Formations of Robotic Swarm: An Artificial Force Based Approach

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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.

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

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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.

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.

Identifying Interacting Genetic Variations by Fish-Swarm Logic Regression

Science.gov (United States)

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

2013-01-01

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

Identifying Interacting Genetic Variations by Fish-Swarm Logic Regression

Directory of Open Access Journals (Sweden)

Xuanping Zhang

2013-01-01

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

Formation control of robotic swarm using bounded artificial forces.

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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.

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.

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.

Hybrid Artificial Bee Colony Algorithm and Particle Swarm Search for Global Optimization

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Wang Chun-Feng

2014-01-01

Full Text Available Artificial bee colony (ABC algorithm is one of the most recent swarm intelligence based algorithms, which has been shown to be competitive to other population-based algorithms. However, there is still an insufficiency in ABC regarding its solution search equation, which is good at exploration but poor at exploitation. To overcome this problem, we propose a novel artificial bee colony algorithm based on particle swarm search mechanism. In this algorithm, for improving the convergence speed, the initial population is generated by using good point set theory rather than random selection firstly. Secondly, in order to enhance the exploitation ability, the employed bee, onlookers, and scouts utilize the mechanism of PSO to search new candidate solutions. Finally, for further improving the searching ability, the chaotic search operator is adopted in the best solution of the current iteration. Our algorithm is tested on some well-known benchmark functions and compared with other algorithms. Results show that our algorithm has good performance.

Improved Artificial Fish Algorithm for Parameters Optimization of PID Neural Network

OpenAIRE

Jing Wang; Yourui Huang

2013-01-01

In order to solve problems such as initial weights are difficult to be determined, training results are easy to trap in local minima in optimization process of PID neural network parameters by traditional BP algorithm, this paper proposed a new method based on improved artificial fish algorithm for parameters optimization of PID neural network. This improved artificial fish algorithm uses a composite adaptive artificial fish algorithm based on optimal artificial fish and nearest artificial fi...

Swarm intelligence of artificial bees applied to In-Core Fuel Management Optimization

Energy Technology Data Exchange (ETDEWEB)

Santos de Oliveira, Iona Maghali, E-mail: ioliveira@con.ufrj.br [Nuclear Engineering Program, Federal University of Rio de Janeiro, P.O. Box 68509, Zip Code 21945-970, Rio de Janeiro, RJ (Brazil); Schirru, Roberto, E-mail: schirru@lmp.ufrj.br [Nuclear Engineering Program, Federal University of Rio de Janeiro, P.O. Box 68509, Zip Code 21945-970, Rio de Janeiro, RJ (Brazil)

2011-05-15

Research highlights: > We present Artificial Bee Colony with Random Keys (ABCRK) for In-Core Fuel Management Optimization. > Its performance is examined through the optimization of a Brazilian '2-loop' PWR. > Feasibility of using ABCRK is shown against some well known population-based algorithms. > Additional advantage includes the utilization of fewer control parameters. - Abstract: Artificial Bee Colony (ABC) algorithm is a relatively new member of swarm intelligence. ABC tries to simulate the intelligent behavior of real honey bees in food foraging and can be used for solving continuous optimization and multi-dimensional numeric problems. This paper introduces the Artificial Bee Colony with Random Keys (ABCRK), a modified ABC algorithm for solving combinatorial problems such as the In-Core Fuel Management Optimization (ICFMO). The ICFMO is a hard combinatorial optimization problem in Nuclear Engineering which during many years has been solved by expert knowledge. It aims at getting the best arrangement of fuel in the nuclear reactor core that leads to a maximization of the operating time. As a consequence, the operation cost decreases and money is saved. In this study, ABCRK is used for optimizing the ICFMO problem of a Brazilian '2-loop' Pressurized Water Reactor (PWR) Nuclear Power Plant (NPP) and the results obtained with the proposed algorithm are compared with those obtained by Genetic Algorithms (GA) and Particle Swarm Optimization (PSO). The results show that the performance of the ABCRK algorithm is better than or similar to that of other population-based algorithms, with the advantage of employing fewer control parameters.

Swarm intelligence of artificial bees applied to In-Core Fuel Management Optimization

International Nuclear Information System (INIS)

Santos de Oliveira, Iona Maghali; Schirru, Roberto

2011-01-01

Research highlights: → We present Artificial Bee Colony with Random Keys (ABCRK) for In-Core Fuel Management Optimization. → Its performance is examined through the optimization of a Brazilian '2-loop' PWR. → Feasibility of using ABCRK is shown against some well known population-based algorithms. → Additional advantage includes the utilization of fewer control parameters. - Abstract: Artificial Bee Colony (ABC) algorithm is a relatively new member of swarm intelligence. ABC tries to simulate the intelligent behavior of real honey bees in food foraging and can be used for solving continuous optimization and multi-dimensional numeric problems. This paper introduces the Artificial Bee Colony with Random Keys (ABCRK), a modified ABC algorithm for solving combinatorial problems such as the In-Core Fuel Management Optimization (ICFMO). The ICFMO is a hard combinatorial optimization problem in Nuclear Engineering which during many years has been solved by expert knowledge. It aims at getting the best arrangement of fuel in the nuclear reactor core that leads to a maximization of the operating time. As a consequence, the operation cost decreases and money is saved. In this study, ABCRK is used for optimizing the ICFMO problem of a Brazilian '2-loop' Pressurized Water Reactor (PWR) Nuclear Power Plant (NPP) and the results obtained with the proposed algorithm are compared with those obtained by Genetic Algorithms (GA) and Particle Swarm Optimization (PSO). The results show that the performance of the ABCRK algorithm is better than or similar to that of other population-based algorithms, with the advantage of employing fewer control parameters.

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

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 ...

Swarm formation control utilizing elliptical surfaces and limiting functions.

Science.gov (United States)

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

2009-12-01

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

Artificial lateral line with biomimetic neuromasts to emulate fish sensing

International Nuclear Information System (INIS)

Yang Yingchen; Chen Nannan; Tucker, Craig; Hu Huan; Liu Chang; Nguyen, Nam; Lockwood, Michael; Jones, Douglas L; Bleckmann, Horst

2010-01-01

Hydrodynamic imaging using the lateral line plays a critical role in fish behavior. To engineer such a biologically inspired sensing system, we developed an artificial lateral line using MEMS (microelectromechanical system) technology and explored its localization capability. Arrays of biomimetic neuromasts constituted an artificial lateral line wrapped around a cylinder. A beamforming algorithm further enabled the artificial lateral line to image real-world hydrodynamic events in a 3D domain. We demonstrate that the artificial lateral line system can accurately localize an artificial dipole source and a natural tail-flicking crayfish under various conditions. The artificial lateral line provides a new sense to man-made underwater vehicles and marine robots so that they can sense like fish.

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

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M. Kumar

2016-01-01

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

Forecasting outpatient visits using empirical mode decomposition coupled with back-propagation artificial neural networks optimized by particle swarm optimization.

Science.gov (United States)

Huang, Daizheng; Wu, Zhihui

2017-01-01

Accurately predicting the trend of outpatient visits by mathematical modeling can help policy makers manage hospitals effectively, reasonably organize schedules for human resources and finances, and appropriately distribute hospital material resources. In this study, a hybrid method based on empirical mode decomposition and back-propagation artificial neural networks optimized by particle swarm optimization is developed to forecast outpatient visits on the basis of monthly numbers. The data outpatient visits are retrieved from January 2005 to December 2013 and first obtained as the original time series. Second, the original time series is decomposed into a finite and often small number of intrinsic mode functions by the empirical mode decomposition technique. Third, a three-layer back-propagation artificial neural network is constructed to forecast each intrinsic mode functions. To improve network performance and avoid falling into a local minimum, particle swarm optimization is employed to optimize the weights and thresholds of back-propagation artificial neural networks. Finally, the superposition of forecasting results of the intrinsic mode functions is regarded as the ultimate forecasting value. Simulation indicates that the proposed method attains a better performance index than the other four methods.

Fish attraction to artificial reefs not always harmful: a simulation study.

Science.gov (United States)

Smith, James A; Lowry, Michael B; Suthers, Iain M

2015-10-01

The debate on whether artificial reefs produce new fish or simply attract existing fish biomass continues due to the difficulty in distinguishing these processes, and there remains considerable doubt as to whether artificial reefs are a harmful form of habitat modification. The harm typically associated with attraction is that fish will be easier to harvest due to the existing biomass aggregating at a newly deployed reef. This outcome of fish attraction has not progressed past an anecdotal form, however, and is always perceived as a harmful process. We present a numerical model that simulates the effect that a redistributed fish biomass, due to an artificial reef, has on fishing catch per unit effort (CPUE). This model can be used to identify the scenarios (in terms of reef, fish, and harvest characteristics) that pose the most risk of exploitation due to fish attraction. The properties of this model were compared to the long-standing predictions by Bohnsack (1989) on the factors that increase the risk or the harm of attraction. Simulations revealed that attraction is not always harmful because it does not always increase maximum fish density. Rather, attraction sometimes disperses existing fish biomass making them harder to catch. Some attraction can be ideal, with CPUE lowest when attraction leads to an equal distribution of biomass between natural and artificial reefs. Simulations also showed that the outcomes from attraction depend on the characteristics of the target fish species, such that transient or pelagic species are often at more risk of harmful attraction than resident species. Our findings generally agree with Bohnsack's predictions, although we recommend distinguishing "mobility" and "fidelity" when identifying species most at risk from attraction, as these traits had great influence on patterns of harvest of attracted fish biomass.

Isogenic transgenic homozygous fish induced by artificial parthenogenesis.

Science.gov (United States)

Nam, Y K; Cho, Y S; Kim, D S

2000-12-01

As a model system for vertebrate transgenesis, fish have many attractive advantages, especially with respect to the characteristics of eggs, allowing us to produce isogenic, transgenic, homozygous vertebrates by combining with chromosome-set manipulation. Here, we describe the large-scale production of isogenic transgenic homozygous animals using our experimental organism, the mud loach Misgurnus mizolepis, by the simple process of artificial parthenogenesis in a single generation. These isogenic fish have retained transgenic homozygous status in a stable manner during the subsequent 5 years, and exhibited increased levels of transgene expression. Furthermore, their isogenic nature was confirmed by cloned transgenic homozygous offspring produced via another step of parthenogenic reproduction of the isogenic homozygous transgenic fish. These results demonstrate that a combination of transgenesis and artificial parthenogenesis will make the rapid utilization of genetically pure homozygous transgenic system in vertebrate transgenesis possible.

Artificial fish schools : Collective effects of school size, body size, and body form

NARCIS (Netherlands)

Kunz, H.; Hemelrijk, C.K.

2003-01-01

Individual-based models of schooling in fish have demonstrated that, via processes of self-organization. artificial fish may school in the absence of a leader or external stimuli, using local information only. We study for the first time how body size and body form of artificial fish affect school

Schooling increases risk exposure for fish navigating past artificial barriers.

Directory of Open Access Journals (Sweden)

Bertrand H Lemasson

Full Text Available Artificial barriers have become ubiquitous features in freshwater ecosystems and they can significantly impact a region's biodiversity. Assessing the risk faced by fish forced to navigate their way around artificial barriers is largely based on assays of individual swimming behavior. However, social interactions can significantly influence fish movement patterns and alter their risk exposure. Using an experimental flume, we assessed the effects of social interactions on the amount of time required for juvenile palmetto bass (Morone chrysops × M. saxatilis to navigate downstream past an artificial barrier. Fish were released either individually or in groups into the flume using flow conditions that approached the limit of their expected swimming stamina. We compared fish swimming behaviors under solitary and schooling conditions and measured risk as the time individuals spent exposed to the barrier. Solitary fish generally turned with the current and moved quickly downstream past the barrier, while fish in groups swam against the current and displayed a 23-fold increase in exposure time. Solitary individuals also showed greater signs of skittish behavior than those released in groups, which was reflected by larger changes in their accelerations and turning profiles. While groups displayed fission-fusion dynamics, inter-individual positions were highly structured and remained steady over time. These spatial patterns align with theoretical positions necessary to reduce swimming exertion through either wake capturing or velocity sheltering, but diverge from any potential gains from channeling effects between adjacent neighbors. We conclude that isolated performance trials and projections based on individual behaviors can lead to erroneous predictions of risk exposure along engineered structures. Our results also suggest that risk perception and behavior may be more important than a fish's swimming stamina in artificially modified systems.

Schooling increases risk exposure for fish navigating past artificial barriers.

Science.gov (United States)

Lemasson, Bertrand H; Haefner, James W; Bowen, Mark D

2014-01-01

Artificial barriers have become ubiquitous features in freshwater ecosystems and they can significantly impact a region's biodiversity. Assessing the risk faced by fish forced to navigate their way around artificial barriers is largely based on assays of individual swimming behavior. However, social interactions can significantly influence fish movement patterns and alter their risk exposure. Using an experimental flume, we assessed the effects of social interactions on the amount of time required for juvenile palmetto bass (Morone chrysops × M. saxatilis) to navigate downstream past an artificial barrier. Fish were released either individually or in groups into the flume using flow conditions that approached the limit of their expected swimming stamina. We compared fish swimming behaviors under solitary and schooling conditions and measured risk as the time individuals spent exposed to the barrier. Solitary fish generally turned with the current and moved quickly downstream past the barrier, while fish in groups swam against the current and displayed a 23-fold increase in exposure time. Solitary individuals also showed greater signs of skittish behavior than those released in groups, which was reflected by larger changes in their accelerations and turning profiles. While groups displayed fission-fusion dynamics, inter-individual positions were highly structured and remained steady over time. These spatial patterns align with theoretical positions necessary to reduce swimming exertion through either wake capturing or velocity sheltering, but diverge from any potential gains from channeling effects between adjacent neighbors. We conclude that isolated performance trials and projections based on individual behaviors can lead to erroneous predictions of risk exposure along engineered structures. Our results also suggest that risk perception and behavior may be more important than a fish's swimming stamina in artificially modified 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.

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.

Effects of artificial feeds on growth and production of fishes in Polyculture

Directory of Open Access Journals (Sweden)

M.A. Hosen

2014-12-01

Full Text Available A study on the effects of artificial feeds on growth and production of fishes along with some limnological conditions were conducted in polyculture system. Species of Indian major carp (Cirrhinus mrigala and exotic fishes (Hypophthalmicthys molitrix and Oreochromis niloticus were stocked in six ponds under two treatments, each with three replications. Stocking rate in both treatments was 100 fish per decimal at the ratio of silver carp: tilapia: mrigal = 2: 2: 1. Fertilization and artificial feeds were given in Ttreatment 1 (T1 and only fertilization was done in Treatment 2 (T2. Wheat bran, rice bran and soybean meal were given daily as artificial feed in T1 in the ratio of wheat bran: rice bran: soybean meal = 2: 2: 1 (by wt. Urea, T.S.P and cow dung were applied fortnightly at the rate of 60 g deci-1, 90 g deci-1 and 2 kg deci-1 respectively. Water temperature, transparency, pH, dissolved oxygen, free CO2, total alkalinity, PO4-P and NO3-N were determined fortnightly and phytoplankton and zooplankton were studied fortnightly. These limnological conditions were more or less similar in the ponds under two treatments and were within suitable ranges. Calculated gross and net yields of fish were 16.56 and 12.48 ton ha-1 respectively in case of fertilization and artificial feeding application (T1 and 9.99 and 5.91 ton ha-1 respectively in case of only fertilization (T2. Application of artificial feed in T1 significantly increased the growth and production of fish more than two times which indicates that artificial feeding in polyculture is very useful for increasing fish production.

A biologically inspired artificial fish using flexible matrix composite actuators: analysis and experiment

International Nuclear Information System (INIS)

Zhang, Zhiye; Philen, Michael; Neu, Wayne

2010-01-01

A bio-inspired prototype fish using the flexible matrix composite (FMC) muscle technology for fin and body actuation is developed. FMC actuators are pressure driven muscle-like actuators capable of large displacements as well as large blocking forces. An analytical model of the artificial fish using FMC actuators is developed and analysis results are presented. An experimental prototype of the artificial fish having FMC artificial muscles has been completed and tested. Constant mean thrusts have been achieved in the laboratory for a stationary fish for different undulation frequencies around 1 Hz. The experimental results demonstrate that a nearly constant thrust can be achieved through tuning of excitation frequency for given body stiffness. Free swimming results show that the prototype can swim at approximately 0.3 m s −1

Swarm intelligence in fish? The difficulty in demonstrating distributed and self-organised collective intelligence in (some) animal groups.

Science.gov (United States)

Ioannou, Christos C

2017-08-01

Larger groups often have a greater ability to solve cognitive tasks compared to smaller ones or lone individuals. This is well established in social insects, navigating flocks of birds, and in groups of prey collectively vigilant for predators. Research in social insects has convincingly shown that improved cognitive performance can arise from self-organised local interactions between individuals that integrates their contributions, often referred to as swarm intelligence. This emergent collective intelligence has gained in popularity and been directly applied to groups of other animals, including fish. Despite being a likely mechanism at least partially explaining group performance in vertebrates, I argue here that other possible explanations are rarely ruled out in empirical studies. Hence, evidence for self-organised collective (or 'swarm') intelligence in fish is not as strong as it would first appear. These other explanations, the 'pool-of-competence' and the greater cognitive ability of individuals when in larger groups, are also reviewed. Also discussed is why improved group performance in general may be less often observed in animals such as shoaling fish compared to social insects. This review intends to highlight the difficulties in exploring collective intelligence in animal groups, ideally leading to further empirical work to illuminate these issues. Copyright © 2016 The Author. Published by Elsevier B.V. All rights reserved.

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 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 Profound Survey on Swarm Intelligence

OpenAIRE

Manish Mahant; Bharat Choudhary; Abhishek Kesharwani; Kalyani Singh Rathore

2012-01-01

Swarm Intelligence (SI) is the collective behavior of decentralized, self-organized systems, natural or artificial. The concept is employed in work on artificial intelligence. The inspiration often comes from nature, especially biological systems. The expression was introduced by Gerardo Beni and Jing Wang in 1989, in the context of cellular robotic systems. SI systems are typically made up of a population of simple agents or boids interacting locally with one another and their environment. T...

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

OpenAIRE

川村, 秀憲; 山本, 雅人; 大内, 東

2001-01-01

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

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.

Cancer Classification Based on Support Vector Machine Optimized by Particle Swarm Optimization and Artificial Bee Colony.

Science.gov (United States)

Gao, Lingyun; Ye, Mingquan; Wu, Changrong

2017-11-29

Intelligent optimization algorithms have advantages in dealing with complex nonlinear problems accompanied by good flexibility and adaptability. In this paper, the FCBF (Fast Correlation-Based Feature selection) method is used to filter irrelevant and redundant features in order to improve the quality of cancer classification. Then, we perform classification based on SVM (Support Vector Machine) optimized by PSO (Particle Swarm Optimization) combined with ABC (Artificial Bee Colony) approaches, which is represented as PA-SVM. The proposed PA-SVM method is applied to nine cancer datasets, including five datasets of outcome prediction and a protein dataset of ovarian cancer. By comparison with other classification methods, the results demonstrate the effectiveness and the robustness of the proposed PA-SVM method in handling various types of data for cancer classification.

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.

An artificial bee colony algorithm for the capacitated vehicle routing problem

DEFF Research Database (Denmark)

Szeto, W.Y.; Wu, Yongzhong; Ho, Sin C.

2011-01-01

This paper introduces an artificial bee colony heuristic for solving the capacitated vehicle routing problem. The artificial bee colony heuristic is a swarm-based heuristic, which mimics the foraging behavior of a honey bee swarm. An enhanced version of the artificial bee colony heuristic is also...... proposed to improve the solution quality of the original version. The performance of the enhanced heuristic is evaluated on two sets of standard benchmark instances, and compared with the original artificial bee colony heuristic. The computational results show that the enhanced heuristic outperforms...

Reduction of herbivorous fish pressure can facilitate focal algal species forestation on artificial structures.

Science.gov (United States)

Gianni, Fabrizio; Bartolini, Fabrizio; Airoldi, Laura; Mangialajo, Luisa

2018-07-01

Coastal areas have been transformed worldwide by urbanization, so that artificial structures are now widespread. Current coastal development locally depletes many native marine species, while offering limited possibilities for their expansion. Eco-engineering interventions intend to identify ways to facilitate the presence of focal species and their associated functions on artificial habitats. An important but overlooked factor controlling restoration operations is overgrazing by herbivores. The aim of this study was to quantify the effects of different potential feeders on Cystoseira amentacea, a native canopy-forming alga of the Mediterranean infralittoral fringe, and test whether manipulation of grazing pressure can facilitate the human-guided installation of this focal species on coastal structures. Results of laboratory tests and field experiments revealed that Sarpa salpa, the only strictly native herbivorous fish in the Western Mediterranean Sea, can be a very effective grazer of C. amentacea in artificial habitats, up to as far as the infralittoral fringe, which is generally considered less accessible to fishes. S. salpa can limit the success of forestation operations in artificial novel habitats, causing up to 90% of Cystoseira loss after a few days. Other grazers, such as limpets and crabs, had only a moderate impact. Future engineering operations,intended to perform forestation of canopy-forming algae on artificial structures, should consider relevant biotic factors, such as fish overgrazing, identifying cost-effective techniques to limit their impact, as is the usual practice in restoration programmes on land. Copyright © 2018 Elsevier Ltd. All rights reserved.

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.

Energy profiling of demersal fish: a case-study in wind farm artificial reefs.

Science.gov (United States)

De Troch, Marleen; Reubens, Jan T; Heirman, Elke; Degraer, Steven; Vincx, Magda

2013-12-01

The construction of wind farms introduces artificial hard substrates in sandy sediments. As Atlantic cod (Gadus morhua) and pouting (Trisopterus luscus) tend to aggregate in order to feed around these reefs, energy profiling and trophic markers were applied to study their feeding ecology in a wind farm in the Belgian part of the North Sea. The proximate composition (carbohydrates, proteins and lipids) differed significantly between liver and muscle tissue but not between fish species or between their potential prey species. Atlantic cod showed to consume more energy than pouting. The latter had a higher overall energy reserve and can theoretically survive twice as long on the available energy than cod. In autumn, both fish species could survive longer on their energy than in spring. Polyunsaturated fatty acids were found in high concentrations in fish liver. The prey species Jassa and Pisidia were both rich in EPA while Jassa had a higher DHA content than Pisidia. Energy profiling supported the statement that wind farm artificial reefs are suitable feeding ground for both fish species. Sufficient energy levels were recorded and there is no indication of competition.

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....

Evolving and Controlling Perimeter, Rendezvous, and Foraging Behaviors in a Computation-Free Robot Swarm

Science.gov (United States)

2016-04-01

in extreme environments. Categories and Subject Descriptors I.2.11 [ Artificial Intelligence ]: Distributed Artificial In- telligence—multiagent systems...coherence and coordination; I.2.9 [ Artificial Intelligence ]: Robotics— intelligent vehi- cles Keywords swarm robotics, evolutionary algorithms...collective behaviors. Rubenstein et al. [12] studied how to collectively transport items using a simple control signals and behaviors. Others have looked

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.

Evaluating the Potential for Marine and Hydrokinetic Devices to Act As Artificial Reefs or Fish Aggregating Devices

Science.gov (United States)

Kramer, S.; Nelson, P.

2016-02-01

Wave energy converters (WECs) and tidal energy converters (TECs) are only beginning to be deployed along the U.S. West Coast and in Hawai'i, and a better understanding of their ecological effects on fish, particularly on special status fish is needed to facilitate project siting, design and environmental permitting. The structures of WECs and TECs placed on to the seabed, such as anchors and foundations, may function as artificial reefs that attract reef associated fishes, while the midwater and surface structures, such as mooring lines, buoys, and wave or tidal power devices, may function as fish aggregating devices (FADs). We evaluated these potential ecological interactions by comparing them to surrogate structures, such as artificial reefs, natural reefs, kelp vegetation, floating and sunken debris, oil and gas platforms, anchored FADs deployed to enhance fishing opportunities, net cages used for mariculture, and piers and marinas. We also conducted guided discussions with scientists and resource managers to provide unpublished observations. Our findings indicate the structures of WECs and TECs placed on or near the seabed in coastal waters of the U.S. West Coast and Hawai`i likely will function as small scale artificial reefs and attract potentially high densities of reef associated fishes and the midwater and surface structures of WECs placed in the tropical waters of Hawai`i likely will function as de facto FADs.

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.

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...

Artificial Intelligence and Economic Theories

OpenAIRE

Marwala, Tshilidzi; Hurwitz, Evan

2017-01-01

The advent of artificial intelligence has changed many disciplines such as engineering, social science and economics. Artificial intelligence is a computational technique which is inspired by natural intelligence such as the swarming of birds, the working of the brain and the pathfinding of the ants. These techniques have impact on economic theories. This book studies the impact of artificial intelligence on economic theories, a subject that has not been extensively studied. The theories that...

Optimal design of permanent magnet flux switching generator for wind applications via artificial neural network and multi-objective particle swarm optimization hybrid approach

International Nuclear Information System (INIS)

Meo, Santolo; Zohoori, Alireza; Vahedi, Abolfazl

2016-01-01

Highlights: • A new optimal design of flux switching permanent magnet generator is developed. • A prototype is employed to validate numerical data used for optimization. • A novel hybrid multi-objective particle swarm optimization approach is proposed. • Optimization targets are weight, cost, voltage and its total harmonic distortion. • The hybrid approach preference is proved compared with other optimization methods. - Abstract: In this paper a new hybrid approach obtained combining a multi-objective particle swarm optimization and artificial neural network is proposed for the design optimization of a direct-drive permanent magnet flux switching generators for low power wind applications. The targets of the proposed multi-objective optimization are to reduce the costs and weight of the machine while maximizing the amplitude of the induced voltage as well as minimizing its total harmonic distortion. The permanent magnet width, the stator and rotor tooth width, the rotor teeth number and stator pole number of the machine define the search space for the optimization problem. Four supervised artificial neural networks are designed for modeling the complex relationships among the weight, the cost, the amplitude and the total harmonic distortion of the output voltage respect to the quantities of the search space. Finite element analysis is adopted to generate training dataset for the artificial neural networks. Finite element analysis based model is verified by experimental results with a 1.5 kW permanent magnet flux switching generator prototype suitable for renewable energy applications, having 6/19 stator poles/rotor teeth. Finally the effectiveness of the proposed hybrid procedure is compared with the results given by conventional multi-objective optimization algorithms. The obtained results show the soundness of the proposed multi objective optimization technique and its feasibility to be adopted as suitable methodology for optimal design of permanent

[The research of near-infrared blood glucose measurement using particle swarm optimization and artificial neural network].

Science.gov (United States)

Dai, Juan; Ji, Zhong; Du, Yubao

2017-08-01

Existing near-infrared non-invasive blood glucose detection modelings mostly detect multi-spectral signals with different wavelength, which is not conducive to the popularization of non-invasive glucose meter at home and does not consider the physiological glucose dynamics of individuals. In order to solve these problems, this study presented a non-invasive blood glucose detection model combining particle swarm optimization (PSO) and artificial neural network (ANN) by using the 1 550 nm near-infrared absorbance as the independent variable and the concentration of blood glucose as the dependent variable, named as PSO-2ANN. The PSO-2ANN model was based on two sub-modules of neural networks with certain structures and arguments, and was built up after optimizing the weight coefficients of the two networks by particle swarm optimization. The results of 10 volunteers were predicted by PSO-2ANN. It was indicated that the relative error of 9 volunteers was less than 20%; 98.28% of the predictions of blood glucose by PSO-2ANN were distributed in the regions A and B of Clarke error grid, which confirmed that PSO-2ANN could offer higher prediction accuracy and better robustness by comparison with ANN. Additionally, even the physiological glucose dynamics of individuals may be different due to the influence of environment, temper, mental state and so on, PSO-2ANN can correct this difference only by adjusting one argument. The PSO-2ANN model provided us a new prospect to overcome individual differences in blood glucose prediction.

Organoleptic, physical, and chemical tests of artificial feed for milk fish substituted by earthworm meal (Lumbricus sp.

Directory of Open Access Journals (Sweden)

Siti Aslamyah

2013-11-01

Full Text Available Earthworms meal (Lumbricus sp. is very prospective as milkfish feed raw materials to substitute fish meal. Type of raw material and the exact composition will generate artificial feed quality with high levels of water stability, desirable, and safe for the fish. The purpose of this study to evaluate the quality of milkfish feed at different levels of fish meal substitution with earthworms (Lumbricus sp. based on organoleptic, physical, and chemical tests. The treatments tested levels of substitution of fish meal with earthworms meal in artificial feed milkfish, namely: feed A (0%; feed B (34,62%; feed C (65,38% and feed D (100%. The organoleptic and physical test showed that all the feed has a smooth texture, pungent aroma, and brown in color, with good water stability (rupture velocity ranged from 91,25±1,47 up to 92,87±1,67 minutes and dispersion of solids 11,14±1,55 up to 11,87±1,3%, hardness 84±0,18 up to 84,71±1,24%, sinking velocity 5,07±0,68 up to 5,64±0,17 cm/sec, the level of homogeneity of 81,34±0,17 up to 85,68±1,85%, the allure of 0,62±0,58 up to 0,65±0,12 cm/sec and delicious power of 0,059±0,024 up to 0,067±0,032 g/fish weight/day. The quality of feed is chemically with moisture content ranging from 8,4–9,1%, 16,7–19,46% ash, 31,07–32,37%, protein, 6,67–7,58% fat, crude fiber 7,45–7,87%, NFE (nitrogen free extracts 35,35–35,48%. Results show that different levels of substitution of fish meal with earthworms meal (Lumbricus sp. produces the same feed quality and contains nutrients in a range requirement milkfish. Accordingly, earthworms meal (Lumbricus sp. can be substituted for fish meal in fish milk feed artificial up to 100%.Keywords: substitution, fish meal, earthworms meal (Lumbricus sp., artificial feed, milkfish

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

Daily Reservoir Runoff Forecasting Method Using Artificial Neural Network Based on Quantum-behaved Particle Swarm Optimization

Directory of Open Access Journals (Sweden)

Chun-tian Cheng

2015-07-01

Full Text Available Accurate daily runoff forecasting is of great significance for the operation control of hydropower station and power grid. Conventional methods including rainfall-runoff models and statistical techniques usually rely on a number of assumptions, leading to some deviation from the exact results. Artificial neural network (ANN has the advantages of high fault-tolerance, strong nonlinear mapping and learning ability, which provides an effective method for the daily runoff forecasting. However, its training has certain drawbacks such as time-consuming, slow learning speed and easily falling into local optimum, which cannot be ignored in the real world application. In order to overcome the disadvantages of ANN model, the artificial neural network model based on quantum-behaved particle swarm optimization (QPSO, ANN-QPSO for short, is presented for the daily runoff forecasting in this paper, where QPSO was employed to select the synaptic weights and thresholds of ANN, while ANN was used for the prediction. The proposed model can combine the advantages of both QPSO and ANN to enhance the generalization performance of the forecasting model. The methodology is assessed by using the daily runoff data of Hongjiadu reservoir in southeast Guizhou province of China from 2006 to 2014. The results demonstrate that the proposed approach achieves much better forecast accuracy than the basic ANN model, and the QPSO algorithm is an alternative training technique for the ANN parameters selection.

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

A review on robotic fish enabled by ionic polymer-metal composite artificial muscles.

Science.gov (United States)

Chen, Zheng

2017-01-01

A novel actuating material, which is lightweight, soft, and capable of generating large flapping motion under electrical stimuli, is highly desirable to build energy-efficient and maneuverable bio-inspired underwater robots. Ionic polymer-metal composites are important category of electroactive polymers, since they can generate large bending motions under low actuation voltages. IPMCs are ideal artificial muscles for small-scale and bio-inspired robots. This paper takes a system perspective to review the recent work on IPMC-enabled underwater robots, from modeling, fabrication, and bio-inspired design perspectives. First, a physics-based and control-oriented model of IPMC actuator will be reviewed. Second, a bio-inspired robotic fish propelled by IPMC caudal fin will be presented and a steady-state speed model of the fish will be demonstrated. Third, a novel fabrication process for 3D actuating membrane will be introduced and a bio-inspired robotic manta ray propelled by two IPMC pectoral fins will be demonstrated. Fourth, a 2D maneuverable robotic fish propelled by multiple IPMC fin will be presented. Last, advantages and challenges of using IPMC artificial muscles in bio-inspired robots will be concluded.

Swarm Intelligence systems

International Nuclear Information System (INIS)

Beni, G.

1994-01-01

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

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.

Optimization of Artificial Propagation in Piracanjuba Fish Brycon orbignyanus Using Cryopreserved Semen.

Science.gov (United States)

Felizardo, V O; Melo, C C V; Murgas, L D S; Andrade, E S; Navarro, R D; Ftreitas, T F

BACKGROUND: Cryopreserved semen could facilitate procedures during the artificial reproduction in fish. Factors affecting cryopreservation efficiency are important to define efficient protocols. This study investigated the application of cryoprotectants on the quality of piracanjuba fish semen, the sperm concentration required for oocyte fertilization and spermatic activation. We evaluated two intracellular cryoprotectant solutions (DMSO and methanol) and two extracellular cryoprotectant solutions (egg yolk and lactose) to cryopreserved piracanjuba semen. Sperm motility rate, motility duration and spermatic alterations were assessed. The protocol for piracanjuba semen cryopreservation can use solutions including either DMSO or methanol as intracellular cryoprotectant and egg yolk or lactose as extracellular cryoprotectants.

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

Directory of Open Access Journals (Sweden)

Fereydoun Naghibi

2016-12-01

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

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.

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.

Artificial marine habitats favour a single fish species on a long-term scale: the dominance of Boops boops around off-shore fish cages

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Rodrigo Riera

2014-12-01

Full Text Available Off-shore fish cages are new artificial habitats that can affect pelagic fish assemblages and constitute an important food source for wild fish assemblages. This aggregation has noticeable ecological consequences in cage areas in impoverished ecosystems such as those in the Canary archipelago (NE Atlantic Ocean. However, this new habitat could be dominated by a single species, reducing its positive ecological effects. Wild fish assemblages associated with an off-shore fish lease on the northeastern coast of Tenerife (Canary Islands were sampled for six years. Fish assemblage structure beneath fish cages and at controls ( > 500 m from cages differed significantly between locations, with 13 times greater abundance at cage locations. These differences were mainly explained by the dominance of bogue (Boops boops around fish cages. This trend was consistent in the long-term throughout the study period (2004-2009, affecting local fisheries. The presence of fish cages significantly altered wild fish assemblages in the study area, enhancing mainly biomass and abundance of one species, bogue, and causing shifts in species composition.

Evolution of Collective Behaviour in an Artificial World Using Linguistic Fuzzy Rule-Based Systems.

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Jure Demšar

Full Text Available Collective behaviour is a fascinating and easily observable phenomenon, attractive to a wide range of researchers. In biology, computational models have been extensively used to investigate various properties of collective behaviour, such as: transfer of information across the group, benefits of grouping (defence against predation, foraging, group decision-making process, and group behaviour types. The question 'why,' however remains largely unanswered. Here the interest goes into which pressures led to the evolution of such behaviour, and evolutionary computational models have already been used to test various biological hypotheses. Most of these models use genetic algorithms to tune the parameters of previously presented non-evolutionary models, but very few attempt to evolve collective behaviour from scratch. Of these last, the successful attempts display clumping or swarming behaviour. Empirical evidence suggests that in fish schools there exist three classes of behaviour; swarming, milling and polarized. In this paper we present a novel, artificial life-like evolutionary model, where individual agents are governed by linguistic fuzzy rule-based systems, which is capable of evolving all three classes of behaviour.

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

Science.gov (United States)

Barlow, Peter W.; Fisahn, Joachim

2013-01-01

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

Co-combustion of peanut hull and coal blends: Artificial neural networks modeling, particle swarm optimization and Monte Carlo simulation.

Science.gov (United States)

Buyukada, Musa

2016-09-01

Co-combustion of coal and peanut hull (PH) were investigated using artificial neural networks (ANN), particle swarm optimization, and Monte Carlo simulation as a function of blend ratio, heating rate, and temperature. The best prediction was reached by ANN61 multi-layer perception model with a R(2) of 0.99994. Blend ratio of 90 to 10 (PH to coal, wt%), temperature of 305°C, and heating rate of 49°Cmin(-1) were determined as the optimum input values and yield of 87.4% was obtained under PSO optimized conditions. The validation experiments resulted in yields of 87.5%±0.2 after three replications. Monte Carlo simulations were used for the probabilistic assessments of stochastic variability and uncertainty associated with explanatory variables of co-combustion process. Copyright © 2016 Elsevier Ltd. All rights reserved.

Modeling and Flocking Consensus Analysis for Large-Scale UAV Swarms

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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.

POSTDOC : THE HUMAN OPTIMIZATION

OpenAIRE

Satish Gajawada

2013-01-01

This paper is dedicated to everyone who is interested in the Artificial Intelligence. John Henry Holland proposed Genetic Algorithm in the early 1970s. Ant Colony Optimization was proposed by Marco Dorigo in 1992. Particle Swarm Optimization was introduced by Kennedy and Eberhart in 1995. Storn and Price introduced Differential Evolution in 1996. K.M. Passino introduced Bacterial Foraging Optimization Algorithm in 2002. In 2003, X.L. Li proposed Artificial Fish Swarm Algorithm....

A Review of Artificial Lateral Line in Sensor Fabrication and Bionic Applications for Robot Fish.

Science.gov (United States)

Liu, Guijie; Wang, Anyi; Wang, Xinbao; Liu, Peng

2016-01-01

Lateral line is a system of sense organs that can aid fishes to maneuver in a dark environment. Artificial lateral line (ALL) imitates the structure of lateral line in fishes and provides invaluable means for underwater-sensing technology and robot fish control. This paper reviews ALL, including sensor fabrication and applications to robot fish. The biophysics of lateral line are first introduced to enhance the understanding of lateral line structure and function. The design and fabrication of an ALL sensor on the basis of various sensing principles are then presented. ALL systems are collections of sensors that include carrier and control circuit. Their structure and hydrodynamic detection are reviewed. Finally, further research trends and existing problems of ALL are discussed.

A Review of Artificial Lateral Line in Sensor Fabrication and Bionic Applications for Robot Fish

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Guijie Liu

2016-01-01

Full Text Available Lateral line is a system of sense organs that can aid fishes to maneuver in a dark environment. Artificial lateral line (ALL imitates the structure of lateral line in fishes and provides invaluable means for underwater-sensing technology and robot fish control. This paper reviews ALL, including sensor fabrication and applications to robot fish. The biophysics of lateral line are first introduced to enhance the understanding of lateral line structure and function. The design and fabrication of an ALL sensor on the basis of various sensing principles are then presented. ALL systems are collections of sensors that include carrier and control circuit. Their structure and hydrodynamic detection are reviewed. Finally, further research trends and existing problems of ALL are discussed.

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.

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

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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.

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.

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.

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.

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.

Artificial fish skin of self-powered micro-electromechanical systems hair cells for sensing hydrodynamic flow phenomena.

Science.gov (United States)

Asadnia, Mohsen; Kottapalli, Ajay Giri Prakash; Miao, Jianmin; Warkiani, Majid Ebrahimi; Triantafyllou, Michael S

2015-10-06

Using biological sensors, aquatic animals like fishes are capable of performing impressive behaviours such as super-manoeuvrability, hydrodynamic flow 'vision' and object localization with a success unmatched by human-engineered technologies. Inspired by the multiple functionalities of the ubiquitous lateral-line sensors of fishes, we developed flexible and surface-mountable arrays of micro-electromechanical systems (MEMS) artificial hair cell flow sensors. This paper reports the development of the MEMS artificial versions of superficial and canal neuromasts and experimental characterization of their unique flow-sensing roles. Our MEMS flow sensors feature a stereolithographically fabricated polymer hair cell mounted on Pb(Zr(0.52)Ti(0.48))O3 micro-diaphragm with floating bottom electrode. Canal-inspired versions are developed by mounting a polymer canal with pores that guide external flows to the hair cells embedded in the canal. Experimental results conducted employing our MEMS artificial superficial neuromasts (SNs) demonstrated a high sensitivity and very low threshold detection limit of 22 mV/(mm s(-1)) and 8.2 µm s(-1), respectively, for an oscillating dipole stimulus vibrating at 35 Hz. Flexible arrays of such superficial sensors were demonstrated to localize an underwater dipole stimulus. Comparative experimental studies revealed a high-pass filtering nature of the canal encapsulated sensors with a cut-off frequency of 10 Hz and a flat frequency response of artificial SNs. Flexible arrays of self-powered, miniaturized, light-weight, low-cost and robust artificial lateral-line systems could enhance the capabilities of underwater vehicles. © 2015 The Author(s).

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.

Atmospheric dispersion prediction and source estimation of hazardous gas using artificial neural network, particle swarm optimization and expectation maximization

Science.gov (United States)

Qiu, Sihang; Chen, Bin; Wang, Rongxiao; Zhu, Zhengqiu; Wang, Yuan; Qiu, Xiaogang

2018-04-01

Hazardous gas leak accident has posed a potential threat to human beings. Predicting atmospheric dispersion and estimating its source become increasingly important in emergency management. Current dispersion prediction and source estimation models cannot satisfy the requirement of emergency management because they are not equipped with high efficiency and accuracy at the same time. In this paper, we develop a fast and accurate dispersion prediction and source estimation method based on artificial neural network (ANN), particle swarm optimization (PSO) and expectation maximization (EM). The novel method uses a large amount of pre-determined scenarios to train the ANN for dispersion prediction, so that the ANN can predict concentration distribution accurately and efficiently. PSO and EM are applied for estimating the source parameters, which can effectively accelerate the process of convergence. The method is verified by the Indianapolis field study with a SF6 release source. The results demonstrate the effectiveness of the method.

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.

Dynamic Analysis of a Phytoplankton-Fish Model with Biological and Artificial Control

OpenAIRE

Wang, Yapei; Zhao, Min; Pan, Xinhong; Dai, Chuanjun

2014-01-01

We investigate a nonlinear model of the interaction between phytoplankton and fish, which uses a pair of semicontinuous systems with biological and artificial control. First, the existence of an order-1 periodic solution to the system is analyzed using a Poincaré map and a geometric method. The stability conditions of the order-1 periodic solution are obtained by a theoretical mathematical analysis. Furthermore, based on previous analysis, we investigate the bifurcation in the order-1 periodi...

Particle swarm optimization based support vector machine for damage level prediction of non-reshaped berm breakwater

Digital Repository Service at National Institute of Oceanography (India)

Harish, N.; Mandal, S.; Rao, S.; Patil, S.G.

breakwater. Soft computing tools like Artificial Neural Network, Fuzzy Logic, Support Vector Machine (SVM), etc, are successfully used to solve complex problems. In the present study, SVM and hybrid of Particle Swarm Optimization (PSO) with SVM (PSO...

Artificial muscles from fishing line and sewing thread.

Science.gov (United States)

Haines, Carter S; Lima, Márcio D; Li, Na; Spinks, Geoffrey M; Foroughi, Javad; Madden, John D W; Kim, Shi Hyeong; Fang, Shaoli; Jung de Andrade, Mônica; Göktepe, Fatma; Göktepe, Özer; Mirvakili, Seyed M; Naficy, Sina; Lepró, Xavier; Oh, Jiyoung; Kozlov, Mikhail E; Kim, Seon Jeong; Xu, Xiuru; Swedlove, Benjamin J; Wallace, Gordon G; Baughman, Ray H

2014-02-21

The high cost of powerful, large-stroke, high-stress artificial muscles has combined with performance limitations such as low cycle life, hysteresis, and low efficiency to restrict applications. We demonstrated that inexpensive high-strength polymer fibers used for fishing line and sewing thread can be easily transformed by twist insertion to provide fast, scalable, nonhysteretic, long-life tensile and torsional muscles. Extreme twisting produces coiled muscles that can contract by 49%, lift loads over 100 times heavier than can human muscle of the same length and weight, and generate 5.3 kilowatts of mechanical work per kilogram of muscle weight, similar to that produced by a jet engine. Woven textiles that change porosity in response to temperature and actuating window shutters that could help conserve energy were also demonstrated. Large-stroke tensile actuation was theoretically and experimentally shown to result from torsional actuation.

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.

Use of a parallel artificial membrane system to evaluate passive absorption and elimination in small fish.

Science.gov (United States)

Kwon, Jung-Hwan; Katz, Lynn E; Liljestrand, Howard M

2006-12-01

A parallel artificial lipid membrane system was developed to mimic passive mass transfer of hydrophobic organic chemicals in fish. In this physical model system, a membrane filter-supported lipid bilayer separates two aqueous phases that represent the external and internal aqueous environments of fish. To predict bioconcentration kinetics in small fish with this system, literature absorption and elimination rates were analyzed with an allometric diffusion model to quantify the mass transfer resistances in the aqueous and lipid phases of fish. The effect of the aqueous phase mass transfer resistance was controlled by adjusting stirring intensity to mimic bioconcentration rates in small fish. Twenty-three simple aromatic hydrocarbons were chosen as model compounds for purposes of evaluation. For most of the selected chemicals, literature absorption/elimination rates fall into the range predicted from measured membrane permeabilities and elimination rates of the selected chemicals determined by the diffusion model system.

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.

Analysis of micro-failure behaviors in artificial muscles based on fishing line and sewing thread

Science.gov (United States)

Xu, J. B.; Cheng, K. F.; Tu, S. L.; He, X. M.; Ma, C.; Jin, Y. Z.; Kang, X. N.; Sun, T.; Zhang, Y.

2017-06-01

The aim of the present study was to discuss a new and effective method for testing artificial muscles based on micro-failure behaviors analysis. Thermo-mechanical actuators based on fishing line and sewing thread, also, the capability of responding to ambient temperature variations producing a large amount of shrinkage ratio of a resulting variation in longitudinal length. The minimum micro-failure value is 0.02μm and the maximum value is 1.72μm with nylon twist pattern. The discovery of an innovative effective testing of artificial muscles based on polymeric fibers specimens on micro-failure, rupture, slippage, etc. This research finds out a micro-failure behavior analysis of thermo-mechanical actuators based on fishing line and sewing thread. The specimens show large deformations when heated together with warping performance in terms of shrinkage of energy and densities. With the purpose of providing useful analysis data for the further technology applications, we attempt micrometre-sized artificial muscles which were also tested was readily accessible and also can be applied to other polymeric fibers. Effective use of this technique achievement relies on rotate speed, temperature and tensile direction. The results of the tensile testing experiments were outstanding with respect to some important issues related to the response of micro-structure, twisted polymeric fibers and shrinkage ratio.

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

Science.gov (United States)

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

2017-12-01

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

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.

A Hybrid Forecasting Model Based on Bivariate Division and a Backpropagation Artificial Neural Network Optimized by Chaos Particle Swarm Optimization for Day-Ahead Electricity Price

Directory of Open Access Journals (Sweden)

Zhilong Wang

2014-01-01

Full Text Available In the electricity market, the electricity price plays an inevitable role. Nevertheless, accurate price forecasting, a vital factor affecting both government regulatory agencies and public power companies, remains a huge challenge and a critical problem. Determining how to address the accurate forecasting problem becomes an even more significant task in an era in which electricity is increasingly important. Based on the chaos particle swarm optimization (CPSO, the backpropagation artificial neural network (BPANN, and the idea of bivariate division, this paper proposes a bivariate division BPANN (BD-BPANN method and the CPSO-BD-BPANN method for forecasting electricity price. The former method creatively transforms the electricity demand and price to be a new variable, named DV, which is calculated using the division principle, to forecast the day-ahead electricity by multiplying the forecasted values of the DVs and forecasted values of the demand. Next, to improve the accuracy of BD-BPANN, chaos particle swarm optimization and BD-BPANN are synthesized to form a novel model, CPSO-BD-BPANN. In this study, CPSO is utilized to optimize the initial parameters of BD-BPANN to make its output more stable than the original model. Finally, two forecasting strategies are proposed regarding different situations.

Discrete particle swarm optimization for identifying community structures in signed social networks.

Science.gov (United States)

Cai, Qing; Gong, Maoguo; Shen, Bo; Ma, Lijia; Jiao, Licheng

2014-10-01

Modern science of networks has facilitated us with enormous convenience to the understanding of complex systems. Community structure is believed to be one of the notable features of complex networks representing real complicated systems. Very often, uncovering community structures in networks can be regarded as an optimization problem, thus, many evolutionary algorithms based approaches have been put forward. Particle swarm optimization (PSO) is an artificial intelligent algorithm originated from social behavior such as birds flocking and fish schooling. PSO has been proved to be an effective optimization technique. However, PSO was originally designed for continuous optimization which confounds its applications to discrete contexts. In this paper, a novel discrete PSO algorithm is suggested for identifying community structures in signed networks. In the suggested method, particles' status has been redesigned in discrete form so as to make PSO proper for discrete scenarios, and particles' updating rules have been reformulated by making use of the topology of the signed network. Extensive experiments compared with three state-of-the-art approaches on both synthetic and real-world signed networks demonstrate that the proposed method is effective and promising. Copyright © 2014 Elsevier Ltd. All rights reserved.

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.

Artificial reefs and reef restoration in the Laurentian Great Lakes

Science.gov (United States)

McLean, Matthew W.; Roseman, Edward; Pritt, Jeremy J.; Kennedy, Gregory W.; Manny, Bruce A.

2015-01-01

We reviewed the published literature to provide an inventory of Laurentian Great Lakes artificial reef projects and their purposes. We also sought to characterize physical and biological monitoring for artificial reef projects in the Great Lakes and determine the success of artificial reefs in meeting project objectives. We found records of 6 artificial reefs in Lake Erie, 8 in Lake Michigan, 3 in Lakes Huron and Ontario, and 2 in Lake Superior. We found 9 reefs in Great Lakes connecting channels and 6 reefs in Great Lakes tributaries. Objectives of artificial reef creation have included reducing impacts of currents and waves, providing safe harbors, improving sport-fishing opportunities, and enhancing/restoring fish spawning habitats. Most reefs in the lakes themselves were incidental (not created purposely for fish habitat) or built to improve local sport fishing, whereas reefs in tributaries and connecting channels were more frequently built to benefit fish spawning. Levels of assessment of reef performance varied; but long-term monitoring was uncommon as was assessment of physical attributes. Artificial reefs were often successful at attracting recreational species and spawning fish; however, population-level benefits of artificial reefs are unclear. Stressors such as sedimentation and bio-fouling can limit the effectiveness of artificial reefs as spawning enhancement tools. Our investigation underscores the need to develop standard protocols for monitoring the biological and physical attributes of artificial structures. Further, long-term monitoring is needed to assess the benefits of artificial reefs to fish populations and inform future artificial reef projects.

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

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...

Transformer Incipient Fault Prediction Using Combined Artificial Neural Network and Various Particle Swarm Optimisation Techniques.

Directory of Open Access Journals (Sweden)

Hazlee Azil Illias

Full Text Available It is important to predict the incipient fault in transformer oil accurately so that the maintenance of transformer oil can be performed correctly, reducing the cost of maintenance and minimise the error. Dissolved gas analysis (DGA has been widely used to predict the incipient fault in power transformers. However, sometimes the existing DGA methods yield inaccurate prediction of the incipient fault in transformer oil because each method is only suitable for certain conditions. Many previous works have reported on the use of intelligence methods to predict the transformer faults. However, it is believed that the accuracy of the previously proposed methods can still be improved. Since artificial neural network (ANN and particle swarm optimisation (PSO techniques have never been used in the previously reported work, this work proposes a combination of ANN and various PSO techniques to predict the transformer incipient fault. The advantages of PSO are simplicity and easy implementation. The effectiveness of various PSO techniques in combination with ANN is validated by comparison with the results from the actual fault diagnosis, an existing diagnosis method and ANN alone. Comparison of the results from the proposed methods with the previously reported work was also performed to show the improvement of the proposed methods. It was found that the proposed ANN-Evolutionary PSO method yields the highest percentage of correct identification for transformer fault type than the existing diagnosis method and previously reported works.

Transformer Incipient Fault Prediction Using Combined Artificial Neural Network and Various Particle Swarm Optimisation Techniques.

Science.gov (United States)

Illias, Hazlee Azil; Chai, Xin Rui; Abu Bakar, Ab Halim; Mokhlis, Hazlie

2015-01-01

It is important to predict the incipient fault in transformer oil accurately so that the maintenance of transformer oil can be performed correctly, reducing the cost of maintenance and minimise the error. Dissolved gas analysis (DGA) has been widely used to predict the incipient fault in power transformers. However, sometimes the existing DGA methods yield inaccurate prediction of the incipient fault in transformer oil because each method is only suitable for certain conditions. Many previous works have reported on the use of intelligence methods to predict the transformer faults. However, it is believed that the accuracy of the previously proposed methods can still be improved. Since artificial neural network (ANN) and particle swarm optimisation (PSO) techniques have never been used in the previously reported work, this work proposes a combination of ANN and various PSO techniques to predict the transformer incipient fault. The advantages of PSO are simplicity and easy implementation. The effectiveness of various PSO techniques in combination with ANN is validated by comparison with the results from the actual fault diagnosis, an existing diagnosis method and ANN alone. Comparison of the results from the proposed methods with the previously reported work was also performed to show the improvement of the proposed methods. It was found that the proposed ANN-Evolutionary PSO method yields the highest percentage of correct identification for transformer fault type than the existing diagnosis method and previously reported works.

Transformer Incipient Fault Prediction Using Combined Artificial Neural Network and Various Particle Swarm Optimisation Techniques

Science.gov (United States)

2015-01-01

It is important to predict the incipient fault in transformer oil accurately so that the maintenance of transformer oil can be performed correctly, reducing the cost of maintenance and minimise the error. Dissolved gas analysis (DGA) has been widely used to predict the incipient fault in power transformers. However, sometimes the existing DGA methods yield inaccurate prediction of the incipient fault in transformer oil because each method is only suitable for certain conditions. Many previous works have reported on the use of intelligence methods to predict the transformer faults. However, it is believed that the accuracy of the previously proposed methods can still be improved. Since artificial neural network (ANN) and particle swarm optimisation (PSO) techniques have never been used in the previously reported work, this work proposes a combination of ANN and various PSO techniques to predict the transformer incipient fault. The advantages of PSO are simplicity and easy implementation. The effectiveness of various PSO techniques in combination with ANN is validated by comparison with the results from the actual fault diagnosis, an existing diagnosis method and ANN alone. Comparison of the results from the proposed methods with the previously reported work was also performed to show the improvement of the proposed methods. It was found that the proposed ANN-Evolutionary PSO method yields the highest percentage of correct identification for transformer fault type than the existing diagnosis method and previously reported works. PMID:26103634

Analysis and optimization of a camber morphing wing model

Directory of Open Access Journals (Sweden)

Bing Li

2016-09-01

Full Text Available This article proposes a camber morphing wing model that can continuously change its camber. A mathematical model is proposed and a kinematic simulation is performed to verify the wing’s ability to change camber. An aerodynamic model is used to test its aerodynamic characteristics. Some important aerodynamic analyses are performed. A comparative analysis is conducted to explore the relationships between aerodynamic parameters, the rotation angle of the trailing edge, and the angle of attack. An improved artificial fish swarm optimization algorithm is proposed, referred to as the weighted adaptive artificial fish-swarm with embedded Hooke–Jeeves search method. Some comparison tests are used to test the performance of the improved optimization algorithm. Finally, the proposed optimization algorithm is used to optimize the proposed camber morphing wing model.

Particle swarm optimization-based support vector machine for forecasting dissolved gases content in power transformer oil

Energy Technology Data Exchange (ETDEWEB)

Fei, Sheng-wei; Wang, Ming-Jun; Miao, Yu-bin; Tu, Jun; Liu, Cheng-liang [School of Mechanical Engineering, Shanghai Jiaotong University, Shanghai 200240 (China)

2009-06-15

Forecasting of dissolved gases content in power transformer oil is a complicated problem due to its nonlinearity and the small quantity of training data. Support vector machine (SVM) has been successfully employed to solve regression problem of nonlinearity and small sample. However, the practicability of SVM is effected due to the difficulty of selecting appropriate SVM parameters. Particle swarm optimization (PSO) is a new optimization method, which is motivated by social behaviour of organisms such as bird flocking and fish schooling. The method not only has strong global search capability, but also is very easy to implement. Thus, the proposed PSO-SVM model is applied to forecast dissolved gases content in power transformer oil in this paper, among which PSO is used to determine free parameters of support vector machine. The experimental data from several electric power companies in China is used to illustrate the performance of proposed PSO-SVM model. The experimental results indicate that the PSO-SVM method can achieve greater forecasting accuracy than grey model, artificial neural network under the circumstances of small sample. (author)

Particle swarm optimization-based support vector machine for forecasting dissolved gases content in power transformer oil

Energy Technology Data Exchange (ETDEWEB)

Fei Shengwei [School of Mechanical Engineering, Shanghai Jiaotong University, Shanghai 200240 (China)], E-mail: feishengwei@sohu.com; Wang Mingjun; Miao Yubin; Tu Jun; Liu Chengliang [School of Mechanical Engineering, Shanghai Jiaotong University, Shanghai 200240 (China)

2009-06-15

Forecasting of dissolved gases content in power transformer oil is a complicated problem due to its nonlinearity and the small quantity of training data. Support vector machine (SVM) has been successfully employed to solve regression problem of nonlinearity and small sample. However, the practicability of SVM is effected due to the difficulty of selecting appropriate SVM parameters. Particle swarm optimization (PSO) is a new optimization method, which is motivated by social behaviour of organisms such as bird flocking and fish schooling. The method not only has strong global search capability, but also is very easy to implement. Thus, the proposed PSO-SVM model is applied to forecast dissolved gases content in power transformer oil in this paper, among which PSO is used to determine free parameters of support vector machine. The experimental data from several electric power companies in China is used to illustrate the performance of proposed PSO-SVM model. The experimental results indicate that the PSO-SVM method can achieve greater forecasting accuracy than grey model, artificial neural network under the circumstances of small sample.

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

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

Artificial reefs: “Attraction versus Production”

Directory of Open Access Journals (Sweden)

Eduardo Barros Fagundes Netto

2011-04-01

Full Text Available The production of fish is the most common reason for the construction and installation of an artificial reef. More recently, environmental concerns and conservation of biological resources have been instrumental to the formulation of new goals of the research. One of the issues to be resolved is the biological function of “attraction vs. production” as a result of the use of artificial reefs. The uncertainty as to the answer to the question whether the artificial reefs will or not benefit the development of fish stocks could be solved if the artificial reefs would be managed as marine protected areas.

Study on network traffic forecast model of SVR optimized by GAFSA

International Nuclear Information System (INIS)

Liu, Yuan; Wang, RuiXue

2016-01-01

There are some problems, such as low precision, on existing network traffic forecast model. In accordance with these problems, this paper proposed the network traffic forecast model of support vector regression (SVR) algorithm optimized by global artificial fish swarm algorithm (GAFSA). GAFSA constitutes an improvement of artificial fish swarm algorithm, which is a swarm intelligence optimization algorithm with a significant effect of optimization. The optimum training parameters used for SVR could be calculated by optimizing chosen parameters, which would make the forecast more accurate. With the optimum training parameters searched by GAFSA algorithm, a model of network traffic forecast, which greatly solved problems of great errors in SVR improved by others intelligent algorithms, could be built with the forecast result approaching stability and the increased forecast precision. The simulation shows that, compared with other models (e.g. GA-SVR, CPSO-SVR), the forecast results of GAFSA-SVR network traffic forecast model is more stable with the precision improved to more than 89%, which plays an important role on instructing network control behavior and analyzing security situation.

Improved discrete swarm intelligence algorithms for endmember extraction from hyperspectral remote sensing images

Science.gov (United States)

Su, Yuanchao; Sun, Xu; Gao, Lianru; Li, Jun; Zhang, Bing

2016-10-01

Endmember extraction is a key step in hyperspectral unmixing. A new endmember extraction framework is proposed for hyperspectral endmember extraction. The proposed approach is based on the swarm intelligence (SI) algorithm, where discretization is used to solve the SI algorithm because pixels in a hyperspectral image are naturally defined within a discrete space. Moreover, a "distance" factor is introduced into the objective function to limit the endmember numbers which is generally limited in real scenarios, while traditional SI algorithms likely produce superabundant spectral signatures, which generally belong to the same classes. Three endmember extraction methods are proposed based on the artificial bee colony, ant colony optimization, and particle swarm optimization algorithms. Experiments with both simulated and real hyperspectral images indicate that the proposed framework can improve the accuracy of endmember extraction.

Escalated convergent artificial bee colony

Science.gov (United States)

Jadon, Shimpi Singh; Bansal, Jagdish Chand; Tiwari, Ritu

2016-03-01

Artificial bee colony (ABC) optimisation algorithm is a recent, fast and easy-to-implement population-based meta heuristic for optimisation. ABC has been proved a rival algorithm with some popular swarm intelligence-based algorithms such as particle swarm optimisation, firefly algorithm and ant colony optimisation. The solution search equation of ABC is influenced by a random quantity which helps its search process in exploration at the cost of exploitation. In order to find a fast convergent behaviour of ABC while exploitation capability is maintained, in this paper basic ABC is modified in two ways. First, to improve exploitation capability, two local search strategies, namely classical unidimensional local search and levy flight random walk-based local search are incorporated with ABC. Furthermore, a new solution search strategy, namely stochastic diffusion scout search is proposed and incorporated into the scout bee phase to provide more chance to abandon solution to improve itself. Efficiency of the proposed algorithm is tested on 20 benchmark test functions of different complexities and characteristics. Results are very promising and they prove it to be a competitive algorithm in the field of swarm intelligence-based algorithms.

Comparison of particle swarm optimization and other metaheuristics on electricity demand estimation: A case study of Iran

International Nuclear Information System (INIS)

Askarzadeh, Alireza

2014-01-01

The importance of energy demand estimation stems from energy planning, formulating strategies and recommending energy policies. Most often, energy demand is mathematically formulated by socio-economic indicators. The challenging problem is to determine the optimal or near optimal weighting factors. Inspired by social behavior of bird flocking or fish schooling, PSO (particle swarm optimization) is a population-based search technique which has attracted significant attention to tackle the complexity of difficult optimization problems. This paper studies the performance of different PSO variants for estimating Iran's electricity demand. Seven PSO variants namely, original PSO, PSO-w (PSO with weighting factor), PSO-cf (PSO with constriction factor), PSO-rf (PSO with repulsion factor), PSO-vc (PSO with velocity control), CLPSO (comprehensive learning PSO) and a MPSO (modified PSO), are used to find the unknown weighting factors based on the data from 1982 to 2003. The validation process is then conducted by testing the optimized models by using the data from 2004 to 2009. It is seen that PSO-vc produces more promising results than the other variants, HS (harmony search) and ABSO (artificial bee swarm optimization) algorithms in terms of MAPE (mean absolute percentage error). This value is obtained 2.47 and 2.50 for the exponential and quadratic models, respectively. - Highlights: • Electricity demand estimation is modelled using socio-economic indicators. • Different PSO variants are investigated in terms of accuracy. • Exponential model can estimate the Iran's electricity demand with high accuracy. • PSO with velocity control produces more accurate result than the others

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...

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

Interacting Brownian Swarms: Some Analytical Results

Directory of Open Access Journals (Sweden)

Guillaume Sartoretti

2016-01-01

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

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...

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

Velocity correlations in laboratory insect swarms

Science.gov (United States)

Ni, R.; Ouellette, N. T.

2015-12-01

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

Particle swarm optimization with random keys applied to the nuclear reactor reload problem

Energy Technology Data Exchange (ETDEWEB)

Meneses, Anderson Alvarenga de Moura [Universidade Federal do Rio de Janeiro (UFRJ), RJ (Brazil). Coordenacao dos Programas de Pos-graduacao de Engenharia (COPPE). Programa de Engenharia Nuclear; Fundacao Educacional de Macae (FUNEMAC), RJ (Brazil). Faculdade Professor Miguel Angelo da Silva Santos; Machado, Marcelo Dornellas; Medeiros, Jose Antonio Carlos Canedo; Schirru, Roberto [Universidade Federal do Rio de Janeiro (UFRJ), RJ (Brazil). Coordenacao dos Programas de Pos-graduacao de Engenharia (COPPE). Programa de Engenharia Nuclear]. E-mails: ameneses@con.ufrj.br; marcelo@lmp.ufrj.br; canedo@lmp.ufrj.br; schirru@lmp.ufrj.br

2007-07-01

In 1995, Kennedy and Eberhart presented the Particle Swarm Optimization (PSO), an Artificial Intelligence metaheuristic technique to optimize non-linear continuous functions. The concept of Swarm Intelligence is based on the socials aspects of intelligence, it means, the ability of individuals to learn with their own experience in a group as well as to take advantage of the performance of other individuals. Some PSO models for discrete search spaces have been developed for combinatorial optimization, although none of them presented satisfactory results to optimize a combinatorial problem as the nuclear reactor fuel reloading problem (NRFRP). In this sense, we developed the Particle Swarm Optimization with Random Keys (PSORK) in previous research to solve Combinatorial Problems. Experiences demonstrated that PSORK performed comparable to or better than other techniques. Thus, PSORK metaheuristic is being applied in optimization studies of the NRFRP for Angra 1 Nuclear Power Plant. Results will be compared with Genetic Algorithms and the manual method provided by a specialist. In this experience, the problem is being modeled for an eight-core symmetry and three-dimensional geometry, aiming at the minimization of the Nuclear Enthalpy Power Peaking Factor as well as the maximization of the cycle length. (author)

Particle swarm optimization with random keys applied to the nuclear reactor reload problem

International Nuclear Information System (INIS)

Meneses, Anderson Alvarenga de Moura; Fundacao Educacional de Macae; Machado, Marcelo Dornellas; Medeiros, Jose Antonio Carlos Canedo; Schirru, Roberto

2007-01-01

In 1995, Kennedy and Eberhart presented the Particle Swarm Optimization (PSO), an Artificial Intelligence metaheuristic technique to optimize non-linear continuous functions. The concept of Swarm Intelligence is based on the socials aspects of intelligence, it means, the ability of individuals to learn with their own experience in a group as well as to take advantage of the performance of other individuals. Some PSO models for discrete search spaces have been developed for combinatorial optimization, although none of them presented satisfactory results to optimize a combinatorial problem as the nuclear reactor fuel reloading problem (NRFRP). In this sense, we developed the Particle Swarm Optimization with Random Keys (PSORK) in previous research to solve Combinatorial Problems. Experiences demonstrated that PSORK performed comparable to or better than other techniques. Thus, PSORK metaheuristic is being applied in optimization studies of the NRFRP for Angra 1 Nuclear Power Plant. Results will be compared with Genetic Algorithms and the manual method provided by a specialist. In this experience, the problem is being modeled for an eight-core symmetry and three-dimensional geometry, aiming at the minimization of the Nuclear Enthalpy Power Peaking Factor as well as the maximization of the cycle length. (author)

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

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.)

Monitoring of the artificial reef fish assemblages of golfe juan marine protected area (France, North-Western Mediterranean

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Bodilis Pascaline

2011-01-01

Full Text Available Artificial reefs were deployed within the Golfe-Juan marine protected area (Alpes-Maritimes coast, France, Northwestern Mediterranean created in 1981. This no-take area is fully protected since its establishment, except in 2004 when some anthropic activities were, exceptionally, authorized. Moreover, no park rangers to prevent poaching since 2002 occur. In order to carry out a long term monitoring of the artificial reef fish assemblages, underwater visual censuses (UVC were carried out in 1988, 1998 and 2008, according to a traditional standardized visual census method that taken into account all fish species. The complexification of some large reefs built with wide voide spaces called Bonna reefs appear to be a good solution to increase species richness and density. Species richness and density of the fish assemblages showed significant increase between 1988 and 1998. However the fast increasing was stopped from 1998 and 2008 probably due to a lack of law enforcement and poaching. Despite artificial reefs were deployed in MPA since at least 20 years, they did not show a real positive impact on fish assemblages. These results could be explained (i by a lack of law enforcement patrol within the protected areas during the last decade, and (ii by the one-year opening to fishing activities within MPA. The real effectiveness of the artificial reefs in sustaining fish assemblages is discussed and the necessity of a regular and efficient control by park rangers is highlighted.Recifes artificiais foram implantados na área protegida Golfe-Juan (costa dos Alpes-Maritimes, Noroeste do Mediterraneo criada em 1981. Esta área NTZ (Area de Restrição da Pesca é inteiramente protegida, desde seu estabelecimento, exceto em 2004, quando algumas atividades antropicas foram excepcionalmente autorizadas. Além disso, desde 2002, não houve nenhuma patrulha florestal para impedir a caça e pesca ilegais. . A fim realizar um monitoramento a longo prazo das assembl

Spatial partition of artificial structures by fish at the surroundings of the conservation unit - Parque Estadual da Ilha Anchieta, SP, Brazil

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Patricia Teresa Monteiro Cunningham

2004-03-01

Full Text Available The aim of this work was to study the spatial partition dynamics of fish at artificial structures. Holed structured concrete blocks were used to construct eight identical artificial structures and disposed between 3m-6m depths. Installation was made in two steps during 1996 (May/June and November/December and daily observations were carried out during 30 consecutive days SCUBA diving. The artificial reef areas were used in discriminated ways by the fish community and was most probably influenced by several factor, mainly biotic. The results of the Krustal-Wallis test led to the refutation of the hypothesis that the artificial structure spaces were shared and randomly used by fish.Este trabalho é parte integrante de um projeto maior realizado pelo Laboratório ECOPEX/IOUSP. Foi desenvolvido nos entornos do Parque Estadual da Ilha Anchieta, Ubatuba, litoral norte de São Paulo, com o objetivo de estudar a dinâmica de repartição espacial dos peixes em estruturas artificiais e de testar a hipótese "os peixes repartem e utilizam aleatoriamente o espaço das estruturas artificiais". Utilizando-se blocos de concreto vazados, foram construídas e colocadas entre 3m - 6m, oito estruturas artificias idênticas. A instalação foi feita em duas etapas durante o ano de 1996 (Maio/Junho e Novembro/Dezembro e as observações efetuadas diariamente durante 30 dias consecutivos usando equipamento de mergulho autônomo. A ictiofauna utilizou de forma diferenciada as áreas dos recifes artificiais, influenciada provavelmente por vários fatores, principalmente bióticos. Os resultados do teste de Kruskal-Wallis levaram a refutar a hipótese desse estudo.

Surface-Chemistry-Mediated Control of Individual Magnetic Helical Microswimmers in a Swarm.

Science.gov (United States)

Wang, Xiaopu; Hu, Chengzhi; Schurz, Lukas; De Marco, Carmela; Chen, Xiangzhong; Pané, Salvador; Nelson, Bradley J

2018-05-31

Magnetic helical microswimmers, also known as artificial bacterial flagella (ABFs), perform 3D navigation in various liquids under low-strength rotating magnetic fields by converting rotational motion to translational motion. ABFs have been widely studied as carriers for targeted delivery and release of drugs and cells. For in vivo/ in vitro therapeutic applications, control over individual groups of swimmers within a swarm is necessary for several biomedical applications such as drug delivery or small-scale surgery. In this work, we present the selective control of individual swimmers in a swarm of geometrically and magnetically identical ABFs by modifying their surface chemistry. We confirm experimentally and analytically that the forward/rotational velocity ratio of ABFs is independent of their surface coatings when the swimmers are operated below their step-out frequency (the frequency requiring the entire available magnetic torque to maintain synchronous rotation). We also show that ABFs with hydrophobic surfaces exhibit larger step-out frequencies and higher maximum forward velocities compared to their hydrophilic counterparts. Thus, selective control of a group of swimmers within a swarm of ABFs can be achieved by operating the selected ABFs at a frequency that is below their step-out frequencies but higher than the step-out frequencies of unselected ABFs. The feasibility of this method is investigated in water and in biologically relevant solutions. Selective control is also demonstrated inside a Y-shaped microfluidic channel. Our results present a systematic approach for realizing selective control within a swarm of magnetic helical microswimmers.

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

Science.gov (United States)

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

2013-10-06

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

Effect of The Phytase Enzyme Addition in The Artificial Feed on Digestibility of Feed, Feed Conversion Ratio and Growth of Gift Tilapia Saline Fish (Oreochromis niloticus) Nursery Stadia I

Science.gov (United States)

Rachmawati, Diana; Samidjan, Istiyanto; Elfitasari, Tita

2018-02-01

The purpose of this study was to determine the effect of adding the phytase enzyme in the artificial feed on digestibility of feed, feed conversion ratio and growth of gift tilapia saline fish (Oreochromis niloticus) nursery stadia I. The fish samples in this study used gift tilapia saline fish (O. niloticus) with an average weight of 0,62 ± 0,008 g/fish and the stocking density of 1 fish1 L. Experimental method used in this study was completely randomized design with 4 treatments and 3 repetitions. The treatments were by adding phytase enzyme in artificial feed with the different level of doses those were A (0 FTU kg1 feed), B (500 FTU kg1 feed), C (1000 FTU kg1 feed) and D (1500 FTU kg1 feed). The results show that the addition of phytase enzyme was significantly (P0.05) affected on Survival Rate (SR) of gift tilapia saline fish. The optimum doses of phytase enzyme on RGR, FCR, PER, ADCP and ADCF of gift tilapia saline fish ranged from 1060 to 1100 FTU kg-1 feed.

Identification of nuclear power plant transients using the Particle Swarm Optimization algorithm

International Nuclear Information System (INIS)

Canedo Medeiros, Jose Antonio Carlos; Schirru, Roberto

2008-01-01

In order to help nuclear power plant operator reduce his cognitive load and increase his available time to maintain the plant operating in a safe condition, transient identification systems have been devised to help operators identify possible plant transients and take fast and right corrective actions in due time. In the design of classification systems for identification of nuclear power plants transients, several artificial intelligence techniques, involving expert systems, neuro-fuzzy and genetic algorithms have been used. In this work we explore the ability of the Particle Swarm Optimization algorithm (PSO) as a tool for optimizing a distance-based discrimination transient classification method, giving also an innovative solution for searching the best set of prototypes for identification of transients. The Particle Swarm Optimization algorithm was successfully applied to the optimization of a nuclear power plant transient identification problem. Comparing the PSO to similar methods found in literature it has shown better results

Identification of nuclear power plant transients using the Particle Swarm Optimization algorithm

Energy Technology Data Exchange (ETDEWEB)

Canedo Medeiros, Jose Antonio Carlos [Universidade Federal do Rio de Janeiro, PEN/COPPE, UFRJ, Ilha do Fundao s/n, CEP 21945-970 Rio de Janeiro (Brazil)], E-mail: canedo@lmp.ufrj.br; Schirru, Roberto [Universidade Federal do Rio de Janeiro, PEN/COPPE, UFRJ, Ilha do Fundao s/n, CEP 21945-970 Rio de Janeiro (Brazil)], E-mail: schirru@lmp.ufrj.br

2008-04-15

In order to help nuclear power plant operator reduce his cognitive load and increase his available time to maintain the plant operating in a safe condition, transient identification systems have been devised to help operators identify possible plant transients and take fast and right corrective actions in due time. In the design of classification systems for identification of nuclear power plants transients, several artificial intelligence techniques, involving expert systems, neuro-fuzzy and genetic algorithms have been used. In this work we explore the ability of the Particle Swarm Optimization algorithm (PSO) as a tool for optimizing a distance-based discrimination transient classification method, giving also an innovative solution for searching the best set of prototypes for identification of transients. The Particle Swarm Optimization algorithm was successfully applied to the optimization of a nuclear power plant transient identification problem. Comparing the PSO to similar methods found in literature it has shown better results.

Bifurcating Particle Swarms in Smooth-Walled Fractures

Science.gov (United States)

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

2010-12-01

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

ACOUSTIC CLASSIFICATION OF FRESHWATER FISH SPECIES USING ARTIFICIAL NEURAL NETWORK: EVALUATION OF THE MODEL PERFORMANCE

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Zulkarnaen Fahmi

2013-06-01

Full Text Available Hydroacoustic techniques are a valuable tool for the stock assessments of many fish species. Nonetheless, such techniques are limited by problems of species identification. Several methods and techniques have been used in addressing the problem of acoustic identification species and one of them is Artificial Neural Networks (ANNs. In this paper, Back propagation (BP and Multi Layer Perceptron (MLP of the Artificial Neural Network were used to classify carp (Cyprinus carpio, tilapia (Oreochromis niloticus, and catfish (Pangasius hypothalmus. Classification was done using a set of descriptors extracted from the acoustic data records, i.e. Volume Back scattering (Sv, Target Strength (TS, Area Back scattering Strength, Skewness, Kurtosis, Depth, Height and Relative altitude. The results showed that the Multi Layer Perceptron approach performed better than the Back propagation. The classification rates was 85.7% with the multi layer perceptron (MLP compared to 84.8% with back propagation (BP ANN.

Enhancing Artificial Bee Colony Algorithm with Self-Adaptive Searching Strategy and Artificial Immune Network Operators for Global Optimization

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Tinggui Chen

2014-01-01

Full Text Available Artificial bee colony (ABC algorithm, inspired by the intelligent foraging behavior of honey bees, was proposed by Karaboga. It has been shown to be superior to some conventional intelligent algorithms such as genetic algorithm (GA, artificial colony optimization (ACO, and particle swarm optimization (PSO. However, the ABC still has some limitations. For example, ABC can easily get trapped in the local optimum when handing in functions that have a narrow curving valley, a high eccentric ellipse, or complex multimodal functions. As a result, we proposed an enhanced ABC algorithm called EABC by introducing self-adaptive searching strategy and artificial immune network operators to improve the exploitation and exploration. The simulation results tested on a suite of unimodal or multimodal benchmark functions illustrate that the EABC algorithm outperforms ACO, PSO, and the basic ABC in most of the experiments.

Particle swarm optimization applied to automatic lens design

Science.gov (United States)

Qin, Hua

2011-06-01

This paper describes a novel application of Particle Swarm Optimization (PSO) technique to lens design. A mathematical model is constructed, and merit functions in an optical system are employed as fitness functions, which combined radiuses of curvature, thicknesses among lens surfaces and refractive indices regarding an optical system. By using this function, the aberration correction is carried out. A design example using PSO is given. Results show that PSO as optical design tools is practical and powerful, and this method is no longer dependent on the lens initial structure and can arbitrarily create search ranges of structural parameters of a lens system, which is an important step towards automatic design with artificial intelligence.

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

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.

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.

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...

Engineering Technology Of Fish Farming Floating Nets Cages On Polka Dot Grouper (Cromileptes Altivelis) Used Artificial Feed Enriched Phytase Enzyme

Science.gov (United States)

Samidjan, Istiyanto; Rachmawati, Diana

2018-02-01

One solution is to utilize engineering technology cultivation floating cage net polka dot grouper (ducker grouper), which is given artificial feed enriched with phytase enzymes. The objectives of this study was to examine the use of technology engineering floating net on ducker grouper on artificial feed that is enriched with different dose phytase enzymes to accelerate growth and survival. The research method used ducker grouper fish size 15,5 ± 0,5 cm in the net cages unit (1 m x 1 m x 1 m), 250 fish per cage, using 12 cages. Each net-cages was made of polyethylens netting, mesh size 12.5 mm. with complete randomized design (CRD) 4 treatment and 3 replication were feed Artificial enriched of phytase enzyme with the doses of A (0 FTU · kg-1 diet), B (200 FTU · kg-1 diet), C (500 FTU · kg-1 diet), and D (800 FTU · kg-1 diet) phytase enzyme. Feed was given 2 times a day in the morning and afternoon with 5% biomass per day. Data includes the growth of absolute weight polka dot grouper, FCR, and survival rate analyzed variety and Test Tukey.The result of the research showed that the difference of artificial feeding enriched phytase enzyme significantly (P <0,05) to growth, food conversion ratio (FCR), survival rete of polka dot grouper. The best treatment at C (500 mg / kg of feed) increase growth of absolute weight of 128.75 g, 1.75 (FCR), and a survival rate of 93.5%.

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.

Comparison result of inversion of gravity data of a fault by particle swarm optimization and Levenberg-Marquardt methods.

Science.gov (United States)

Toushmalani, Reza

2013-01-01

The purpose of this study was to compare the performance of two methods for gravity inversion of a fault. First method [Particle swarm optimization (PSO)] is a heuristic global optimization method and also an optimization algorithm, which is based on swarm intelligence. It comes from the research on the bird and fish flock movement behavior. Second method [The Levenberg-Marquardt algorithm (LM)] is an approximation to the Newton method used also for training ANNs. In this paper first we discussed the gravity field of a fault, then describes the algorithms of PSO and LM And presents application of Levenberg-Marquardt algorithm, and a particle swarm algorithm in solving inverse problem of a fault. Most importantly the parameters for the algorithms are given for the individual tests. Inverse solution reveals that fault model parameters are agree quite well with the known results. A more agreement has been found between the predicted model anomaly and the observed gravity anomaly in PSO method rather than LM method.

Application of the artificial bee colony algorithm for solving the set covering problem.

Science.gov (United States)

Crawford, Broderick; Soto, Ricardo; Cuesta, Rodrigo; Paredes, Fernando

2014-01-01

The set covering problem is a formal model for many practical optimization problems. In the set covering problem the goal is to choose a subset of the columns of minimal cost that covers every row. Here, we present a novel application of the artificial bee colony algorithm to solve the non-unicost set covering problem. The artificial bee colony algorithm is a recent swarm metaheuristic technique based on the intelligent foraging behavior of honey bees. Experimental results show that our artificial bee colony algorithm is competitive in terms of solution quality with other recent metaheuristic approaches for the set covering problem.

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...

PID control for chaotic synchronization using particle swarm optimization

International Nuclear Information System (INIS)

Chang, W.-D.

2009-01-01

In this paper, we attempt to use the proportional-integral-derivative (PID) controller to achieve the chaos synchronization for delayed discrete chaotic systems. Three PID control gains can be optimally determined by means of using a novel optimization algorithm, called the particle swarm optimization (PSO). The algorithm is motivated from the organism behavior of fish schooling and bird flocking, and involves the social psychology principles in socio-cognition human agents and evolutionary computations. It has a good numerical convergence for solving optimization problem. To show the validity of the PSO-based PID control for chaos synchronization, several cases with different initial populations are considered and some simulation results are shown.

PID control for chaotic synchronization using particle swarm optimization

Energy Technology Data Exchange (ETDEWEB)

Chang, W.-D. [Department of Computer and Communication, Shu-Te University, Kaohsiung 824, Taiwan (China)], E-mail: wdchang@mail.stu.edu.tw

2009-01-30

In this paper, we attempt to use the proportional-integral-derivative (PID) controller to achieve the chaos synchronization for delayed discrete chaotic systems. Three PID control gains can be optimally determined by means of using a novel optimization algorithm, called the particle swarm optimization (PSO). The algorithm is motivated from the organism behavior of fish schooling and bird flocking, and involves the social psychology principles in socio-cognition human agents and evolutionary computations. It has a good numerical convergence for solving optimization problem. To show the validity of the PSO-based PID control for chaos synchronization, several cases with different initial populations are considered and some simulation results are shown.

A Swarm Optimization Algorithm for Multimodal Functions and Its Application in Multicircle Detection

Directory of Open Access Journals (Sweden)

Erik Cuevas

2013-01-01

Full Text Available In engineering problems due to physical and cost constraints, the best results, obtained by a global optimization algorithm, cannot be realized always. Under such conditions, if multiple solutions (local and global are known, the implementation can be quickly switched to another solution without much interrupting the design process. This paper presents a new swarm multimodal optimization algorithm named as the collective animal behavior (CAB. Animal groups, such as schools of fish, flocks of birds, swarms of locusts, and herds of wildebeest, exhibit a variety of behaviors including swarming about a food source, milling around a central location, or migrating over large distances in aligned groups. These collective behaviors are often advantageous to groups, allowing them to increase their harvesting efficiency to follow better migration routes, to improve their aerodynamic, and to avoid predation. In the proposed algorithm, searcher agents emulate a group of animals which interact with each other based on simple biological laws that are modeled as evolutionary operators. Numerical experiments are conducted to compare the proposed method with the state-of-the-art methods on benchmark functions. The proposed algorithm has been also applied to the engineering problem of multi-circle detection, achieving satisfactory results.

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...

Scouts behave as streakers in honeybee swarms

Science.gov (United States)

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

2013-08-01

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

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

Swarm intelligence algorithms for integrated optimization of piezoelectric actuator and sensor placement and feedback gains

International Nuclear Information System (INIS)

Dutta, Rajdeep; Ganguli, Ranjan; Mani, V

2011-01-01

Swarm intelligence algorithms are applied for optimal control of flexible smart structures bonded with piezoelectric actuators and sensors. The optimal locations of actuators/sensors and feedback gain are obtained by maximizing the energy dissipated by the feedback control system. We provide a mathematical proof that this system is uncontrollable if the actuators and sensors are placed at the nodal points of the mode shapes. The optimal locations of actuators/sensors and feedback gain represent a constrained non-linear optimization problem. This problem is converted to an unconstrained optimization problem by using penalty functions. Two swarm intelligence algorithms, namely, Artificial bee colony (ABC) and glowworm swarm optimization (GSO) algorithms, are considered to obtain the optimal solution. In earlier published research, a cantilever beam with one and two collocated actuator(s)/sensor(s) was considered and the numerical results were obtained by using genetic algorithm and gradient based optimization methods. We consider the same problem and present the results obtained by using the swarm intelligence algorithms ABC and GSO. An extension of this cantilever beam problem with five collocated actuators/sensors is considered and the numerical results obtained by using the ABC and GSO algorithms are presented. The effect of increasing the number of design variables (locations of actuators and sensors and gain) on the optimization process is investigated. It is shown that the ABC and GSO algorithms are robust and are good choices for the optimization of smart structures

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.

Optimal Grid Scheduling Using Improved Artificial Bee Colony Algorithm

OpenAIRE

T. Vigneswari; M. A. Maluk Mohamed

2015-01-01

Job Scheduling plays an important role for efficient utilization of grid resources available across different domains and geographical zones. Scheduling of jobs is challenging and NPcomplete. Evolutionary / Swarm Intelligence algorithms have been extensively used to address the NP problem in grid scheduling. Artificial Bee Colony (ABC) has been proposed for optimization problems based on foraging behaviour of bees. This work proposes a modified ABC algorithm, Cluster Hete...

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.

Fault Diagnosis of Plunger Pump in Truck Crane Based on Relevance Vector Machine with Particle Swarm Optimization Algorithm

Directory of Open Access Journals (Sweden)

Wenliao Du

2013-01-01

Full Text Available Promptly and accurately dealing with the equipment breakdown is very important in terms of enhancing reliability and decreasing downtime. A novel fault diagnosis method PSO-RVM based on relevance vector machines (RVM with particle swarm optimization (PSO algorithm for plunger pump in truck crane is proposed. The particle swarm optimization algorithm is utilized to determine the kernel width parameter of the kernel function in RVM, and the five two-class RVMs with binary tree architecture are trained to recognize the condition of mechanism. The proposed method is employed in the diagnosis of plunger pump in truck crane. The six states, including normal state, bearing inner race fault, bearing roller fault, plunger wear fault, thrust plate wear fault, and swash plate wear fault, are used to test the classification performance of the proposed PSO-RVM model, which compared with the classical models, such as back-propagation artificial neural network (BP-ANN, ant colony optimization artificial neural network (ANT-ANN, RVM, and support vectors, machines with particle swarm optimization (PSO-SVM, respectively. The experimental results show that the PSO-RVM is superior to the first three classical models, and has a comparative performance to the PSO-SVM, the corresponding diagnostic accuracy achieving as high as 99.17% and 99.58%, respectively. But the number of relevance vectors is far fewer than that of support vector, and the former is about 1/12–1/3 of the latter, which indicates that the proposed PSO-RVM model is more suitable for applications that require low complexity and real-time monitoring.

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....

Inseminación artificial de abejas reinas

OpenAIRE

Flores Serrano, J.M.; Padilla-Alvarez, F.; Ruiz, J.A.; Ruz, J.M.; Puerta Puerta, F.; Bustos Ruiz, M.; Campano Cabanes, Francisco

1998-01-01

The race commonly used by spanish beekeepers is Apis mellifera iberica. Up to date, any selection process has been carried out with this race, and a lot of characteristics in the colony can be improved. Artificial insemination is a technique used in order to control genetic origin, and open a way to control those tasks usefull for beekeepers, both productive (honey, pollen or royal jelly production...) o linked with behaviour (agresiveless, short tendency to swarming, natural resistance to di...

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...

A swarm intelligence framework for reconstructing gene networks: searching for biologically plausible architectures.

Science.gov (United States)

Kentzoglanakis, Kyriakos; Poole, Matthew

2012-01-01

In this paper, we investigate the problem of reverse engineering the topology of gene regulatory networks from temporal gene expression data. We adopt a computational intelligence approach comprising swarm intelligence techniques, namely particle swarm optimization (PSO) and ant colony optimization (ACO). In addition, the recurrent neural network (RNN) formalism is employed for modeling the dynamical behavior of gene regulatory systems. More specifically, ACO is used for searching the discrete space of network architectures and PSO for searching the corresponding continuous space of RNN model parameters. We propose a novel solution construction process in the context of ACO for generating biologically plausible candidate architectures. The objective is to concentrate the search effort into areas of the structure space that contain architectures which are feasible in terms of their topological resemblance to real-world networks. The proposed framework is initially applied to the reconstruction of a small artificial network that has previously been studied in the context of gene network reverse engineering. Subsequently, we consider an artificial data set with added noise for reconstructing a subnetwork of the genetic interaction network of S. cerevisiae (yeast). Finally, the framework is applied to a real-world data set for reverse engineering the SOS response system of the bacterium Escherichia coli. Results demonstrate the relative advantage of utilizing problem-specific knowledge regarding biologically plausible structural properties of gene networks over conducting a problem-agnostic search in the vast space of network architectures.

Man-made flows from a fish's perspective: autonomous classification of turbulent fishway flows with field data collected using an artificial lateral line.

Science.gov (United States)

Tuhtan, Jeffrey A; Fuentes-Perez, Juan Francisco; Toming, Gert; Schneider, Matthias; Schwarzenberger, Richard; Schletterer, Martin; Kruusmaa, Maarja

2018-05-25

The lateral line system provides fish with advanced mechanoreception over a wide range of flow conditions. Inspired by the abilities of their biological counterparts, artificial lateral lines have been developed and tested exclusively under laboratory settings. Motivated by the lack of flow measurements taken in the field which consider fluid-body interactions, we built a fish-shaped lateral line probe. The device is outfitted with 11 high-speed (2.5 kHz) time-synchronized pressure transducers, and designed to capture and classify flows in fish passage structures. A total of 252 field measurements, each with a sample size of 132 000 discrete sensor readings were recorded in the slots and across the pools of vertical slot fishways. These data were used to estimate the time-averaged flow velocity (R 2   =  0.952), which represents the most common metric to assess fishway flows. The significant contribution of this work is the creation and application of hydrodynamic signatures generated by the spatial distribution of pressure fluctuations on the fish-shaped body. The signatures are based on the collection of the pressure fluctuations' probability distributions, and it is shown that they can be used to automatically classify distinct flow regions within the pools of three different vertical slot fishways. For the first time, field data from operational fishway measurements are sampled and classified using an artificial lateral line, providing a completely new source of bioinspired flow information.

Collective motion of a class of social foraging swarms

International Nuclear Information System (INIS)

Liu Bo; Chu Tianguang; Wang Long; Wang Zhanfeng

2008-01-01

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

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

OpenAIRE

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

2002-01-01

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

The Fate of Colloidal Swarms in Fractures

Science.gov (United States)

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

2009-12-01

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

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)

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

Determination of trace elements in tailpipe fish produced in artificial farms and from white and blue nile

International Nuclear Information System (INIS)

Ahmed, Zeinb Khalil Elsaim

2017-01-01

In this study, an analytical protocol of x-ray fluorescence spectroscopy was used to determine the concentration of 13 trace elements, potassium (K), antimony (Sb), iron (Fe), copper (Cu), zinc (Zn), lead (Pb), bromine (Br), rubidium (Rb), strontium (Sr), mercury (Hg), chromium (Cu), manjense (Mn), and calcium (Ca), in tilapa fish. A total of 70 samples covering 35 fish samples collected from different states includes Eldamazine for blue nile samples and the Mawrada market for the white nile samples and 5 artificial fish farms samples were collected from Om badda in Omdurman and Bahry state for three farms Alsamraband Aldorshab and from Alshagra state in Khartoum, during may to June 2016. The trace elements detected in all samples, and the concentration in part million (ppm). The concentrations of trace elements followed the sequence of, K, Ca, Fe, Zn, Cu, Sr, Rb, Pb, but Cr, Hg and Ni were observed in one fish fram (farm A). The analysis included two tissues (flesh and gills), because most people in Sudan consume the flesh and gills, specially in the small fishes, consider as good indicators for the trace elements, and toxic compounds in general. The analysis indicated that the white nile fishes have higher l eves of most of the trace elements compared to the blue nile, e.g. Fe (560±186) in the white nile, whereas in the blue nile, (188±63). On the other hand , the artificial tilapia farms showed significant variations in the trace elements concentrations. The analysis revealed that a higher concentrations of most of the trace elements in gill tissues than flesh, e.g. Fe (1673±1453) in the flesh, and (9768±1175) in the gills. These results indicated that the gill accumulated higher levels of heavy metals than other organs, because they acted as a depot tissue. In addition, the post hoc test was performed following (Dunnett tests), using the blue nile group as a control group, since it has the lowest trace elements concentrations, among the river nile fishes in

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

Complex emergent dynamics of anisotropic swarms: Convergence vs oscillation

International Nuclear Information System (INIS)

Chu Tianguang; Wang Long; Chen Tongwen; Mu Shumei

2006-01-01

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

Improving Vector Evaluated Particle Swarm Optimisation by Incorporating Nondominated Solutions

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.

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.

Artificial intelligence and synthetic biology: A tri-temporal contribution.

Science.gov (United States)

Bianchini, Francesco

2016-10-01

Artificial intelligence can make numerous contributions to synthetic biology. I would like to suggest three that are related to the past, present and future of artificial intelligence. From the past, works in biology and artificial systems by Turing and von Neumann prove highly interesting to explore within the new framework of synthetic biology, especially with regard to the notions of self-modification and self-replication and their links to emergence and the bottom-up approach. The current epistemological inquiry into emergence and research on swarm intelligence, superorganisms and biologically inspired cognitive architecture may lead to new achievements on the possibilities of synthetic biology in explaining cognitive processes. Finally, the present-day discussion on the future of artificial intelligence and the rise of superintelligence may point to some research trends for the future of synthetic biology and help to better define the boundary of notions such as "life", "cognition", "artificial" and "natural", as well as their interconnections in theoretical synthetic biology. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

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.

Particle Swarm Optimization approach to defect detection in armour ceramics.

Science.gov (United States)

Kesharaju, Manasa; Nagarajah, Romesh

2017-03-01

In this research, various extracted features were used in the development of an automated ultrasonic sensor based inspection system that enables defect classification in each ceramic component prior to despatch to the field. Classification is an important task and large number of irrelevant, redundant features commonly introduced to a dataset reduces the classifiers performance. Feature selection aims to reduce the dimensionality of the dataset while improving the performance of a classification system. In the context of a multi-criteria optimization problem (i.e. to minimize classification error rate and reduce number of features) such as one discussed in this research, the literature suggests that evolutionary algorithms offer good results. Besides, it is noted that Particle Swarm Optimization (PSO) has not been explored especially in the field of classification of high frequency ultrasonic signals. Hence, a binary coded Particle Swarm Optimization (BPSO) technique is investigated in the implementation of feature subset selection and to optimize the classification error rate. In the proposed method, the population data is used as input to an Artificial Neural Network (ANN) based classification system to obtain the error rate, as ANN serves as an evaluator of PSO fitness function. Copyright © 2016. Published by Elsevier B.V.

Swarm controlled emergence for ant clustering

DEFF Research Database (Denmark)

Scheidler, Alexander; Merkle, Daniel; Middendorf, Martin

2013-01-01

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

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.

Hybrid chaotic ant swarm optimization

International Nuclear Information System (INIS)

Li Yuying; Wen Qiaoyan; Li Lixiang; Peng Haipeng

2009-01-01

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

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

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.

Artificial wetlands - yes or no?

Czech Academy of Sciences Publication Activity Database

Horák, Václav; Lusk, Stanislav; Halačka, Karel; Lusková, Věra

2004-01-01

Roč. 4, č. 2 (2004), s. 119-127 ISSN 1642-3593. [International Symposium on the Ecology of Fluvial Fishes /9./. Lodz, 23.06.2003-26.06.2003] R&D Projects: GA AV ČR IBS6093007; GA AV ČR KSK6005114 Institutional research plan: CEZ:AV0Z6093917 Keywords : floodplain * artificial wetlands * fish communities Subject RIV: EH - Ecology, Behaviour

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.

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.

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.

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

International Nuclear Information System (INIS)

Santos Coelho, Leandro dos; Mariani, Viviana Cocco

2008-01-01

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

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

Energy Technology Data Exchange (ETDEWEB)

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

2008-11-15

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

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

Energy Technology Data Exchange (ETDEWEB)

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

2008-11-15

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

Assessing Human Judgment of Computationally Generated Swarming Behavior

Directory of Open Access Journals (Sweden)

John Harvey

2018-02-01

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

A REVIEW OF SWARMING UNMANNED AERIAL VEHICLES

Directory of Open Access Journals (Sweden)

CORNEA Mihai

2016-11-01

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

Impacts of Artificial Reefs on Surrounding Ecosystems

Science.gov (United States)

Manoukian, Sarine

Artificial reefs are becoming a popular biological and management component in shallow water environments characterized by soft seabed, representing both important marine habitats and tools to manage coastal fisheries and resources. An artificial reef in the marine environment acts as an open system with exchange of material and energy, altering the physical and biological characteristics of the surrounding area. Reef stability will depend on the balance of scour, settlement, and burial resulting from ocean conditions over time. Because of the unstable nature of sediments, they require a detailed and systematic investigation. Acoustic systems like high-frequency multibeam sonar are efficient tools in monitoring the environmental evolution around artificial reefs, whereas water turbidity can limit visual dive and ROV inspections. A high-frequency multibeam echo sounder offers the potential of detecting fine-scale distribution of reef units, providing an unprecedented level of resolution, coverage, and spatial definition. How do artificial reefs change over time in relation to the coastal processes? How accurately does multibeam technology map different typologies of artificial modules of known size and shape? How do artificial reefs affect fish school behavior? What are the limitations of multibeam technology for investigating fish school distribution as well as spatial and temporal changes? This study addresses the above questions and presents results of a new approach for artificial reef seafloor mapping over time, based upon an integrated analysis of multibeam swath bathymetry data and geoscientific information (backscatter data analysis, SCUBA observations, physical oceanographic data, and previous findings on the geology and sedimentation processes, integrated with unpublished data) from Senigallia artificial reef, northwestern Adriatic Sea (Italy) and St. Petersburg Beach Reef, west-central Florida continental shelf. A new approach for observation of fish

Multiobjective RFID Network Optimization Using Multiobjective Evolutionary and Swarm Intelligence Approaches

Directory of Open Access Journals (Sweden)

Hanning Chen

2014-01-01

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

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 ...

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

Directory of Open Access Journals (Sweden)

Yudong Zhang

2015-01-01

Full Text Available Particle swarm optimization (PSO is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used optimization techniques. This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO, population topology (as fully connected, von Neumann, ring, star, random, etc., hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic search, and biogeography-based optimization, extensions (to multiobjective, constrained, discrete, and binary optimization, theoretical analysis (parameter selection and tuning, and convergence analysis, and parallel implementation (in multicore, multiprocessor, GPU, and cloud computing forms. On the other hand, we offered a survey on applications of PSO to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology. It is hoped that this survey would be beneficial for the researchers studying PSO algorithms.

A Simple and Efficient Artificial Bee Colony Algorithm

Directory of Open Access Journals (Sweden)

Yunfeng Xu

2013-01-01

Full Text Available Artificial bee colony (ABC is a new population-based stochastic algorithm which has shown good search abilities on many optimization problems. However, the original ABC shows slow convergence speed during the search process. In order to enhance the performance of ABC, this paper proposes a new artificial bee colony (NABC algorithm, which modifies the search pattern of both employed and onlooker bees. A solution pool is constructed by storing some best solutions of the current swarm. New candidate solutions are generated by searching the neighborhood of solutions randomly chosen from the solution pool. Experiments are conducted on a set of twelve benchmark functions. Simulation results show that our approach is significantly better or at least comparable to the original ABC and seven other stochastic algorithms.

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.

Cosmological parameter estimation using Particle Swarm Optimization

Science.gov (United States)

Prasad, J.; Souradeep, T.

2014-03-01

Constraining parameters of a theoretical model from observational data is an important exercise in cosmology. There are many theoretically motivated models, which demand greater number of cosmological parameters than the standard model of cosmology uses, and make the problem of parameter estimation challenging. It is a common practice to employ Bayesian formalism for parameter estimation for which, in general, likelihood surface is probed. For the standard cosmological model with six parameters, likelihood surface is quite smooth and does not have local maxima, and sampling based methods like Markov Chain Monte Carlo (MCMC) method are quite successful. However, when there are a large number of parameters or the likelihood surface is not smooth, other methods may be more effective. In this paper, we have demonstrated application of another method inspired from artificial intelligence, called Particle Swarm Optimization (PSO) for estimating cosmological parameters from Cosmic Microwave Background (CMB) data taken from the WMAP satellite.

Cosmological parameter estimation using Particle Swarm Optimization

International Nuclear Information System (INIS)

Prasad, J; Souradeep, T

2014-01-01

Constraining parameters of a theoretical model from observational data is an important exercise in cosmology. There are many theoretically motivated models, which demand greater number of cosmological parameters than the standard model of cosmology uses, and make the problem of parameter estimation challenging. It is a common practice to employ Bayesian formalism for parameter estimation for which, in general, likelihood surface is probed. For the standard cosmological model with six parameters, likelihood surface is quite smooth and does not have local maxima, and sampling based methods like Markov Chain Monte Carlo (MCMC) method are quite successful. However, when there are a large number of parameters or the likelihood surface is not smooth, other methods may be more effective. In this paper, we have demonstrated application of another method inspired from artificial intelligence, called Particle Swarm Optimization (PSO) for estimating cosmological parameters from Cosmic Microwave Background (CMB) data taken from the WMAP satellite

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.

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

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)

Diagnostics of Nuclear Reactor Accidents Based on Particle Swarm Optimization Trained Neural Networks

International Nuclear Information System (INIS)

Abdel-Aal, M.M.Z.

2004-01-01

Automation in large, complex systems such as chemical plants, electrical power generation, aerospace and nuclear plants has been steadily increasing in the recent past. automated diagnosis and control forms a necessary part of these systems,this contains thousands of alarms processing in every component, subsystem and system. so the accurate and speed of diagnosis of faults is an important factors in operation and maintaining their health and continued operation and in reducing of repair and recovery time. using of artificial intelligence facilitates the alarm classifications and faults diagnosis to control any abnormal events during the operation cycle of the plant. thesis work uses the artificial neural network as a powerful classification tool. the work basically is has two components, the first is to effectively train the neural network using particle swarm optimization, which non-derivative based technique. to achieve proper training of the neural network to fault classification problem and comparing this technique to already existing techniques

Solving Unconstrained Global Optimization Problems via Hybrid Swarm Intelligence Approaches

Directory of Open Access Journals (Sweden)

Jui-Yu Wu

2013-01-01

Full Text Available Stochastic global optimization (SGO algorithms such as the particle swarm optimization (PSO approach have become popular for solving unconstrained global optimization (UGO problems. The PSO approach, which belongs to the swarm intelligence domain, does not require gradient information, enabling it to overcome this limitation of traditional nonlinear programming methods. Unfortunately, PSO algorithm implementation and performance depend on several parameters, such as cognitive parameter, social parameter, and constriction coefficient. These parameters are tuned by using trial and error. To reduce the parametrization of a PSO method, this work presents two efficient hybrid SGO approaches, namely, a real-coded genetic algorithm-based PSO (RGA-PSO method and an artificial immune algorithm-based PSO (AIA-PSO method. The specific parameters of the internal PSO algorithm are optimized using the external RGA and AIA approaches, and then the internal PSO algorithm is applied to solve UGO problems. The performances of the proposed RGA-PSO and AIA-PSO algorithms are then evaluated using a set of benchmark UGO problems. Numerical results indicate that, besides their ability to converge to a global minimum for each test UGO problem, the proposed RGA-PSO and AIA-PSO algorithms outperform many hybrid SGO algorithms. Thus, the RGA-PSO and AIA-PSO approaches can be considered alternative SGO approaches for solving standard-dimensional UGO problems.

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...

[Fishery resource protection by artificial propagation in hydroelectric development: Lixianjiang River drainage in Yunnan as an example].

Science.gov (United States)

Yang, Yong-Hong; Yang, Jun-Xing; Pan, Xiao-Fu; Zhou, Wei; Yang, Mei-Lin

2011-04-01

Hydroelectric developments can result in a number of negative environmental consequences. Conservation aquaculture is a branch of science derived from conservation and population recovery studies on endangered fishes. Here we discuss the impacts on fishes caused by hydropower projects in Lixianjiang, and evaluate effects and problems on the propagation of Parazacco spilurus, Hemibagrus pluriradiatus, Neolissochilus benasi and Semilabeo obscurus. A successful propagation project includes foraging ecology in fields, pond cultivation, juvenile fish raising, prevention and curing on fish disease, genetic management, artificial releasing and population monitoring. Artificial propagation is the practicable act on genetic intercommunication, preventing population deterioration for fishes in upper and lower reaches of the dam. For long-term planning, fish stocks are not suitable for many kind of fishes, but can prevent fishes from going extinct in the wild. Basic data collection on fish ecology, parent fish hunting, prevention on fish disease are the most important factors on artificial propagation. Strengthening the genetic management of stock population for keeping a higher genetic diversity can increase the success of stock enhancement. The works on Lixianjiang provide a new model for river fish protection. To make sure the complicated project works well, project plans, commission contracts, base line monitoring and techniques on artificial reproduction must be considered early. Last, fishery conservation should be considered alongside location development.

Light-Controlled Swarming and Assembly of Colloidal Particles

Directory of Open Access Journals (Sweden)

Jianhua Zhang

2018-02-01

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

A Clustering Approach Using Cooperative Artificial Bee Colony Algorithm

Directory of Open Access Journals (Sweden)

Wenping Zou

2010-01-01

Full Text Available Artificial Bee Colony (ABC is one of the most recently introduced algorithms based on the intelligent foraging behavior of a honey bee swarm. This paper presents an extended ABC algorithm, namely, the Cooperative Article Bee Colony (CABC, which significantly improves the original ABC in solving complex optimization problems. Clustering is a popular data analysis and data mining technique; therefore, the CABC could be used for solving clustering problems. In this work, first the CABC algorithm is used for optimizing six widely used benchmark functions and the comparative results produced by ABC, Particle Swarm Optimization (PSO, and its cooperative version (CPSO are studied. Second, the CABC algorithm is used for data clustering on several benchmark data sets. The performance of CABC algorithm is compared with PSO, CPSO, and ABC algorithms on clustering problems. The simulation results show that the proposed CABC outperforms the other three algorithms in terms of accuracy, robustness, and convergence speed.

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.

Artificial bee colony in neuro - Symbolic integration

Science.gov (United States)

Kasihmuddin, Mohd Shareduwan Mohd; Sathasivam, Saratha; Mansor, Mohd. Asyraf

2017-08-01

Swarm intelligence is a research area that models the population of the swarm based on natural computation. Artificial bee colony (ABC) algorithm is a swarm based metaheuristic algorithm introduced by Karaboga to optimize numerical problem. Pattern-SAT is a pattern reconstruction paradigm that utilized 2SAT logical rule in representing the behavior of the desired pattern. The information of the desired pattern in terms of 2SAT logic is embedded to Hopfield neural network (HNN-P2SAT) and the desired pattern is reconstructed during the retrieval phase. Since the performance of HNN-P2SAT in Pattern-SAT deteriorates when the number of 2SAT clause increased, newly improved ABC is used to reduce the computation burden during the learning phase of HNN-P2SAT (HNN-P2SATABC). The aim of this study is to investigate the performance of Pattern-SAT produced by ABC incorporated with HNN-P2SAT and compare it with conventional standalone HNN. The comparison is examined by using Microsoft Visual Basic C++ 2013 software. The detailed comparison in doing Pattern-SAT is discussed based on global Pattern-SAT, ratio of activated clauses and computation time. The result obtained from computer simulation indicates the beneficial features of HNN-P2SATABC in doing Pattern-SAT. This finding is expected to result in a significant implication on the choice of searching method used to do Pattern-SAT.

Performance comparison of genetic algorithms and particle swarm optimization for model integer programming bus timetabling problem

Science.gov (United States)

Wihartiko, F. D.; Wijayanti, H.; Virgantari, F.

2018-03-01

Genetic Algorithm (GA) is a common algorithm used to solve optimization problems with artificial intelligence approach. Similarly, the Particle Swarm Optimization (PSO) algorithm. Both algorithms have different advantages and disadvantages when applied to the case of optimization of the Model Integer Programming for Bus Timetabling Problem (MIPBTP), where in the case of MIPBTP will be found the optimal number of trips confronted with various constraints. The comparison results show that the PSO algorithm is superior in terms of complexity, accuracy, iteration and program simplicity in finding the optimal solution.

Hierarchical Swarm Model: A New Approach to Optimization

Directory of Open Access Journals (Sweden)

Hanning Chen

2010-01-01

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

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)

High Performance Artificial Muscles Using Nanofiber and Hybrid Yarns

Science.gov (United States)

2015-07-14

2. Above advance led to “Artificial Muscles From Fishing Line and Sewing Thread”, which was patent filed and then published in Science in 2014...consuming significant energy. The publication of Artificial Muscles From Fishing Line and Sewing Thread (Science, 2014) generated TV, radio, and other...gn f cant energy. The pub cat on of “Art f c a Musc es From F sh ng L ne and Sew ng Thread” (Sc ence, 2014) generated TV, rad o, and other wor d-w de

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....

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

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.

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.

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.

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.

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)

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...

Colias: An Autonomous Micro Robot for Swarm Robotic Applications

Directory of Open Access Journals (Sweden)

Farshad Arvin

2014-07-01

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

On the application of artificial bee colony (ABC algorithm for optimization of well placements in fractured reservoirs; efficiency comparison with the particle swarm optimization (PSO methodology

Directory of Open Access Journals (Sweden)

Behzad Nozohour-leilabady

2016-03-01

Full Text Available The application of a recent optimization technique, the artificial bee colony (ABC, was investigated in the context of finding the optimal well locations. The ABC performance was compared with the corresponding results from the particle swarm optimization (PSO algorithm, under essentially similar conditions. Treatment of out-of-boundary solution vectors was accomplished via the Periodic boundary condition (PBC, which presumably accelerates convergence towards the global optimum. Stochastic searches were initiated from several random staring points, to minimize starting-point dependency in the established results. The optimizations were aimed at maximizing the Net Present Value (NPV objective function over the considered oilfield production durations. To deal with the issue of reservoir heterogeneity, random permeability was applied via normal/uniform distribution functions. In addition, the issue of increased number of optimization parameters was address, by considering scenarios with multiple injector and producer wells, and cases with deviated wells in a real reservoir model. The typical results prove ABC to excel PSO (in the cases studied after relatively short optimization cycles, indicating the great premise of ABC methodology to be used for well-optimization purposes.

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.

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)

Artificial Neural Networks and the Mass Appraisal of Real Estate

Directory of Open Access Journals (Sweden)

Gang Zhou

2018-03-01

Full Text Available With the rapid development of computer, artificial intelligence and big data technology, artificial neural networks have become one of the most powerful machine learning algorithms. In the practice, most of the applications of artificial neural networks use back propagation neural network and its variation. Besides the back propagation neural network, various neural networks have been developing in order to improve the performance of standard models. Though neural networks are well known method in the research of real estate, there is enormous space for future research in order to enhance their function. Some scholars combine genetic algorithm, geospatial information, support vector machine model, particle swarm optimization with artificial neural networks to appraise the real estate, which is helpful for the existing appraisal technology. The mass appraisal of real estate in this paper includes the real estate valuation in the transaction and the tax base valuation in the real estate holding. In this study we focus on the theoretical development of artificial neural networks and mass appraisal of real estate, artificial neural networks model evolution and algorithm improvement, artificial neural networks practice and application, and review the existing literature about artificial neural networks and mass appraisal of real estate. Finally, we provide some suggestions for the mass appraisal of China's real estate.

[Species composition and distribution patterns of ichthyoplankton within and outside artificial reefs in Qingshan Bay, Qingdao, China].

Science.gov (United States)

Guo, Shu Xin; Gao, Dong Kui; Zhang, Xiu Mei; Li, Wen Tao; Zhang, Pei Dong

2017-06-18

To assess the fish attraction and shelter effects of the artificial reefs in Qingshan Bay of Qingdao, the species composition and distribution patterns of ichthyoplankton in artificial reefs were investigated using vertical and horizontal tows in 2014 and 2015. In total, 7306 fish eggs and 52 fish larvae, belonging to 4 orders, 9 families, 11 genera and 12 species, were collected during 7 cruises in spring, summer and autumn of 2014. In 2015, 10373 eggs and 159 fish larvae, belonging to 6 orders, 11 families, 14 genera and 15 species, were collected in the same period as in 2014. Perciformes were the majority for both fish eggs and larvae collected during the two surveys, followed by Pleuronectiformes. Among fish eggs, Sillago japonica and Cynoglossus joyneri were the most dominant species. Among fish larvae, S. japonica exhibited the highest dominance but was not the dominant species. The high dominant species in both fish eggs and larvae appeared alternately in diffe-rent seasons. The Margalef richness index (R), Shannon diversity index (H) and Pielou evenness index (J) of ichthyoplankton community were low during the spring, summer, and autumn, showing an instable community structure of ichthyoplankton. The average trophic level of adult fish corresponding to the ichthyoplankton collected in the two years were 3.71 and 3.78, respectively, and both belonged to low carnivorous fish of third trophic level. All the species were either warm-tempe-rate species or warm-water species, which was coincident with the warm-temperate zone characteristic. Comprehensive analysis showed that the biodiversity of the ichthyoplankton community within the artificial reef areas was higher than that outside the artificial reef areas, which might be linked to flow velocity, flow field characteristics, or spatial heterogeneity of artificial reef areas.

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.

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...

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...

Artificial Reefs as Surrogate Habitats for Red Snapper in the Northwestern Gulf of Mexico: A Fishery-Independent Comparison of Artificial and Natural Habitats

Science.gov (United States)

Streich, M.; Wetz, J. J.; Ajemian, M. J.; Stunz, G. W.

2016-02-01

The goal of our study was to evaluate the relative abundance, size and age structure of Red Snapper among three different habitat types (standing oil and gas platforms, artificial reefs [rigs-to-reefs], and natural banks) in the northwestern Gulf of Mexico. From May 2013 - January 2015, we conducted 140 vertical line sets and captured 1538 Red Snapper ranging in size from 251 to 855 mm TL. Ages determined for 801 of these fish ranged from 2-30 years. No differences were detected in Red Snapper CPUE among the three habitats. However, a comparison of TL and TW distributions suggested that natural banks supported a greater proportion of larger fish than artificial reefs or standing platforms (K-S test, pdata will help elucidate the role artificial structures play in maintaining the Red Snapper population.

Augmented fish health monitoring

International Nuclear Information System (INIS)

Michak, P.; Rogers, R.; Amos, K.

1991-05-01

The Bonneville Power Administration (BPA) initiated the Augmented Fish Health Monitoring project in 1986. This project was a five year interagency project involving fish rearing agencies in the Columbia Basin. Historically, all agencies involved with fish health in the Columbia Basin were conducting various levels of fish health monitoring, pathogen screening and collection. The goals of this project were; to identify, develop and implement a standardized level of fish health methodologies, develop a common data collection and reporting format in the area of artificial production, evaluate and monitor water quality, improve communications between agencies and provide annual evaluation of fish health information for production of healthier smolts. This completion report will contain a project evaluation, review of the goals of the project, evaluation of the specific fish health analyses, an overview of highlights of the project and concluding remarks. 8 refs., 1 fig., 4 tabs

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.

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.

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.

A modified scout bee for artificial bee colony algorithm and its performance on optimization problems

Directory of Open Access Journals (Sweden)

Syahid Anuar

2016-10-01

Full Text Available The artificial bee colony (ABC is one of the swarm intelligence algorithms used to solve optimization problems which is inspired by the foraging behaviour of the honey bees. In this paper, artificial bee colony with the rate of change technique which models the behaviour of scout bee to improve the performance of the standard ABC in terms of exploration is introduced. The technique is called artificial bee colony rate of change (ABC-ROC because the scout bee process depends on the rate of change on the performance graph, replace the parameter limit. The performance of ABC-ROC is analysed on a set of benchmark problems and also on the effect of the parameter colony size. Furthermore, the performance of ABC-ROC is compared with the state of the art algorithms.

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)

Proxy measures of fitness suggest coastal fish farms can act as population sources and not ecological traps for wild gadoid fish.

Directory of Open Access Journals (Sweden)

Tim Dempster

Full Text Available BACKGROUND: Ecological traps form when artificial structures are added to natural habitats and induce mismatches between habitat preferences and fitness consequences. Their existence in terrestrial systems has been documented, yet little evidence suggests they occur in marine environments. Coastal fish farms are widespread artificial structures in coastal ecosystems and are highly attractive to wild fish. METHODOLOGY/PRINCIPAL FINDINGS: To investigate if coastal salmon farms act as ecological traps for wild Atlantic cod (Gadus morhua and saithe (Pollachius virens, we compared proxy measures of fitness between farm-associated fish and control fish caught distant from farms in nine locations throughout coastal Norway, the largest coastal fish farming industry in the world. Farms modified wild fish diets in both quality and quantity, thereby providing farm-associated wild fish with a strong trophic subsidy. This translated to greater somatic (saithe: 1.06-1.12 times; cod: 1.06-1.11 times and liver condition indices (saithe: 1.4-1.8 times; cod: 2.0-2.8 times than control fish caught distant from farms. Parasite loads of farm-associated wild fish were modified from control fish, with increased external and decreased internal parasites, however the strong effect of the trophic subsidy overrode any effects of altered loads upon condition. CONCLUSIONS AND SIGNIFICANCE: Proxy measures of fitness provided no evidence that salmon farms function as ecological traps for wild fish. We suggest fish farms may act as population sources for wild fish, provided they are protected from fishing while resident at farms to allow their increased condition to manifest as greater reproductive output.

Effect of citric acid on the acidification of artificial pepsin solution for metacercariae isolation from fish.

Science.gov (United States)

Kim, Min-Ki; Pyo, Kyoung-Ho; Hwang, Young-Sang; Chun, Hyang Sook; Park, Ki Hwan; Ko, Seong-Hee; Chai, Jong-Yil; Shin, Eun-Hee

2013-11-15

Artificial digestive solution based on pepsin is essential for collecting metacercariae from fish. To promote the enzymatic reactivity of pepsin, the pH of the solution has to be adjusted to pH 1.0-2.0. Hydrochloride (HCl) is usually used for this purpose, but the use of HCl raises safety concerns. The aim of this work was to address the usefulness of citric acid as an alternative for HCl for the acidification of pepsin solution, and to examine its potential to damage metacercariae during in vitro digestion as compared with HCl. Changes in pH after adding 1-9% of citric acid (m/v) to pepsin solution were compared to a 1% HCl (v/v) addition. Digestion of fish muscle was evaluated by measuring released protein concentrations by spectrophotometry. In addition, survival rates of metacercariae in pepsin solution were determined at different citric acid concentrations and were compared that of with 1% HCl. The present study shows that addition of citric acid reduced the pH of pepsin solutions to the required level. Addition of more than 5% of citric acid resulted in the effective digestion of fish muscle over 3h in vitro, and 5% citric acid was less lethal to metacercariae than 1% HCl in pepsin solution. Pepsin solution containing 5% citric acid had digestive capacity superior to pepsin solution containing 1% HCl after 3h incubation with released protein concentrations of 12.0 ng/ml for 5% citric acid and 9.6 ng/ml for 1% HCl. Accordingly, the present study suggests that the addition of 5% citric acid to pepsin solution is a good alternative to 1% HCl in infection studies because citric acid is a stable at room temperature and has a good safety profile. In addition, we suggest that the use of citric acid enables the preparation of commercial digestive solutions for the detection of microorganisms in fish and other vertebrate muscle tissue. Copyright © 2013 The Authors. Published by Elsevier B.V. All rights reserved.

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.

Spatial distribution of juvenile fish along an artificialized seascape, insights from common coastal species in the Northwestern Mediterranean Sea.

Science.gov (United States)

Mercader, Manon; Rider, Mary; Cheminée, Adrien; Pastor, Jérémy; Zawadzki, Audrey; Mercière, Alexandre; Crec'hriou, Romain; Verdoit-Jarraya, Marion; Lenfant, Philippe

2018-06-01

Along the littoral, a growing number of anthropogenic structures have caused substantial habitat destruction. Despite their detrimental impact, these constructions could play a role in the functioning of coastal ecosystems. The objective of this work was to assess the distribution of juvenile coastal fish along a seascape composed of various natural and artificial habitats in order to determine the potential role of coastal infrastructures as juvenile habitat. We surveyed juvenile populations on various infrastructures and natural sites along a 100 km shoreline of the French Mediterranean coast. Juvenile densities varied according to the level of artificialization of the sites. Densities were the highest on coastal defense structures, intermediate in natural sites and lowest in harbors. Focusing inside harbors revealed highly variable densities depending on the type of habitat, with densities on ripraps or jetties that were equivalent to those of natural sites. Our results underline the importance of anthropogenic structures as potential juvenile habitats, which is too often not considered in management plans. Copyright © 2018 Elsevier Ltd. All rights reserved.

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

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.

A comprehensive overview of the applications of artificial life.

Science.gov (United States)

Kim, Kyung-Joong; Cho, Sung-Bae

2006-01-01

We review the applications of artificial life (ALife), the creation of synthetic life on computers to study, simulate, and understand living systems. The definition and features of ALife are shown by application studies. ALife application fields treated include robot control, robot manufacturing, practical robots, computer graphics, natural phenomenon modeling, entertainment, games, music, economics, Internet, information processing, industrial design, simulation software, electronics, security, data mining, and telecommunications. In order to show the status of ALife application research, this review primarily features a survey of about 180 ALife application articles rather than a selected representation of a few articles. Evolutionary computation is the most popular method for designing such applications, but recently swarm intelligence, artificial immune network, and agent-based modeling have also produced results. Applications were initially restricted to the robotics and computer graphics, but presently, many different applications in engineering areas are of interest.

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

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

Simulation Study of Swarm Intelligence Based on Life Evolution Behavior

Directory of Open Access Journals (Sweden)

Yanmin Liu

2015-01-01

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

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.

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.

On the Idea of a New Artificial Intelligence Based Optimization Algorithm Inspired From the Nature of Vortex

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Utku Kose

2015-07-01

Full Text Available In this paper, the idea of a new artificial intelligence based optimization algorithm, which is inspired from the nature of vortex, has been provided briefly. As also a bio-inspired computation algorithm, the idea is generally focused on a typical vortex flow / behavior in nature and inspires from some dynamics that are occurred in the sense of vortex nature. Briefly, the algorithm is also a swarm-oriented evolutional problem solution approach; because it includes many methods related to elimination of weak swarm members and trying to improve the solution process by supporting the solution space via new swarm members. In order have better idea about success of the algorithm; it has been tested via some benchmark functions. At this point, the obtained results show that the algorithm can be an alternative to the literature in terms of single-objective optimizationsolution ways. Vortex Optimization Algorithm (VOA is the name suggestion by the authors; for this new idea of intelligent optimization approach.

Evaluating the Potential for Marine and Hydrokinetic Devices to Act as Artificial Reefs or Fish Aggregating Devices. Based on Analysis of Surrogates in Tropical, Subtropical, and Temperate U.S. West Coast and Hawaiian Coastal Waters

Energy Technology Data Exchange (ETDEWEB)

Kramer, Sharon H. [H. T. Harvey & Associates, Honolulu, HI (United States); Hamilton, Christine D. [H. T. Harvey & Associates, Honolulu, HI (United States); Spencer, Gregory C. [H. T. Harvey & Associates, Honolulu, HI (United States); Ogston, Heather O. [H. T. Harvey & Associates, Honolulu, HI (United States)

2015-05-12

Wave energy converters (WECs) and tidal energy converters (TECs) are only beginning to be deployed along the U.S. West Coast and in Hawai‘i, and a better understanding of their ecological effects on fish, particularly on special-status fish (e.g., threatened and endangered) is needed to facilitate project design and environmental permitting. The structures of WECs and TECs placed on to the seabed, such as anchors and foundations, may function as artificial reefs that attract reef-associated fishes, while the midwater and surface structures, such as mooring lines, buoys, and wave or tidal power devices, may function as fish aggregating devices (FADs), forming the nuclei for groups of fishes. Little is known about the potential for WECs and TECs to function as artificial reefs and FADs in coastal waters of the U.S. West Coast and Hawai‘i. We evaluated these potential ecological interactions by reviewing relevant information about fish associations with surrogate structures, such as artificial reefs, natural reefs, kelps, floating debris, oil and gas platforms, marine debris, anchored FADs deployed to enhance fishing opportunities, net-cages used for mariculture, and piers and docks. Based on our review, we postulate that the structures of WECs and TECs placed on or near the seabed in coastal waters of the U.S. West Coast and Hawai‘i likely will function as small-scale artificial reefs and attract potentially high densities of reef-associated fishes (including special-status rockfish species [Sebastes spp.] along the mainland), and that the midwater and surface structures of WECs placed in the tropical waters of Hawai‘i likely will function as de facto FADs with species assemblages varying by distance from shore and deployment depth. Along the U.S. West Coast, frequent associations with midwater and surface structures may be less likely: juvenile, semipelagic, kelp-associated rockfishes may occur at midwater and surface structures of WECs in coastal waters of

A Constructive Data Classification Version of the Particle Swarm Optimization Algorithm

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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.

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...

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).

A modified parallel artificial membrane permeability assay for evaluating the bioconcentration of highly hydrophobic chemicals in fish.

Science.gov (United States)

Kwon, Jung-Hwan; Escher, Beate I

2008-03-01

Low cost in vitro tools are needed at the screening stage of assessment of bioaccumulation potential of new and existing chemicals because the number of chemical substances that needs to be tested highly exceeds the capacity of in vivo bioconcentration tests. Thus, the parallel artificial membrane permeability assay (PAMPA) system was modified to predict passive uptake/ elimination rate in fish. To overcome the difficulties associated with low aqueous solubility and high membrane affinity of highly hydrophobic chemicals, we measured the rate of permeation from the donor poly(dimethylsiloxane)(PDMS) disk to the acceptor PDMS disk through aqueous and PDMS membrane boundary layers and term the modified PAMPA system "PDMS-PAMPA". Twenty chemicals were selected for validation of PDMS-PAMPA. The measured permeability is proportional to the passive elimination rate constant in fish and was used to predict the "minimum" in vivo elimination rate constant. The in vivo data were very close to predicted values except for a few polar chemicals and metabolically active chemicals, such as pyrene and benzo[a]pyrene. Thus, PDMS-PAMPA can be an appropriate in vitro system for nonmetabolizable chemicals. Combination with metabolic clearance rates using a battery of metabolic degradation assays would enhance the applicability for metabolizable chemicals.

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.

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)

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.

ARTIFICIAL BREEDING OF NERETVA SOFTMOUTH TROUT (Salmo obtusirostris Oxyrhincus Heckel, 1851

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Dževad Handžar

2015-06-01

Full Text Available The paper presents an artificial breeding of softmouth trout (Salmo obtusirostris oxyrhynchus, which is one of the endemic species of fish Neretva basin and which is on the Red List of the International Union for Conservation of Nature (IUCN as endangered species facing extinction. Production of fry softmouth trout intended for restocking is very complex and complicated process, and in this paper, which is based on long experience in artificial breeding softmouth trout, we want to contribute to the conservation and breeding of native salmonid fish and aquaculture development of BiH. The paper describes the production technology and production phase, diet and food as well as the application of preventive and therapeutic measures in production. Key word - softmouth trout, artificial breeding, preventive and therapeutic measures

Influence of artificially induced light pollution on the hormone system of two common fish species, perch and roach, in a rural habitat.

Science.gov (United States)

Brüning, Anika; Kloas, Werner; Preuer, Torsten; Hölker, Franz

2018-01-01

Almost all life on earth has adapted to natural cycles of light and dark by evolving circadian and circannual rhythms to synchronize behavioural and physiological processes with the environment. Artificial light at night (ALAN) is suspected to interfere with these rhythms. In this study we examined the influence of ALAN on nocturnal melatonin and sex steroid blood concentrations and mRNA expression of gonadotropins in the pituitary of European perch ( Perca fluviatilis ) and roach ( Rutilus rutilus ). In a rural experimental setting, fish were held in net cages in drainage channels experiencing either additional ALAN of ~15 lx at the water surface or natural light conditions at half-moon. No differences in melatonin concentrations between ALAN and natural conditions were detected. However, blood concentration of sex steroids (17β-estradiol; 11-ketotestosterone) as well as mRNA expression of gonadotropins (luteinizing hormone, follicle stimulating hormone) was reduced in both fish species. We conclude that ALAN can disturb biological rhythms in fish in urban waters. However, impacts on melatonin rhythm might have been blurred by individual differences, sampling methods and moonlight. The effect of ALAN on biomarkers of reproduction suggests a photo-labile period around the onset of gonadogenesis, including the experimental period (August). Light pollution therefore has a great potential to influence crucial life history traits with unpredictable outcome for fish population dynamics.

Heuristic Artificial Bee Colony Algorithm for Uncovering Community in Complex Networks

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Yuquan Guo

2017-01-01

Full Text Available Community structure is important for us to understand the functions and structure of the complex networks. In this paper, Heuristic Artificial Bee Colony (HABC algorithm based on swarm intelligence is proposed for uncovering community. The proposed HABC includes initialization, employed bee searching, onlooker searching, and scout bee searching. In initialization stage, the nectar sources with simple community structure are generated through network dynamic algorithm associated with complete subgraph. In employed bee searching and onlooker searching stages, the searching function is redefined to address the community problem. The efficiency of searching progress can be improved by a heuristic function which is an average agglomerate probability of two neighbor communities. Experiments are carried out on artificial and real world networks, and the results demonstrate that HABC will have better performance in terms of comparing with the state-of-the-art algorithms.

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

Science.gov (United States)

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

2015-01-07

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

Method to improve the survival of night-swarming mayflies near bridges in areas of distracting light pollution.

Science.gov (United States)

Egri, Ádám; Száz, Dénes; Farkas, Alexandra; Pereszlényi, Ádám; Horváth, Gábor; Kriska, György

2017-11-01

Numerous negative ecological effects of urban lighting have been identified during the last decades. In spite of the development of lighting technologies, the detrimental effect of this form of light pollution has not declined. Several insect species are affected including the night-swarming mayfly Ephoron virgo : when encountering bridges during their mass swarming, these mayflies often fall victim to artificial lighting. We show a simple method for the conservation of these mayflies exploiting their positive phototaxis. With downstream-facing light-emitting diode beacon lights above two tributaries of the river Danube, we managed to guide egg-laying females to the water and prevent them from perishing outside the river near urban lights. By means of measuring the mayfly outflow from the river as a function of time and the on/off state of the beacons, we showed that the number of mayflies exiting the river's area was practically zero when our beacons were operating. Tributaries could be the sources of mayfly recolonization in case of water quality degradation of large rivers. The protection of mayfly populations in small rivers and safeguarding their aggregation and oviposition sites is therefore important.

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

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.

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.

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

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.

Conservation of Native Fishes of the San Francisco Estuary: Considerations for Artificial Propagation of Chinook Salmon, Delta Smelt, and Green Sturgeon

Directory of Open Access Journals (Sweden)

Joshua A. Israel

2011-04-01

Full Text Available Many native fishes in the San Francisco Estuary and its watersheds have reached all-time low abundances. Some of these declining species (e.g., Chinook salmon Oncorhynchus tschawytscha have been under artificial propagation for decades. For others (e.g., delta smelt, Hypomesus transpacificus, and green sturgeon, Acipenser medirostris, this management option is just beginning to be discussed and implemented. Propagation strategies, in which organisms spend some portion of their lives in captivity, pose well-documented genetic and ecological threats to natural populations. Negative impacts of propagation have been documented for all Central Valley Chinook salmon runs, but limited efforts have been made to adapt hatchery operations to minimize the genetic and ecological threats caused by propagated fishes. A delta smelt propagation program is undergoing intensive design and review for operations and monitoring. However, if limiting factors facing this species in its estuarine habitat are not effectively addressed, captive propagation may not be a useful conservation approach, regardless of how carefully the propagation activity is designed or monitored. Scientifically defensible, ecologically based restoration programs that include monitoring and research aimed at quantifying natural population vital rates should be fully implemented before there is any attempt to supplement natural populations of delta smelt. Green sturgeon are also likely to face risks from artificial propagation if a large–scale program is implemented before this species’ limiting factors are better understood. In each of these cases, restoring habitats, and reducing loss from human actions, are likely to be the best strategy for rebuilding and supporting self–sustaining populations.

Lamp-lit bridges as dual light-traps for the night-swarming mayfly, Ephoron virgo: interaction of polarized and unpolarized light pollution.

Directory of Open Access Journals (Sweden)

Denes Szaz

Full Text Available Ecological photopollution created by artificial night lighting can alter animal behavior and lead to population declines and biodiversity loss. Polarized light pollution is a second type of photopollution that triggers water-seeking insects to ovisposit on smooth and dark man-made objects, because they simulate the polarization signatures of natural water bodies. We document a case study of the interaction of these two forms of photopollution by conducting observations and experiments near a lamp-lit bridge over the river Danube that attracts mass swarms of the mayfly Ephoron virgo away from the river to oviposit on the asphalt road of the bridge. Millions of mayflies swarmed near bridge-lights for two weeks. We found these swarms to be composed of 99% adult females performing their upstream compensatory flight and were attracted upward toward unpolarized bridge-lamp light, and away from the horizontally polarized light trail of the river. Imaging polarimetry confirmed that the asphalt surface of the bridge was strongly and horizontally polarized, providing a supernormal ovipositional cue to Ephoron virgo, while other parts of the bridge were poor polarizers of lamplight. Collectively, we confirm that Ephoron virgo is independently attracted to both unpolarized and polarized light sources, that both types of photopollution are being produced at the bridge, and that spatial patterns of swarming and oviposition are consistent with evolved behaviors being triggered maladaptively by these two types of light pollution. We suggest solutions to bridge and lighting design that should prevent or mitigate the impacts of such scenarios in the future. The detrimental impacts of such scenarios may extend beyond Ephoron virgo.

Lamp-lit bridges as dual light-traps for the night-swarming mayfly, Ephoron virgo: interaction of polarized and unpolarized light pollution.

Science.gov (United States)

Szaz, Denes; Horvath, Gabor; Barta, Andras; Robertson, Bruce A; Farkas, Alexandra; Egri, Adam; Tarjanyi, Nikolett; Racz, Gergely; Kriska, Gyorgy

2015-01-01

Ecological photopollution created by artificial night lighting can alter animal behavior and lead to population declines and biodiversity loss. Polarized light pollution is a second type of photopollution that triggers water-seeking insects to ovisposit on smooth and dark man-made objects, because they simulate the polarization signatures of natural water bodies. We document a case study of the interaction of these two forms of photopollution by conducting observations and experiments near a lamp-lit bridge over the river Danube that attracts mass swarms of the mayfly Ephoron virgo away from the river to oviposit on the asphalt road of the bridge. Millions of mayflies swarmed near bridge-lights for two weeks. We found these swarms to be composed of 99% adult females performing their upstream compensatory flight and were attracted upward toward unpolarized bridge-lamp light, and away from the horizontally polarized light trail of the river. Imaging polarimetry confirmed that the asphalt surface of the bridge was strongly and horizontally polarized, providing a supernormal ovipositional cue to Ephoron virgo, while other parts of the bridge were poor polarizers of lamplight. Collectively, we confirm that Ephoron virgo is independently attracted to both unpolarized and polarized light sources, that both types of photopollution are being produced at the bridge, and that spatial patterns of swarming and oviposition are consistent with evolved behaviors being triggered maladaptively by these two types of light pollution. We suggest solutions to bridge and lighting design that should prevent or mitigate the impacts of such scenarios in the future. The detrimental impacts of such scenarios may extend beyond Ephoron virgo.

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 ...

Quantitative Analysis of Dynamic Behaviours of Rural Areas at Provincial Level Using Public Data of Gross Domestic Product

DEFF Research Database (Denmark)

Chen, Yi; Zhang, Guangfeng; Li, Yiyang

2013-01-01

A spatial approach that incorporates three economic components and one environmental factor has been developed to evaluate the dynamic behaviours of the rural areas at a provincial level. An artificial fish swarm algorithm with variable population size (AFSAVP) is proposed for the spatial problem......, and the results have shown that the modelling method based on GDP data can assess the spatial dynamic behaviours and can be taken as an operational tool for the policy planners....

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

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.

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.

Spatial extent and dynamics of dam impacts on tropical island freshwater fish assemblages

Science.gov (United States)

Cooney, Patrick B.; Kwak, Thomas J.

2013-01-01

Habitat connectivity is vital to the persistence of migratory fishes. Native tropical island stream fish assemblages composed of diadromous species require intact corridors between ocean and riverine habitats. High dams block fish migration, but low-head artificial barriers are more widespread and are rarely assessed for impacts. Among all 46 drainages in Puerto Rico, we identified and surveyed 335 artificial barriers that hinder fish migration to 74.5% of the upstream habitat. We also surveyed occupancy of native diadromous fishes (Anguillidae, Eleotridae, Gobiidae, and Mugilidae) in 118 river reaches. Occupancy models demonstrated that barriers 2 meters (m) high restricted nongoby fish migration and extirpated those fish upstream of 4-m barriers. Gobies are adapted to climbing and are restricted by 12-m barriers and extirpated upstream of 32-m barriers. Our findings quantitatively illustrate the extensive impact of low-head structures on island stream fauna and provide guidance for natural resource management, habitat restoration, and water development strategies.

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 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.

Multiple cues produced by a robotic fish modulate aggressive behaviour in Siamese fighting fishes.

Science.gov (United States)

Romano, Donato; Benelli, Giovanni; Donati, Elisa; Remorini, Damiano; Canale, Angelo; Stefanini, Cesare

2017-07-05

The use of robotics to establish social interactions between animals and robots, represents an elegant and innovative method to investigate animal behaviour. However, robots are still underused to investigate high complex and flexible behaviours, such as aggression. Here, Betta splendens was tested as model system to shed light on the effect of a robotic fish eliciting aggression. We evaluated how multiple signal systems, including a light stimulus, affect aggressive responses in B. splendens. Furthermore, we conducted experiments to estimate if aggressive responses were triggered by the biomimetic shape of fish replica, or whether any intruder object was effective as well. Male fishes showed longer and higher aggressive displays as puzzled stimuli from the fish replica increased. When the fish replica emitted its full sequence of cues, the intensity of aggression exceeded even that produced by real fish opponents. Fish replica shape was necessary for conspecific opponent perception, evoking significant aggressive responses. Overall, this study highlights that the efficacy of an artificial opponent eliciting aggressive behaviour in fish can be boosted by exposure to multiple signals. Optimizing the cue combination delivered by the robotic fish replica may be helpful to predict escalating levels of aggression.

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.

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.

Swarm Optimization-Based Magnetometer Calibration for Personal Handheld Devices

Directory of Open Access Journals (Sweden)

Naser El-Sheimy

2012-09-01

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

Intelligent Fish Freshness Assessment

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Hamid Gholam Hosseini

2008-01-01

Full Text Available Fish species identification and automated fish freshness assessment play important roles in fishery industry applications. This paper describes a method based on support vector machines (SVMs to improve the performance of fish identification systems. The result is used for the assessment of fish freshness using artificial neural network (ANN. Identification of the fish species involves processing of the images of fish. The most efficient features were extracted and combined with the down-sampled version of the images to create a 1D input vector. Max-Win algorithm applied to the SVM-based classifiers has enhanced the reliability of sorting to 96.46%. The realisation of Cyranose 320 Electronic nose (E-nose, in order to evaluate the fish freshness in real-time, is experimented. Intelligent processing of the sensor patterns involves the use of a dedicated ANN for each species under study. The best estimation of freshness was provided by the most sensitive sensors. Data was collected from four selected species of fishes over a period of ten days. It was concluded that the performance can be increased using individual trained ANN for each specie. The proposed system has been successful in identifying the number of days after catching the fish with an accuracy of up to 91%.

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

A Multiuser Detector Based on Artificial Bee Colony Algorithm for DS-UWB Systems

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Zhendong Yin

2013-01-01

Full Text Available Artificial Bee Colony (ABC algorithm is an optimization algorithm based on the intelligent behavior of honey bee swarm. The ABC algorithm was developed to solve optimizing numerical problems and revealed premising results in processing time and solution quality. In ABC, a colony of artificial bees search for rich artificial food sources; the optimizing numerical problems are converted to the problem of finding the best parameter which minimizes an objective function. Then, the artificial bees randomly discover a population of initial solutions and then iteratively improve them by employing the behavior: moving towards better solutions by means of a neighbor search mechanism while abandoning poor solutions. In this paper, an efficient multiuser detector based on a suboptimal code mapping multiuser detector and artificial bee colony algorithm (SCM-ABC-MUD is proposed and implemented in direct-sequence ultra-wideband (DS-UWB systems under the additive white Gaussian noise (AWGN channel. The simulation results demonstrate that the BER and the near-far effect resistance performances of this proposed algorithm are quite close to those of the optimum multiuser detector (OMD while its computational complexity is much lower than that of OMD. Furthermore, the BER performance of SCM-ABC-MUD is not sensitive to the number of active users and can obtain a large system capacity.

Ideal Directed-Energy System To Defeat Small Unmanned Aircraft System Swarms

Science.gov (United States)

2017-05-21

large number of animate or inanimate things massed together and usually in motion.”19 Unlike bees that developed swarming behaviors over time...set multiple records in recent years. From 2015 to 2017, Intel increased the quantity of sUAS in their light shows conducted around the world from...successfully- tests- worlds -largest-micro-drone-swarm. 25 Ibid. 26 Chris Loterina, “Department Of Defense Tests Swarm Of 3D-Printed Micro-Drones Called Perdix

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...

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

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.

Attacks and Intrusion Detection in Cloud Computing Using Neural Networks and Particle Swarm Optimization Algorithms

Directory of Open Access Journals (Sweden)

Ahmad Shokuh Saljoughi

2018-01-01

Full Text Available Today, cloud computing has become popular among users in organizations and companies. Security and efficiency are the two major issues facing cloud service providers and their customers. Since cloud computing is a virtual pool of resources provided in an open environment (Internet, cloud-based services entail security risks. Detection of intrusions and attacks through unauthorized users is one of the biggest challenges for both cloud service providers and cloud users. In the present study, artificial intelligence techniques, e.g. MLP Neural Network sand particle swarm optimization algorithm, were used to detect intrusion and attacks. The methods were tested for NSL-KDD, KDD-CUP datasets. The results showed improved accuracy in detecting attacks and intrusions by unauthorized users.

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

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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.

Artificial Feeds Given in Different Dose to the Growth and Feed Consumption of Semah Fish Seed (Tor Douronensis in Order to Domestication

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. Sunarto

2009-01-01

Full Text Available Semah fish (Tor douronensis is a kind of freshwater fish pertained as a wild fish that almost extint and rare, therefore, it is necessary to preserve through the culture activity. Meanwhile, in fish culture effort, feed is considers as an important factor.  Thus, feed must meet a proper quality and quantity due to the fish maintenance, growth, and reproduction requirement. Test feed employed in this research was an artificial feed in form of pellet which was consists of 40% protein by dose tested of 3%, 6%, 9% and 12% of biomass weight. The result indicated that daily growth rate was ranged between 1.44-1.99% by highest growth achieved at feed dose 6% and from the quadratic regression analysis achieved optimal dose by 6.18%.  Daily feed comsumption rate of semah fish seed was ranged between 2.69-10.19% per day.  Feed efficiency was ranged between 13.85-54.09%, and survival rate was 100%. Keywords: dose, growth, feed comsumption, semah fish, Tor douronensis   ABSTRAK Ikan semah (Tor douronensis adalah jenis ikan air tawar yang tergolong jenis ikan liar yang hampir punah dan sudah langka, karena itu perlu upaya pelestariannya dengan usaha pembudidayaan. Dalam usaha budidaya ikan, pakan merupakan salah satu faktor penting. Oleh sebab itu pakan harus berkualitas dengan kuantitas yang tepat sesuai dengan kebutuhan ikan untuk pertumbuhannya, pemeliharaan tubuh dan reproduksi. Pakan uji yang digunakan dalam penelitian ini adalah pakan buatan berupa pelet yang mengandung  protein 40% dengan dosis pakan yang diuji 3%, 6%, 9% dan 12% dari bobot biomassa. Hasil menunjukkan laju pertumbuhan harian berkisar antara 1,99-1,44% dengan pertumbuhan tertinggi dicapai pada dosis pakan 6% dan dari analisis regresi kwadratik diperoleh dosis optimum sebesar 6,18%. Laju konsumsi harian benih ikan semah selama penelitian ini berkisar antara 2,69-10,19 %/hari. Efesiensi pakan berkisar antara 54,09-13,85%, dan tingkat kelangsungan hidup 100% Kata kunci: dosis

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).

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

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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.

A hybrid artificial bee colony algorithm for numerical function optimization

Science.gov (United States)

Alqattan, Zakaria N.; Abdullah, Rosni

2015-02-01

Artificial Bee Colony (ABC) algorithm is one of the swarm intelligence algorithms; it has been introduced by Karaboga in 2005. It is a meta-heuristic optimization search algorithm inspired from the intelligent foraging behavior of the honey bees in nature. Its unique search process made it as one of the most competitive algorithm with some other search algorithms in the area of optimization, such as Genetic algorithm (GA) and Particle Swarm Optimization (PSO). However, the ABC performance of the local search process and the bee movement or the solution improvement equation still has some weaknesses. The ABC is good in avoiding trapping at the local optimum but it spends its time searching around unpromising random selected solutions. Inspired by the PSO, we propose a Hybrid Particle-movement ABC algorithm called HPABC, which adapts the particle movement process to improve the exploration of the original ABC algorithm. Numerical benchmark functions were used in order to experimentally test the HPABC algorithm. The results illustrate that the HPABC algorithm can outperform the ABC algorithm in most of the experiments (75% better in accuracy and over 3 times faster).

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.

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.

Artificial enzyme-powered microfish for water-quality testing.

Science.gov (United States)

Orozco, Jahir; García-Gradilla, Victor; D'Agostino, Mattia; Gao, Wei; Cortés, Allan; Wang, Joseph

2013-01-22

We present a novel micromotor-based strategy for water-quality testing based on changes in the propulsion behavior of artificial biocatalytic microswimmers in the presence of aquatic pollutants. The new micromotor toxicity testing concept mimics live-fish water testing and relies on the toxin-induced inhibition of the enzyme catalase, responsible for the biocatalytic bubble propulsion of tubular microengines. The locomotion and survival of the artificial microfish are thus impaired by exposure to a broad range of contaminants, that lead to distinct time-dependent irreversible losses in the catalase activity, and hence of the propulsion behavior. Such use of enzyme-powered biocompatible polymeric (PEDOT)/Au-catalase tubular microengine offers highly sensitive direct optical visualization of changes in the swimming behavior in the presence of common contaminants and hence to a direct real-time assessment of the water quality. Quantitative data on the adverse effects of the various toxins upon the swimming behavior of the enzyme-powered artificial swimmer are obtained by estimating common ecotoxicological parameters, including the EC(50) (exposure concentration causing 50% attenuation of the microfish locomotion) and the swimmer survival time (lifetime expectancy). Such novel use of artificial microfish addresses major standardization and reproducibility problems as well as ethical concerns associated with live-fish toxicity assays and hence offers an attractive alternative to the common use of aquatic organisms for water-quality testing.

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.

Coevolution of Artificial Agents Using Evolutionary Computation in Bargaining Game

Directory of Open Access Journals (Sweden)

Sangwook Lee

2015-01-01

Full Text Available Analysis of bargaining game using evolutionary computation is essential issue in the field of game theory. This paper investigates the interaction and coevolutionary process among heterogeneous artificial agents using evolutionary computation (EC in the bargaining game. In particular, the game performance with regard to payoff through the interaction and coevolution of agents is studied. We present three kinds of EC based agents (EC-agent participating in the bargaining game: genetic algorithm (GA, particle swarm optimization (PSO, and differential evolution (DE. The agents’ performance with regard to changing condition is compared. From the simulation results it is found that the PSO-agent is superior to the other agents.

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

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.

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.

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.

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

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.

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

Pasteurization of fish meal by irradiation. Pt. 1

International Nuclear Information System (INIS)

Reusse, U.; Bischoff, J.; Fleischhauer, G.; Geister, R.

1976-01-01

Studies were made on a number of samples of fish meal heavily contaminated (several foci of infection per sample, corresponding 'more than 100% contamination') with salmonella. A dose of 0.7 Mrad proved to be sufficient to inactivate all salmonellaes in all samples. The apparently reduced effect of radiation after artificial contamination of the meal was due to the extreme super-infection. Inactivation curves showed that the salmonella strains used for artificial contamination were more susceptible than those in naturally infected fish meal. Calculation of the slope parameter for a single salmonella type enables the dose of radiation needed to ensure freedom of the meal from salmonella to be determined for each level of infection. With two naturally infected fish meals which contained a total of 18 different serotypes a mean slope parameter of b = -1.99 was calculated which met the requirements posed by the problem of freeing meal from salmonella. (orig.) [de

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.

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.

Artificial Immune Systems as a Modern Tool for Solving Multi-Purpose Optimization Tasks in the Field of Logistics

Directory of Open Access Journals (Sweden)

Skitsko Volodymyr I.

2017-03-01

Full Text Available The article investigates various aspects of the functioning of artificial immune systems and their using to solve different tasks. The analysis of the studied literature showed that nowadays there exist combinations of artificial immune systems, in particular with genetic algorithms, the particle swarm optimization method, artificial neural networks, etc., to solve different tasks. However, the solving of economic tasks is paid little attention. The article presents the basic terminology of artificial immune systems; the steps of the clonal selection algorithm are described, as well as a brief description of the negative selection algorithm, the immune network algorithm and the dendritic algorithm is given; conceptual aspects of the use of an artificial immune system for solving multi-purpose optimization problems are formulated, and an example of solving a problem in the field of logistics is described. Artificial immune systems as a means of solving various weakly structured, multi-criteria and multi-purpose economic tasks, in particular in the sphere of logistics, are a promising tool that requires further research. Therefore, it is advisable in the future to focus on the use of various existing immune algorithms for solving various economic problems.

Estimation of Valve Stiction Using Particle Swarm Optimization

Directory of Open Access Journals (Sweden)

S. Sivagamasundari

2011-06-01

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

Self-regulating and self-evolving particle swarm optimizer

Science.gov (United States)

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

2015-01-01

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

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

Directory of Open Access Journals (Sweden)

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

Directory of Open Access Journals (Sweden)

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

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

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.

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.

Biodiversity in floodplains with special reference to artificial stocking

OpenAIRE

Hossain, M.S.; Ehshan, M.A.; Mazid, M.A.; Rahman, S.; Razzaque, A.

2000-01-01

A five years investigation on fish biodiversity in connection with artificial stocking was conducted in three south-western floodplains of Bangladesh from 1992 to 1996. The ten top most available and ten rarest fish species were identified. Puntius sp., Channa punctatus, Mystus sp., Anabus testudinius, Ambasis sp., Colisha sp. and Macrobrachium sp. etc. were the most common available species. On the other hand, Mystus aor, Notopterus chitala, Clupisoma garua, Aplocheilus panchax, Ctenophmyngo...

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.

Optimizing Blasting’s Air Overpressure Prediction Model using Swarm Intelligence

Science.gov (United States)

Nur Asmawisham Alel, Mohd; Ruben Anak Upom, Mark; Asnida Abdullah, Rini; Hazreek Zainal Abidin, Mohd

2018-04-01

Air overpressure (AOp) resulting from blasting can cause damage and nuisance to nearby civilians. Thus, it is important to be able to predict AOp accurately. In this study, 8 different Artificial Neural Network (ANN) were developed for the purpose of prediction of AOp. The ANN models were trained using different variants of Particle Swarm Optimization (PSO) algorithm. AOp predictions were also made using an empirical equation, as suggested by United States Bureau of Mines (USBM), to serve as a benchmark. In order to develop the models, 76 blasting operations in Hulu Langat were investigated. All the ANN models were found to outperform the USBM equation in three performance metrics; root mean square error (RMSE), mean absolute percentage error (MAPE) and coefficient of determination (R2). Using a performance ranking method, MSO-Rand-Mut was determined to be the best prediction model for AOp with a performance metric of RMSE=2.18, MAPE=1.73% and R2=0.97. The result shows that ANN models trained using PSO are capable of predicting AOp with great accuracy.

Particle Swarm Optimization applied to combinatorial problem aiming the fuel recharge problem solution in a nuclear reactor; Particle swarm optimization aplicado ao problema combinatorio com vistas a solucao do problema de recarga em um reator nuclear

Energy Technology Data Exchange (ETDEWEB)

Meneses, Anderson Alvarenga de Moura; Schirru, Roberto [Universidade Federal, Rio de Janeiro, RJ (Brazil). Coordenacao dos Programas de Pos-graduacao de Engenharia. Programa de Engenharia Nuclear]. E-mail: ameneses@con.ufrj.br; schirru@lmp.ufrj.br

2005-07-01

This work focuses on the usage the Artificial Intelligence technique Particle Swarm Optimization (PSO) to optimize the fuel recharge at a nuclear reactor. This is a combinatorial problem, in which the search of the best feasible solution is done by minimizing a specific objective function. However, in this first moment it is possible to compare the fuel recharge problem with the Traveling Salesman Problem (TSP), since both of them are combinatorial, with one advantage: the evaluation of the TSP objective function is much more simple. Thus, the proposed methods have been applied to two TSPs: Oliver 30 and Rykel 48. In 1995, KENNEDY and EBERHART presented the PSO technique to optimize non-linear continued functions. Recently some PSO models for discrete search spaces have been developed for combinatorial optimization. Although all of them having different formulation from the ones presented here. In this paper, we use the PSO theory associated with to the Random Keys (RK)model, used in some optimizations with Genetic Algorithms. The Particle Swarm Optimization with Random Keys (PSORK) results from this association, which combines PSO and RK. The adaptations and changings in the PSO aim to allow the usage of the PSO at the nuclear fuel recharge. This work shows the PSORK being applied to the proposed combinatorial problem and the obtained results. (author)

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....

Quality and biochemical properties of artificially hibernated crucian carp for waterless preservation.

Science.gov (United States)

Mi, Hongbo; Qian, Chunlu; Mao, Linchun

2012-12-01

The aim of this study was to explore the artificial hibernation of crucian carp for waterless preservation and to characterize the quality and biochemical properties during and after the hibernation. Anesthetized crucian carp using eugenol were stored at 8 °C with 90 % oxygen and 95-100 % relative humidity for 38 h and then transferred to fresh water to recover. Liquid loss and cooking loss had no significant changes (p > 0.05). The total volatile basic nitrogen content and 2-thiobarbituric acid value in hibernated fish were significantly higher (p 0.05). Both ACP and AKP activities decreased upon the fish recovered, but only the ACP activity returned to normal. However, there were increased serum glucose concentration, GOT and GPT activities in recovered fish. On the basis of these findings, it can be concluded that the artificially hibernated life of crucian carp was 38 h by the combination of anaesthetizing and low temperature. The muscle quality would not be influenced, and most of the stress responses would disappear after hibernated fish recovered.

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.

Arsenic bioaccumulation in a marine juvenile fish Terapon jarbua

International Nuclear Information System (INIS)

Zhang Wei; Huang Liangmin; Wang Wenxiong

2011-01-01

Highlights: Radiotracer technique was used to quantify the biokinetics of As(V) in a marine fish. As(V) had a low bioavailability to Terapon jarbua. Dietary assimilation of As was only 3.1–7.4% for fish fed with different preys. Dietary uptake could be the primary route for As bioaccumulation in fish. - Abstract: Arsenic (As) is a ubiquitous toxic metalloid that is causing widespread public concern. Recent measurements have indicated that some marine fish in China might be seriously contaminated with As. Yet the biokinetics and bioaccumulation pathway of As in fish remain little understood. In this study, we employed a radiotracer technique to quantify the dissolved uptake, dietary assimilation and subsequent efflux of As(V) in a marine predatory fish, Terapon jarbua. The dissolved uptake of As showed a linear pattern over a range of dissolved concentrations from 0.5 to 50 μg L −1 , with a corresponding uptake rate constant of 0.0015 L g −1 d −1 . The assimilation efficiencies (AEs) of dietary As were only 3.1–7.4% for fish fed with copepods, clams, prey fish, or artificial diets, and were much lower than the As that entered the trophically available metal fraction in the prey. The dietary AEs were independent of the As(V) concentrations in the artificial diets. The efflux rate constant of As in fish following the dietary exposure was 0.03 d −1 . Modeling calculations showed that dietary uptake could be the primary route for As bioaccumulation in fish, and the corresponding contributions of waterborne and dietary uptakes were related to the bioconcentration factor (BCF) of the prey and the ingestion rate of fish. This study demonstrates that As(V) has a low bioavailability to T. jarbua.

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...

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)

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...

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.

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.

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.

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.

Particle swarm optimization algorithm based low cost magnetometer calibration

Science.gov (United States)

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

2011-12-01

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

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.

Development of nylon-based artificial muscles for the usage in robotic prosthetic limb

Science.gov (United States)

Atikah, Nurul Anis; Weng, Leong Yeng; Anuar, Adzly; Fat, Chau Chien; Abidin, Izham Zainal; Sahari, Khairul Salleh Mohamed

2017-09-01

This paper describes the development of nylon-based artificial muscles that is intended to be used in prosthetic limb for young amputees. Prosthetic limbs are very expensive and this situation is further compounded for young amputees who are very quickly out-grow their prosthesis. The proposed artificial muscles are made of nylon fishing strings from various size such as 0.45mm, 0.55mm, 0.65mm and 1.00mm. These fishing strings were twisted into coils to create Super Coiled Polymers (SCP) and tested using hot air blower. These artificial muscles react counterintuitively, where when it is exposed to heat, contracts, and when cooled, expands. Peltier devices, when switched-on acts as heat pump, where one side is hot and the other is cold. This phenomenon, when affixed in between 2 SCP's, creates tandem motion similar to triceps and biceps. As initial study, the hot side of the Peltier module was tested using these artificial muscles. The string was measured for both its force production, length contraction, the initial results were promising.

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...

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...

A Modified Artificial Bee Colony Algorithm for p-Center Problems

Directory of Open Access Journals (Sweden)

Alkın Yurtkuran

2014-01-01

Full Text Available The objective of the p-center problem is to locate p-centers on a network such that the maximum of the distances from each node to its nearest center is minimized. The artificial bee colony algorithm is a swarm-based meta-heuristic algorithm that mimics the foraging behavior of honey bee colonies. This study proposes a modified ABC algorithm that benefits from a variety of search strategies to balance exploration and exploitation. Moreover, random key-based coding schemes are used to solve the p-center problem effectively. The proposed algorithm is compared to state-of-the-art techniques using different benchmark problems, and computational results reveal that the proposed approach is very efficient.

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...

Loading pattern optimization of PWR reactors using Artificial Bee Colony

International Nuclear Information System (INIS)

Safarzadeh, O.; Zolfaghari, A.; Norouzi, A.; Minuchehr, H.

2011-01-01

Highlights: → ABC algorithm is comparable to the canonical GA algorithm and PSO. → The performance of ABC shows that the algorithm is quiet promising. → The final band width of search fitness values by ABC is narrow. → The ABC algorithm is relatively easy to implement. - Abstract: In this paper a core reloading technique using Artificial Bee Colony algorithm, ABC, is presented in the context of finding an optimal configuration of fuel assemblies. The proposed method can be used for in-core fuel management optimization problems in pressurized water reactors. To evaluate the proposed technique, the power flattening of a VVER-1000 core is considered as an objective function although other variables such as K eff , power peaking factor, burn up and cycle length can also be taken into account. The proposed optimization method is applied to a core design optimization problem previously solved with Genetic and Particle Swarm Intelligence Algorithm. The results, convergence rate and reliability of the new method are quite promising and show that the ABC algorithm performs very well and is comparable to the canonical Genetic Algorithm and Particle Swarm Intelligence, hence demonstrating its potential for other optimization applications in nuclear engineering field as, for instance, the cascade problems.

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.

Artificial neural network and particle swarm optimization for removal of methyl orange by gold nanoparticles loaded on activated carbon and Tamarisk

Science.gov (United States)

Ghaedi, M.; Ghaedi, A. M.; Ansari, A.; Mohammadi, F.; Vafaei, A.

2014-11-01

The influence of variables, namely initial dye concentration, adsorbent dosage (g), stirrer speed (rpm) and contact time (min) on the removal of methyl orange (MO) by gold nanoparticles loaded on activated carbon (Au-NP-AC) and Tamarisk were investigated using multiple linear regression (MLR) and artificial neural network (ANN) and the variables were optimized by partial swarm optimization (PSO). Comparison of the results achieved using proposed models, showed the ANN model was better than the MLR model for prediction of methyl orange removal using Au-NP-AC and Tamarisk. Using the optimal ANN model the coefficient of determination (R2) for the test data set were 0.958 and 0.989; mean squared error (MSE) values were 0.00082 and 0.0006 for Au-NP-AC and Tamarisk adsorbent, respectively. In this study a novel and green approach were reported for the synthesis of gold nanoparticle and activated carbon by Tamarisk. This material was characterized using different techniques such as SEM, TEM, XRD and BET. The usability of Au-NP-AC and activated carbon (AC) Tamarisk for the methyl orange from aqueous solutions was investigated. The effect of variables such as pH, initial dye concentration, adsorbent dosage (g) and contact time (min) on methyl orange removal were studied. Fitting the experimental equilibrium data to various isotherm models such as Langmuir, Freundlich, Tempkin and Dubinin-Radushkevich models show the suitability and applicability of the Langmuir model. Kinetic models such as pseudo-first order, pseudo-second order, Elovich and intraparticle diffusion models indicate that the second-order equation and intraparticle diffusion models control the kinetic of the adsorption process. The small amount of proposed Au-NP-AC and activated carbon (0.015 g and 0.75 g) is applicable for successful removal of methyl orange (>98%) in short time (20 min for Au-NP-AC and 45 min for Tamarisk-AC) with high adsorption capacity 161 mg g-1 for Au-NP-AC and 3.84 mg g-1 for Tamarisk-AC.

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.

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

Fish habitat preferences in an artificial reservoir system

Czech Academy of Sciences Publication Activity Database

Prchalová, Marie; Kubečka, Jan; Hladík, Milan; Hohausová, Eva; Čech, Martin; Frouzová, Jaroslava

2006-01-01

Roč. 29, č. 4 (2006), s. 1890-1894 ISSN 0368-0770. [Congress of SIL - International association of theoretical and applied limnology /29./. Lahti, 08.08.2004-14.08.2004] R&D Projects: GA ČR(CZ) GA206/02/0520 Institutional research plan: CEZ:AV0Z60170517 Keywords : multivariate analysis * fish community * reservoirs * spatial distribution Subject RIV: EH - Ecology, Behaviour

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.

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

Particle Swarm Optimization applied to combinatorial problem aiming the fuel recharge problem solution in a nuclear reactor

International Nuclear Information System (INIS)

Meneses, Anderson Alvarenga de Moura; Schirru, Roberto

2005-01-01

This work focuses on the usage the Artificial Intelligence technique Particle Swarm Optimization (PSO) to optimize the fuel recharge at a nuclear reactor. This is a combinatorial problem, in which the search of the best feasible solution is done by minimizing a specific objective function. However, in this first moment it is possible to compare the fuel recharge problem with the Traveling Salesman Problem (TSP), since both of them are combinatorial, with one advantage: the evaluation of the TSP objective function is much more simple. Thus, the proposed methods have been applied to two TSPs: Oliver 30 and Rykel 48. In 1995, KENNEDY and EBERHART presented the PSO technique to optimize non-linear continued functions. Recently some PSO models for discrete search spaces have been developed for combinatorial optimization. Although all of them having different formulation from the ones presented here. In this paper, we use the PSO theory associated with to the Random Keys (RK)model, used in some optimizations with Genetic Algorithms. The Particle Swarm Optimization with Random Keys (PSORK) results from this association, which combines PSO and RK. The adaptations and changings in the PSO aim to allow the usage of the PSO at the nuclear fuel recharge. This work shows the PSORK being applied to the proposed combinatorial problem and the obtained results. (author)

A Hybrid Swarm Intelligence Algorithm for Intrusion Detection Using Significant Features

Directory of Open Access Journals (Sweden)

P. Amudha

2015-01-01

Full Text Available Intrusion detection has become a main part of network security due to the huge number of attacks which affects the computers. This is due to the extensive growth of internet connectivity and accessibility to information systems worldwide. To deal with this problem, in this paper a hybrid algorithm is proposed to integrate Modified Artificial Bee Colony (MABC with Enhanced Particle Swarm Optimization (EPSO to predict the intrusion detection problem. The algorithms are combined together to find out better optimization results and the classification accuracies are obtained by 10-fold cross-validation method. The purpose of this paper is to select the most relevant features that can represent the pattern of the network traffic and test its effect on the success of the proposed hybrid classification algorithm. To investigate the performance of the proposed method, intrusion detection KDDCup’99 benchmark dataset from the UCI Machine Learning repository is used. The performance of the proposed method is compared with the other machine learning algorithms and found to be significantly different.

Impact of coastal defence structures (tetrapods) on a demersal hard-bottom fish community in the southern North Sea.

Science.gov (United States)

Wehkamp, Stephanie; Fischer, Philipp

2013-02-01

In the coming decades, artificial defence structures will increase in importance worldwide for the protection of coasts against the impacts of global warming. However, the ecological effects of such structures on the natural surroundings remain unclear. We investigated the impact of experimentally introduced tetrapod fields on the demersal fish community in a hard-bottom area in the southern North Sea. The results indicated a significant decrease in fish abundance in the surrounding area caused by migration effects towards the artificial structures. Diversity (HB) and evenness (E) values exhibited greater variation after the introduction of the tetrapods. Additionally, a distinct increase in young-of-the-year (YOY) fish was observed near the structures within the second year after introduction. We suggest that the availability of adequate refuges in combination with additional food resources provided by the artificial structures has a highly species-specific attraction effect. However, these findings also demonstrate that our knowledge regarding the impact of artificial structures on temperate fish communities is still too limited to truly understand the ecological processes that are initiated by the introduction of artificial structures. Long-term investigations and additional experimental in situ work worldwide will be indispensable for a full understanding of the mechanisms by which coastal defence structures interact with the coastal environment. Copyright © 2012 Elsevier Ltd. All rights reserved.

Effects of deuterium depleted water on reproduction of Rainbow fish

International Nuclear Information System (INIS)

Stefanescu, Ion; Saros-Rogobete, Irina; Titescu, Gheorghe; Caraus, Ion; Pricop, Ferdinand

2001-01-01

The paper refers to an isotopic composition used to prepare fecundating solutions for artificial reproduction of fish. The solution is constituted as a mixture of deuterium depleted water and natural water (whose isotopic concentration is of 85-90 ppm D/(D+H)) in which we can add activating and energizing substances. This fecundating solution ensures an improved fecundating level of fish roe, increase life index in the next growth up stages and increase fish resistance at special medium conditions. (authors)

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

RESULTS CONCERNING THE USE OF THE‚NERISTIN’ SYNTHETIC HORMONE IN THE ARTIFICIAL REPRODUCTION OF THE HYPOPHTHALMYCHTYS MOLITRIX (VAL SPECIES

Directory of Open Access Journals (Sweden)

ADINA SIRBU

2009-10-01

Full Text Available The paper presents data about the artificial reproduction of the H. molitrix species. The artificial reproduction took place in the station destined to the reproduction of the phytoplanktonophag fish of the Carja 1 fish farm – Vaslui. The annual batch of breeders came from the personal stock of the Carja 1 farm.The experimental work took place between 2005-2007, following the work methodology and the phases of the technological process. The stimulation of the maturation was done with neristin and carp hypophysis, watching through comparison the technological indicators specific to the artificial reproduction.The results of the experiments are presented in tables and in graphs.

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...

Improving the Adaptability of Simulated Evolutionary Swarm Robots in Dynamically Changing Environments

Science.gov (United States)

Yao, Yao; Marchal, Kathleen; Van de Peer, Yves

2014-01-01

One of the important challenges in the field of evolutionary robotics is the development of systems that can adapt to a changing environment. However, the ability to adapt to unknown and fluctuating environments is not straightforward. Here, we explore the adaptive potential of simulated swarm robots that contain a genomic encoding of a bio-inspired gene regulatory network (GRN). An artificial genome is combined with a flexible agent-based system, representing the activated part of the regulatory network that transduces environmental cues into phenotypic behaviour. Using an artificial life simulation framework that mimics a dynamically changing environment, we show that separating the static from the conditionally active part of the network contributes to a better adaptive behaviour. Furthermore, in contrast with most hitherto developed ANN-based systems that need to re-optimize their complete controller network from scratch each time they are subjected to novel conditions, our system uses its genome to store GRNs whose performance was optimized under a particular environmental condition for a sufficiently long time. When subjected to a new environment, the previous condition-specific GRN might become inactivated, but remains present. This ability to store ‘good behaviour’ and to disconnect it from the novel rewiring that is essential under a new condition allows faster re-adaptation if any of the previously observed environmental conditions is reencountered. As we show here, applying these evolutionary-based principles leads to accelerated and improved adaptive evolution in a non-stable environment. PMID:24599485

Improving the adaptability of simulated evolutionary swarm robots in dynamically changing environments.

Directory of Open Access Journals (Sweden)

Yao Yao

Full Text Available One of the important challenges in the field of evolutionary robotics is the development of systems that can adapt to a changing environment. However, the ability to adapt to unknown and fluctuating environments is not straightforward. Here, we explore the adaptive potential of simulated swarm robots that contain a genomic encoding of a bio-inspired gene regulatory network (GRN. An artificial genome is combined with a flexible agent-based system, representing the activated part of the regulatory network that transduces environmental cues into phenotypic behaviour. Using an artificial life simulation framework that mimics a dynamically changing environment, we show that separating the static from the conditionally active part of the network contributes to a better adaptive behaviour. Furthermore, in contrast with most hitherto developed ANN-based systems that need to re-optimize their complete controller network from scratch each time they are subjected to novel conditions, our system uses its genome to store GRNs whose performance was optimized under a particular environmental condition for a sufficiently long time. When subjected to a new environment, the previous condition-specific GRN might become inactivated, but remains present. This ability to store 'good behaviour' and to disconnect it from the novel rewiring that is essential under a new condition allows faster re-adaptation if any of the previously observed environmental conditions is reencountered. As we show here, applying these evolutionary-based principles leads to accelerated and improved adaptive evolution in a non-stable environment.

An Integrated Intrusion Detection Model of Cluster-Based Wireless Sensor Network.

Science.gov (United States)

Sun, Xuemei; Yan, Bo; Zhang, Xinzhong; Rong, Chuitian

2015-01-01

Considering wireless sensor network characteristics, this paper combines anomaly and mis-use detection and proposes an integrated detection model of cluster-based wireless sensor network, aiming at enhancing detection rate and reducing false rate. Adaboost algorithm with hierarchical structures is used for anomaly detection of sensor nodes, cluster-head nodes and Sink nodes. Cultural-Algorithm and Artificial-Fish-Swarm-Algorithm optimized Back Propagation is applied to mis-use detection of Sink node. Plenty of simulation demonstrates that this integrated model has a strong performance of intrusion detection.

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.

Reproduction of Persian Gulf anemone fish (Amphiprion clarkii) in captive system

OpenAIRE

Javad Sahandi

2011-01-01

The present study was carried out to assess the reproduction of Persian Gulf anemone fish,Amphiprion clarkii (Bennett, 1830), in captive conditions with artificial features. Persian Gulf, havinggood relation with Indian Ocean, is one of the important niches of fishes and the specific position ofthis Gulf makes its fishes popular. The yellow tail clown fish which originates to this gulf has the bestsurvival rate and health than the other areas. Live food is the most important factor in product...

An Improved Artificial Bee Colony Algorithm and Its Application to Multi-Objective Optimal Power Flow

Directory of Open Access Journals (Sweden)

Xuanhu He

2015-03-01

Full Text Available Optimal power flow (OPF objective functions involve minimization of the total fuel costs of generating units, minimization of atmospheric pollutant emissions, minimization of active power losses and minimization of voltage deviations. In this paper, a fuzzy multi-objective OPF model is established by the fuzzy membership functions and the fuzzy satisfaction-maximizing method. The improved artificial bee colony (IABC algorithm is applied to solve the model. In the IABC algorithm, the mutation and crossover operations of a differential evolution algorithm are utilized to generate new solutions to improve exploitation capacity; tent chaos mapping is utilized to generate initial swarms, reference mutation solutions and the reference dimensions of crossover operations to improve swarm diversity. The proposed method is applied to multi-objective OPF problems in IEEE 30-bus, IEEE 57-bus and IEEE 300-bus test systems. The results are compared with those obtained by other algorithms, which demonstrates the effectiveness and superiority of the IABC algorithm, and how the optimal scheme obtained by the proposed model can make systems more economical and stable.

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....

Convergence analysis of particle swarm optimization (PSO) method on the with-in host dengue infection treatment model

Science.gov (United States)

Handayani, D.; Nuraini, N.; Tse, O.; Saragih, R.; Naiborhu, J.

2016-04-01

PSO is a computational optimization method motivated by the social behavior of organisms like bird flocking, fish schooling and human social relations. PSO is one of the most important swarm intelligence algorithms. In this study, we analyze the convergence of PSO when it is applied to with-in host dengue infection treatment model simulation in our early research. We used PSO method to construct the initial adjoin equation and to solve a control problem. Its properties of control input on the continuity of objective function and ability of adapting to the dynamic environment made us have to analyze the convergence of PSO. With the convergence analysis of PSO we will have some parameters that ensure the convergence result of numerical simulations on this model using PSO.

Efficiency of three reproductive hormones in artificial propagation of ...

African Journals Online (AJOL)

Efficiency of three reproductive hormones: ovaprim, ovulin and carp pituitary extract (C.P.E) in artificial propagation of Heterobranchus longifilis was investigated. Total of twelve broodstock fish, comprising nine females and three males at eighteen months of age were used. Females were divided into three groups.

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.

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.

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)

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-

RESULTS CONCERNING THE USE OF THE‚NERISTIN’ SYNTHETIC HORMONE IN THE ARTIFICIAL REPRODUCTION OF THE HYPOPHTHALMYCHTYS MOLITRIX (VAL) SPECIES

OpenAIRE

ADINA SIRBU; S. STANCIOIU; V. CRISTEA; A. DOCAN

2009-01-01

The paper presents data about the artificial reproduction of the H. molitrix species. The artificial reproduction took place in the station destined to the reproduction of the phytoplanktonophag fish of the Carja 1 fish farm – Vaslui. The annual batch of breeders came from the personal stock of the Carja 1 farm.The experimental work took place between 2005-2007, following the work methodology and the phases of the technological process. The stimulation of the maturation was done with neristin...

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...

Possible applications of modern fish larviculture technology to ornamental fish production

OpenAIRE

Dhert, P.; Lim, L.C.; Candreva, P.; Van Duffel, H.; Sorgeloos, P.

1997-01-01

There has been rapid development in the marine foodfish larviculture technology in Europe since the early eighties, especially in the flat fish, turbot and halibut, and the bass and bream species. The most significant improvements in the eighties were the introduction of light control, artificial reproduction techniques, appropriate water treatment and the use of rotifers and Artemia nauplii of specific sizes and in the late eighties and early nineties the quality enhancement of live food org...

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

Imaging dipole flow sources using an artificial lateral-line system made of biomimetic hair flow sensors

NARCIS (Netherlands)

Dagamseh, A.M.K.; Wiegerink, Remco J.; Lammerink, Theodorus S.J.; Krijnen, Gijsbertus J.M.

2013-01-01

In Nature, fish have the ability to localize prey, school, navigate, etc., using the lateral-line organ. Artificial hair flow sensors arranged in a linear array shape (inspired by the lateral-line system (LSS) in fish) have been applied to measure airflow patterns at the sensor positions. Here, we

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....

Impact of Noise on a Dynamical System: Prediction and Uncertainties from a Swarm-Optimized Neural Network