objectives. To illustrate the working of PSOMCS, we apply it to a toy network. Next we show its superiority by comparing its performance against other comparable methods on a medium sized E. coli core metabolic network. PSOMCS not only finds solutions comparable to previously published results but also it is orders of magnitude faster. Finally, we use PSOMCS to predict knockouts satisfying multiple objectives in a genome-scale metabolic model of E. coli and compare it with OptKnock and RobustKnock. PSOMCS finds competitive knockout strategies and designs compared to other current methods and is in some cases significantly faster. It can be used in identifying knockouts which will force optimal desired behaviors in large and genome scale metabolic networks. It will be even more useful as larger metabolic models of industrially relevant organisms become available.

The transport parameters of an electron swarm in nitrogen at elevated E/N

International Nuclear Information System (INIS)

Blevin, H.A.; Fletcher, J.; Reid, I.D.

1980-06-01

Values of the electron drift velocity, the longitudonal diffusion coefficient and the transverse diffusion coefficient for electron swarms in nitrogen over the range 50 Td <= E/N <500 Td have been determined by a method of counting and analysing the photons produced by such an electron swarm in a drift tube. A measure of the Nitrogen E state deactivation rate is presented

Self-Assembling Wireless Autonomous Reconfigurable Modules (SWARM), Phase I

Data.gov (United States)

National Aeronautics and Space Administration — Payload Systems Inc. and the MIT Space Systems Laboratory propose Self-assembling, Wireless, Autonomous, Reconfigurable Modules (SWARM) as an innovative approach to...

Parallel Global Optimization with the Particle Swarm Algorithm (Preprint)

National Research Council Canada - National Science Library

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

2004-01-01

.... To obtain enhanced computational throughput and global search capability, we detail the coarse-grained parallelization of an increasingly popular global search method, the Particle Swarm Optimization (PSO) algorithm...

Swarm Verification

Science.gov (United States)

Holzmann, Gerard J.; Joshi, Rajeev; Groce, Alex

2008-01-01

Reportedly, supercomputer designer Seymour Cray once said that he would sooner use two strong oxen to plow a field than a thousand chickens. Although this is undoubtedly wise when it comes to plowing a field, it is not so clear for other types of tasks. Model checking problems are of the proverbial "search the needle in a haystack" type. Such problems can often be parallelized easily. Alas, none of the usual divide and conquer methods can be used to parallelize the working of a model checker. Given that it has become easier than ever to gain access to large numbers of computers to perform even routine tasks it is becoming more and more attractive to find alternate ways to use these resources to speed up model checking tasks. This paper describes one such method, called swarm verification.

Steady-state configuration and tension calculations of marine cables under complex currents via separated particle swarm optimization

Science.gov (United States)

Xu, Xue-song

2014-12-01

Under complex currents, the motion governing equations of marine cables are complex and nonlinear, and the calculations of cable configuration and tension become difficult compared with those under the uniform or simple currents. To obtain the numerical results, the usual Newton-Raphson iteration is often adopted, but its stability depends on the initial guessed solution to the governing equations. To improve the stability of numerical calculation, this paper proposed separated the particle swarm optimization, in which the variables are separated into several groups, and the dimension of search space is reduced to facilitate the particle swarm optimization. Via the separated particle swarm optimization, these governing nonlinear equations can be solved successfully with any initial solution, and the process of numerical calculation is very stable. For the calculations of cable configuration and tension of marine cables under complex currents, the proposed separated swarm particle optimization is more effective than the other particle swarm optimizations.

A Monte-Carlo simulation of the behaviour of electron swarms in hydrogen using an anisotropic scattering model

International Nuclear Information System (INIS)

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

1978-05-01

In a recent paper, a Monte-Carlo simulation of electron swarms in hydrogen using an isotropic scattering model was reported. In this previous work discrepancies between the predicted and measured electron transport parameters were observed. In this paper a far more realistic anisotropic scattering model is used. Good agreement between predicted and experimental data is observed and the simulation code has been used to calculate various parameters which are not directly measurable

Bubble Swarm Rise Velocity in Fluidized Beds.

Czech Academy of Sciences Publication Activity Database

Punčochář, Miroslav; Růžička, Marek; Šimčík, Miroslav

2016-01-01

Roč. 152, OCT 2 (2016), s. 84-94 ISSN 0009-2509 R&D Projects: GA ČR(CZ) GA15-05534S Institutional support: RVO:67985858 Keywords : bubbling fluidized bed * gas-solid * bubble swarm velocity Subject RIV: CI - Industrial Chemistry, Chemical Engineering Impact factor: 2.895, year: 2016

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.

Multi-objective reliability redundancy allocation in an interval environment using particle swarm optimization

International Nuclear Information System (INIS)

Zhang, Enze; Chen, Qingwei

2016-01-01

Most of the existing works addressing reliability redundancy allocation problems are based on the assumption of fixed reliabilities of components. In real-life situations, however, the reliabilities of individual components may be imprecise, most often given as intervals, under different operating or environmental conditions. This paper deals with reliability redundancy allocation problems modeled in an interval environment. An interval multi-objective optimization problem is formulated from the original crisp one, where system reliability and cost are simultaneously considered. To render the multi-objective particle swarm optimization (MOPSO) algorithm capable of dealing with interval multi-objective optimization problems, a dominance relation for interval-valued functions is defined with the help of our newly proposed order relations of interval-valued numbers. Then, the crowding distance is extended to the multi-objective interval-valued case. Finally, the effectiveness of the proposed approach has been demonstrated through two numerical examples and a case study of supervisory control and data acquisition (SCADA) system in water resource management. - Highlights: • We model the reliability redundancy allocation problem in an interval environment. • We apply the particle swarm optimization directly on the interval values. • A dominance relation for interval-valued multi-objective functions is defined. • The crowding distance metric is extended to handle imprecise objective functions.

Seismic swarms and fluid flow offshore Central America

Science.gov (United States)

Dzierma, Yvonne; Thorwart, Martin; Hensen, Christian; Rabbel, Wolfgang; Wolf, Florian

2010-05-01

Offshore Nicaragua and Northern Costa Rica, the Cocos Plate subducts beneath the Caribbean Plate, carrying with it a large amount of fluids and volatiles. While some of these are set free at great depth beneath the volcanic arc, causing the extremely high water content observed in Nicaraguan mafic magmas (Carr et al., 2003; Kutterolf et al., 2007), some early dehydration reactions already release fluids from the subducting plate underneath the continental slope. Unlike in accretionary margins, where these fluids migrate up along the decollement towards the deformation front, fluid release at erosional margins seems to occur through fractures in the overriding plate (Ranero et al., 2008). Fluid seeps in this region have be observed at seafloor mounds, appearing as side-scan sonar backscatter anomalies or revealed by the presence of chemosynthetic communities (Sahling et al., 2008). In the framework of the General Research Area SFB 574 "Volatiles and Fluids in Subduction Zones", a network of 20 ocean-bottom-stations was deployed offshore Sta Elena Peninsula, Northern Costa Rica, from December 2005 to June 2006. Several distinct swarms of small earthquakes were observed at the seismic stations, which occurred clustered over a time period of several days and have very similar seismic waveforms. Since a correlation of fluid-release sites with the occurrence of sporadic seismic swarms would indicate that fluid migration and fracturing is the mechanism responsible for triggering the earthquake swarms, the events are re-analysed by double-difference localisation to enhance the resolution of the earthquake locations. The results are then considered to estimate the migration velocity and direction and compare the localisations with the known mound sites. Carr, M., Feigenson, M. D., Patino, L. C., and Walker, J. A., 2003: Volcanism and geochemistry in Central America: Progress and problems, in Eiler, J. (ed.), Inside the subduction factory, pp. 153-179, American Geophysical

Some possible correlations between electro-magnetic emission and seismic activity during West Bohemia 2008 earthquake swarm

Directory of Open Access Journals (Sweden)

P. Kolář

2010-10-01

Full Text Available A potential link between electromagnetic emission (EME and seismic activity (SA has been the subject of scientific speculations for a long time. EME versus SA relations obtained during the 2008 earthquake swarm which occurred in West Bohemia are presented. First, a brief characterisation of the seismic region and then the EME recording method and data analysis will be described. No simple direct link between EME and SA intensity was observed, nevertheless a deeper statistical analysis indicates: (i slight increase of EME activity in the time interval 60 to 30 min before a seismic event with prevalent periods about 10 min, (ii some gap in EME activity approximately 2 h after the event, and (iii again a flat maximum about 4 h after the seismic events. These results qualitatively correspond with the observations from other seismically active regions (Fraser-Smith et al., 1990. The global decrease of EME activity correlating with the swarm activity decay was also observed. Due to the incomplete EME data and short observation time, these results are limited in reliability and are indicative only.

A chaos wolf optimization algorithm with self-adaptive variable step-size

Directory of Open Access Journals (Sweden)

Yong Zhu

2017-10-01

Full Text Available To explore the problem of parameter optimization for complex nonlinear function, a chaos wolf optimization algorithm (CWOA with self-adaptive variable step-size was proposed. The algorithm was based on the swarm intelligence of wolf pack, which fully simulated the predation behavior and prey distribution way of wolves. It possessed three intelligent behaviors such as migration, summons and siege. And the competition rule as “winner-take-all” and the update mechanism as “survival of the fittest” were also the characteristics of the algorithm. Moreover, it combined the strategies of self-adaptive variable step-size search and chaos optimization. The CWOA was utilized in parameter optimization of twelve typical and complex nonlinear functions. And the obtained results were compared with many existing algorithms, including the classical genetic algorithm, the particle swarm optimization algorithm and the leader wolf pack search algorithm. The investigation results indicate that CWOA possess preferable optimization ability. There are advantages in optimization accuracy and convergence rate. Furthermore, it demonstrates high robustness and global searching ability.

Estimation of the magnetic field gradient tensor using the Swarm constellation

DEFF Research Database (Denmark)

Kotsiaros, Stavros; Finlay, Chris; Olsen, Nils

2014-01-01

For the first time, part of the magnetic field gradient tensor is estimated in space by the Swarm mission. We investigate the possibility of a more complete estimation of the gradient tensor exploiting the Swarm constellation. The East-West gradients can be approximated by observations from...... deviations compared to conventional vector observations at almost all latitudes. Analytical and numerical analysis of the spectral properties of the gradient tensor shows that specific combinations of the East-West and North-South gradients have almost identical signal content to the radial gradient...

The effect of non-uniformities on the measured transport parameters of electron swarms in hydrogen

International Nuclear Information System (INIS)

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

1978-01-01

Measurements of transport parameters of pulsed electron swarms moving through a low-pressure gas by observation of the photon flux resulting from electron-molecule collisions have been recently reported by Blevin et al. (J. Phys. D., 9:465, 471 and 1671 (1976)). One of the possible sources of error in this kind of experiment is the variation of mean electron energy through the swarm. This effect is considered here along with the resulting variation of ionisation and excitation frequency through the swarm. The validity of the experimental method is considered in the light of the above factors. (author)

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.

Structural preconditions of West Bohemia earthquake swarms

Czech Academy of Sciences Publication Activity Database

Novotný, Miroslav; Špičák, Aleš; Weinlich, F. H.

2013-01-01

Roč. 34, č. 4 (2013), s. 491-519 ISSN 0169-3298 R&D Projects: GA MŠk LM2010008 Institutional support: RVO:67985530 Keywords : West Bohemia earthquake swarm s * depth-recursive refraction tomography * CEL09 refraction profile Subject RIV: DC - Siesmology, Volcanology, Earth Structure Impact factor: 5.112, year: 2013

Rushan earthquake swarm in eastern China and its indications of fluid-triggered rupture

Science.gov (United States)

Zheng, Jian-Chang; Li, Dong-Mei; Li, Cui-Qin; Wang, Peng; Xu, Chang-Peng

2017-12-01

An extraordinary earthquake swarm occurred at Rushan on the Jiaodong Peninsula from October 1, 2013, onwards, and more than 12,000 aftershocks had been detected by December 31, 2015. All the activities of the whole swarm were recorded at the nearest station, RSH, which is located about 12 km from the epicenter. We examine the statistical characteristics of the Rushan swarm in this paper using RSH station data to assess the arrival time difference, t_{{{S} - {P}}} , of Pg and Sg phases. A temporary network comprising 18 seismometers was set up on May 6, 2014, within the area of the epicenter; based on the data from this network and use of the double difference method, we determine precise hypocenter locations. As the distribution of relocated sources reveals migration of seismic activity, we applied the mean-shift cluster method to perform clustering analysis on relocated catalogs. The results of this study show that there were at least 16 clusters of seismic activities between May 6, 2014, and June 30, 2014, and that each was characterized by a hypocenter spreading process. We estimated the hydraulic diffusivity, D, of each cluster using envelope curve fitting; the results show that D values range between 1.2 and 3.5 m2/d and that approximate values for clusters on the edge of the source area are lower than those within the central area. We utilize an epidemic-type aftershock sequence (ETAS) model to separate external triggered events from self-excited aftershocks within the Rushan swarm. The estimated parameters for this model suggest that α = 1.156, equivalent to sequences induced by fluid-injection, and that the forcing rate (μ) implies just 0.15 events per day. These estimates indicate that around 3% of the events within the swarm were externally triggered. The fact that variation in μ is synchronous with swarm activity implies that pulses in fluid pressure likely drove this series of earthquakes.

Adaptive Gradient Multiobjective Particle Swarm Optimization.

Science.gov (United States)

Han, Honggui; Lu, Wei; Zhang, Lu; Qiao, Junfei

2017-10-09

An adaptive gradient multiobjective particle swarm optimization (AGMOPSO) algorithm, based on a multiobjective gradient (stocktickerMOG) method and a self-adaptive flight parameters mechanism, is developed to improve the computation performance in this paper. In this AGMOPSO algorithm, the stocktickerMOG method is devised to update the archive to improve the convergence speed and the local exploitation in the evolutionary process. Meanwhile, the self-adaptive flight parameters mechanism, according to the diversity information of the particles, is then established to balance the convergence and diversity of AGMOPSO. Attributed to the stocktickerMOG method and the self-adaptive flight parameters mechanism, this AGMOPSO algorithm not only has faster convergence speed and higher accuracy, but also its solutions have better diversity. Additionally, the convergence is discussed to confirm the prerequisite of any successful application of AGMOPSO. Finally, with regard to the computation performance, the proposed AGMOPSO algorithm is compared with some other multiobjective particle swarm optimization algorithms and two state-of-the-art multiobjective algorithms. The results demonstrate that the proposed AGMOPSO algorithm can find better spread of solutions and have faster convergence to the true Pareto-optimal front.

Hybrid Bacterial Foraging and Particle Swarm Optimization for detecting Bundle Branch Block.

Science.gov (United States)

Kora, Padmavathi; Kalva, Sri Ramakrishna

2015-01-01

Abnormal cardiac beat identification is a key process in the detection of heart diseases. Our present study describes a procedure for the detection of left and right bundle branch block (LBBB and RBBB) Electrocardiogram (ECG) patterns. The electrical impulses that control the cardiac beat face difficulty in moving inside the heart. This problem is termed as bundle branch block (BBB). BBB makes it harder for the heart to pump blood effectively through the heart circulatory system. ECG feature extraction is a key process in detecting heart ailments. Our present study comes up with a hybrid method combining two heuristic optimization methods: Bacterial Forging Optimization (BFO) and Particle Swarm Optimization (PSO) for the feature selection of ECG signals. One of the major controlling forces of BFO algorithm is the chemotactic movement of a bacterium that models a test solution. The chemotaxis process of the BFO depends on random search directions which may lead to a delay in achieving the global optimum solution. The hybrid technique: Bacterial Forging-Particle Swarm Optimization (BFPSO) incorporates the concepts from BFO and PSO and it creates individuals in a new generation. This BFPSO method performs local search through the chemotactic movement of BFO and the global search over the entire search domain is accomplished by a PSO operator. The BFPSO feature values are given as the input for the Levenberg-Marquardt Neural Network classifier.

Amphibious Quadcopter Swarm for the Exploration of Titan

Science.gov (United States)

Rajguru, A.; Faler, A. C.; Franz, B.

2014-06-01

This is a proposal for a low mass and cost effective mission architecture consisting of an amphibious quadcopter swarm flight vehicle system for the exploration of Titan's liquid methane lake, Ligeia Mare. The paper focuses on the EDL and operations.

Handbook of swarm intelligence concepts, principles and applications

CERN Document Server

Shi, Yuhui; Panigrahi, Bijaya Ketan

2011-01-01

Recent work on the behavior of swarming creatures such as bees posits an innate collective intelligence that gives rise to myriad computational problem-solving techniques. This volume is both an introduction to the topic and a survey of leading-edge research.

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.

A swarm of autonomous miniature underwater robot drifters for exploring submesoscale ocean dynamics.

Science.gov (United States)

Jaffe, Jules S; Franks, Peter J S; Roberts, Paul L D; Mirza, Diba; Schurgers, Curt; Kastner, Ryan; Boch, Adrien

2017-01-24

Measuring the ever-changing 3-dimensional (3D) motions of the ocean requires simultaneous sampling at multiple locations. In particular, sampling the complex, nonlinear dynamics associated with submesoscales (swarm of 16 independent vehicles whose 3D trajectories are measured near-continuously, underwater. As the vehicles drift with the ambient flow or execute preprogrammed vertical behaviours, the simultaneous measurements at multiple, known locations resolve the details of the flow within the swarm. We describe the design, construction, control and underwater navigation of the M-AUE. A field programme in the coastal ocean using a swarm of these robots programmed with a depth-holding behaviour provides a unique test of a physical-biological interaction leading to plankton patch formation in internal waves. The performance of the M-AUE vehicles illustrates their novel capability for measuring submesoscale dynamics.

A Swarm Optimization approach for clinical knowledge mining.

Science.gov (United States)

Christopher, J Jabez; Nehemiah, H Khanna; Kannan, A

2015-10-01

Rule-based classification is a typical data mining task that is being used in several medical diagnosis and decision support systems. The rules stored in the rule base have an impact on classification efficiency. Rule sets that are extracted with data mining tools and techniques are optimized using heuristic or meta-heuristic approaches in order to improve the quality of the rule base. In this work, a meta-heuristic approach called Wind-driven Swarm Optimization (WSO) is used. The uniqueness of this work lies in the biological inspiration that underlies the algorithm. WSO uses Jval, a new metric, to evaluate the efficiency of a rule-based classifier. Rules are extracted from decision trees. WSO is used to obtain different permutations and combinations of rules whereby the optimal ruleset that satisfies the requirement of the developer is used for predicting the test data. The performance of various extensions of decision trees, namely, RIPPER, PART, FURIA and Decision Tables are analyzed. The efficiency of WSO is also compared with the traditional Particle Swarm Optimization. Experiments were carried out with six benchmark medical datasets. The traditional C4.5 algorithm yields 62.89% accuracy with 43 rules for liver disorders dataset where as WSO yields 64.60% with 19 rules. For Heart disease dataset, C4.5 is 68.64% accurate with 98 rules where as WSO is 77.8% accurate with 34 rules. The normalized standard deviation for accuracy of PSO and WSO are 0.5921 and 0.5846 respectively. WSO provides accurate and concise rulesets. PSO yields results similar to that of WSO but the novelty of WSO lies in its biological motivation and it is customization for rule base optimization. The trade-off between the prediction accuracy and the size of the rule base is optimized during the design and development of rule-based clinical decision support system. The efficiency of a decision support system relies on the content of the rule base and classification accuracy. Copyright �

Data distribution in the OLFAR satellite swarm

NARCIS (Netherlands)

Budianu, A.; Willink-Castro, T.J.; Engelen, S.; Rajan, R.T.; Rajan, Raj; Smith, D.M.P.; Meijerink, Arjan; Bentum, Marinus Jan

2013-01-01

The Orbiting Low Frequency Antennas for Radio Astronomy (OLFAR) project aims to develop a radio telescope for very low frequencies (below 30 MHz) by using a swarm of 50 or more nano-satellites. Spread in a 100-km diameter cloud, the satellites will form a very large aperture capable of sensing the

Optical Intersatellite Communications for CubeSat Swarms, Phase I

Data.gov (United States)

National Aeronautics and Space Administration — The growing interest in CubeSat swarm and constellation systems by NASA, the Department of Defense and commercial ventures has created a need for self-managed...

Investigating the effect of the reality gap on the human psychophysiological state in the context of human-swarm interaction

Directory of Open Access Journals (Sweden)

Gaëtan Podevijn

2016-09-01

Full Text Available The reality gap is the discrepancy between simulation and reality—the same behavioural algorithm results in different robot swarm behaviours in simulation and in reality (with real robots. In this paper, we study the effect of the reality gap on the psychophysiological reactions of humans interacting with a robot swarm. We compare the psychophysiological reactions of 28 participants interacting with a simulated robot swarm and with a real (non-simulated robot swarm. Our results show that a real robot swarm provokes stronger reactions in our participants than a simulated robot swarm. We also investigate how to mitigate the effect of the reality gap (i.e., how to diminish the difference in the psychophysiological reactions between reality and simulation by comparing psychophysiological reactions in simulation displayed on a computer screen and psychophysiological reactions in simulation displayed in virtual reality. Our results show that our participants tend to have stronger psychophysiological reactions in simulation displayed in virtual reality (suggesting a potential way of diminishing the effect of the reality gap.

KANTS: a stigmergic ant algorithm for cluster analysis and swarm art.

Science.gov (United States)

Fernandes, Carlos M; Mora, Antonio M; Merelo, Juan J; Rosa, Agostinho C

2014-06-01

KANTS is a swarm intelligence clustering algorithm inspired by the behavior of social insects. It uses stigmergy as a strategy for clustering large datasets and, as a result, displays a typical behavior of complex systems: self-organization and global patterns emerging from the local interaction of simple units. This paper introduces a simplified version of KANTS and describes recent experiments with the algorithm in the context of a contemporary artistic and scientific trend called swarm art, a type of generative art in which swarm intelligence systems are used to create artwork or ornamental objects. KANTS is used here for generating color drawings from the input data that represent real-world phenomena, such as electroencephalogram sleep data. However, the main proposal of this paper is an art project based on well-known abstract paintings, from which the chromatic values are extracted and used as input. Colors and shapes are therefore reorganized by KANTS, which generates its own interpretation of the original artworks. The project won the 2012 Evolutionary Art, Design, and Creativity Competition.

AN OFFSET FOR AFSOF: COMBINING ADDITIVE MANUFACTURING AND AUTONOMOUS SYSTEMS WITH SWARM EMPLOYMENT

Science.gov (United States)

2016-10-01

to service members by removing them from the hostile environment. Swarm technologies and employment use a larger number of simple, relatively low...A swarm consists of disparate elements that coordinate and adapt their movements in order to give rise to an emergent , coherent whole.”7...goal in a dynamically changing environment. This is a very active area of research and exemplified by the robot soccer league, a competition where

The impact of GPS receiver modifications and ionospheric activity on Swarm baseline determination

Science.gov (United States)

Mao, X.; Visser, P. N. A. M.; van den IJssel, J.

2018-05-01

The European Space Agency (ESA) Swarm mission is a satellite constellation launched on 22 November 2013 aiming at observing the Earth geomagnetic field and its temporal variations. The three identical satellites are equipped with high-precision dual-frequency Global Positioning System (GPS) receivers, which make the constellation an ideal test bed for baseline determination. From October 2014 to August 2016, a number of GPS receiver modifications and a new GPS Receiver Independent Exchange Format (RINEX) converter were implemented. Moreover, the on-board GPS receiver performance has been influenced by the ionospheric scintillations. The impact of these factors is assessed for baseline determination of the pendulum formation flying Swarm-A and -C satellites. In total 30 months of data - from 15 July 2014 to the end of 2016 - is analyzed. The assessment includes analysis of observation residuals, success rate of GPS carrier phase ambiguity fixing, a consistency check between the so-called kinematic and reduced-dynamic baseline solution, and validations of orbits by comparing with Satellite Laser Ranging (SLR) observations. External baseline solutions from The German Space Operations Center (GSOC) and Astronomisches Institut - Universität Bern (AIUB) are also included in the comparison. Results indicate that the GPS receiver modifications and RINEX converter changes are effective to improve the baseline determination. This research eventually shows a consistency level of 9.3/4.9/3.0 mm between kinematic and reduced-dynamic baselines in the radial/along-track/cross-track directions. On average 98.3% of the epochs have kinematic solutions. Consistency between TU Delft and external reduced-dynamic baseline solutions is at a level of 1 mm level in all directions.

Fluid-faulting interactions: Fracture-mesh and fault-valve behavior in the February 2014 Mammoth Mountain, California, earthquake swarm

Science.gov (United States)

Shelly, David R.; Taira, Taka’aki; Prejean, Stephanie; Hill, David P.; Dreger, Douglas S.

2015-01-01

Faulting and fluid transport in the subsurface are highly coupled processes, which may manifest seismically as earthquake swarms. A swarm in February 2014 beneath densely monitored Mammoth Mountain, California, provides an opportunity to witness these interactions in high resolution. Toward this goal, we employ massive waveform-correlation-based event detection and relative relocation, which quadruples the swarm catalog to more than 6000 earthquakes and produces high-precision locations even for very small events. The swarm's main seismic zone forms a distributed fracture mesh, with individual faults activated in short earthquake bursts. The largest event of the sequence, M 3.1, apparently acted as a fault valve and was followed by a distinct wave of earthquakes propagating ~1 km westward from the updip edge of rupture, 1–2 h later. Late in the swarm, multiple small, shallower subsidiary faults activated with pronounced hypocenter migration, suggesting that a broader fluid pressure pulse propagated through the subsurface.

The Manchester earthquake swarm of October 2002

Science.gov (United States)

Baptie, B.; Ottemoeller, L.

2003-04-01

An earthquake sequence started in the Greater Manchester area of the United Kingdom on October 19, 2002. This has continued to the time of writing and has consisted of more than 100 discrete earthquakes. Three temporary seismograph stations were installed to supplement existing permanent stations and to better understand the relationship between the seismicity and local geology. Due to the urban location, these were experienced by a large number of people. The largest event on October 21 had a magnitude ML 3.9. The activity appears to be an earthquake swarm, since there is no clear distinction between a main shock and aftershocks. However, most of the energy during the sequence was actually released in two earthquakes separated by a few seconds in time, on October 21 at 11:42. Other examples of swarm activity in the UK include Comrie (1788-1801, 1839-46), Glenalmond (1970-72), Doune (1997) and Blackford (1997-98, 2000-01) in central Scotland, Constantine (1981, 1986, 1992-4) in Cornwall, and Johnstonbridge (mid1980s) and Dumfries (1991,1999). The clustering of these events in time and space does suggest that there is a causal relationship between the events of the sequence. Joint hypocenter determination was used to simultaneously locate the swarm earthquakes, determine station corrections and improve the relative locations. It seems likely that all events in the sequence originate from a relatively small source volume. This is supported by the similarities in source mechanism and waveform signals between the various events. Focal depths were found to be very shallow and of the order of about 2-3 km. Source mechanisms determined for the largest of the events show strike-slip solutions along either northeast-southwest or northwest-southeast striking fault planes. The surface expression of faults in the epicentral area is generally northwest-southeast, suggesting that this is the more likely fault plane.

The 2017 Maple Creek Seismic Swarm in Yellowstone National Park

Science.gov (United States)

Pang, G.; Hale, J. M.; Farrell, J.; Burlacu, R.; Koper, K. D.; Smith, R. B.

2017-12-01

The University of Utah Seismograph Stations (UUSS) performs near-real-time monitoring of seismicity in the region around Yellowstone National Park in partnership with the United States Geological Survey and the National Park Service. UUSS operates and maintains 29 seismic stations with network code WY (short-period, strong-motion, and broadband) and records data from five other seismic networks—IW, MB, PB, TA, and US—to enhance the location capabilities in the Yellowstone region. A seismic catalog is produced using a conventional STA/LTA detector and single-event location techniques (Hypoinverse). On June 12, 2017, a seismic swarm began in Yellowstone National Park about 5 km east of Hebgen Lake. The swarm is adjacent to the source region of the 1959 MW 7.3 Hebgen Lake earthquake, in an area corresponding to positive Coulumb stress change from that event. As of Aug. 1, 2017, the swarm consists of 1481 earthquakes with 1 earthquake above magnitude 4, 8 earthquakes in the magnitude 3 range, 115 earthquakes in the magnitude 2 range, 469 earthquakes in the magnitude 1 range, 856 earthquakes in the magnitude 0 range, 22 earthquakes with negative magnitudes, and 10 earthquakes with no magnitude. Earthquake depths are mostly between 3 and 10 km and earthquake depth increases toward the northwest. Moment tensors for the 2 largest events (3.6 MW and 4.4. MW) show strike-slip faulting with T axes oriented NE-SW, consistent with the regional stress field. We are currently using waveform cross-correlation methods to measure differential travel times that are being used with the GrowClust program to generate high-accuracy relative relocations. Those locations will be used to identify structures in the seismicity and make inferences about the tectonic and magmatic processes causing the swarm.

Earthquake Swarm Along the San Andreas Fault near Palmdale, Southern California, 1976 to 1977.

Science.gov (United States)

McNally, K C; Kanamori, H; Pechmann, J C; Fuis, G

1978-09-01

Between November 1976 and November 1977 a swarm of small earthquakes (local magnitude foreshock sequences, such as tight clustering of hypocenters and time-dependent rotations of stress axes inferred from focal mechanisms. However, because of our present lack of understanding of the processes that precede earthquake faulting, the implications of the swarm for future large earthquakes on the San Andreas fault are unknown.

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

Inverse Estimation of Surface Radiation Properties Using Repulsive Particle Swarm Optimization Algorithm

Energy Technology Data Exchange (ETDEWEB)

Lee, Kyun Ho [Sejong University, Sejong (Korea, Republic of); Kim, Ki Wan [Agency for Defense Development, Daejeon (Korea, Republic of)

2014-09-15

The heat transfer mechanism for radiation is directly related to the emission of photons and electromagnetic waves. Depending on the participation of the medium, the radiation can be classified into two forms: surface and gas radiation. In the present study, unknown radiation properties were estimated using an inverse boundary analysis of surface radiation in an axisymmetric cylindrical enclosure. For efficiency, a repulsive particle swarm optimization (RPSO) algorithm, which is a relatively recent heuristic search method, was used as inverse solver. By comparing the convergence rates and accuracies with the results of a genetic algorithm (GA), the performances of the proposed RPSO algorithm as an inverse solver was verified when applied to the inverse analysis of the surface radiation problem.

Inverse Estimation of Surface Radiation Properties Using Repulsive Particle Swarm Optimization Algorithm

International Nuclear Information System (INIS)

Lee, Kyun Ho; Kim, Ki Wan

2014-01-01

The heat transfer mechanism for radiation is directly related to the emission of photons and electromagnetic waves. Depending on the participation of the medium, the radiation can be classified into two forms: surface and gas radiation. In the present study, unknown radiation properties were estimated using an inverse boundary analysis of surface radiation in an axisymmetric cylindrical enclosure. For efficiency, a repulsive particle swarm optimization (RPSO) algorithm, which is a relatively recent heuristic search method, was used as inverse solver. By comparing the convergence rates and accuracies with the results of a genetic algorithm (GA), the performances of the proposed RPSO algorithm as an inverse solver was verified when applied to the inverse analysis of the surface radiation problem

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

Science.gov (United States)