WorldWideScience

Sample records for swarm intelligence techniques

  1. 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)

  2. 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.       

  3. 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.

  4. Handbook of swarm intelligence concepts, principles and applications

    CERN Document Server

    Shi, Yuhui; Panigrahi, Bijaya Ketan

    2011-01-01

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

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

    Science.gov (United States)

    Goodarzi, Mohammad; dos Santos Coelho, Leandro

    2014-12-10

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

  6. Simulation of Swarm Intelligence and Possible Applications in Engineering

    OpenAIRE

    Öztürk, Savaş; Esin, E.

    2003-01-01

    Modeling biological and natural systems in order to solve complex problems have become popular. Traditional techniques fail at solving some types of problems. On the other hand, it is seen that these kind of problems are solved in nature without help of human. Swarm intelligence(SI) as a research field, proposes such solutions. SI models the collective behavior of the social insects like ants, bees or termites and their coordination without communication. The emerged intelligence has some spe...

  7. 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.

  8. 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.

  9. 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.

  10. 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.

  11. 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...

  12. 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...

  13. 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.

  14. 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

  15. Efficient Networks Communication Routing Using Swarm Intelligence

    OpenAIRE

    Koushal Kumar

    2012-01-01

    As demonstrated by natural biological swarm’s collective intelligence has an abundance of desirable properties for problem-solving like in network routing. The focus of this paper is in the applications of swarm based intelligence in information routing for communication networks. As we know networks are growing and adopting new platforms as new technologies comes. Also according to new demands and requirements networks topologies and its complexity is increasing with time. Thus it is becomin...

  16. Foundations of Swarm Intelligence: From Principles to Practice

    National Research Council Canada - National Science Library

    Fleischer, Mark

    2003-01-01

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

  17. A Dynamic Multistage Hybrid Swarm Intelligence Optimization Algorithm for Function Optimization

    Directory of Open Access Journals (Sweden)

    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.

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

    Science.gov (United States)

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

    2015-12-01

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

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

    Directory of Open Access Journals (Sweden)

    Alireza Rowhanimanesh

    2017-07-01

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

  20. 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...

  1. 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

  2. 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...

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

    Directory of Open Access Journals (Sweden)

    Imran Rahman

    2015-01-01

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

  4. 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.

  5. Swarm Intelligence for Urban Dynamics Modelling

    International Nuclear Information System (INIS)

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

    2009-01-01

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

  6. Swarm Intelligence for Urban Dynamics Modelling

    Science.gov (United States)

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

    2009-04-01

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

  7. The Study of Intelligent Vehicle Navigation Path Based on Behavior Coordination of Particle Swarm.

    Science.gov (United States)

    Han, Gaining; Fu, Weiping; Wang, Wen

    2016-01-01

    In the behavior dynamics model, behavior competition leads to the shock problem of the intelligent vehicle navigation path, because of the simultaneous occurrence of the time-variant target behavior and obstacle avoidance behavior. Considering the safety and real-time of intelligent vehicle, the particle swarm optimization (PSO) algorithm is proposed to solve these problems for the optimization of weight coefficients of the heading angle and the path velocity. Firstly, according to the behavior dynamics model, the fitness function is defined concerning the intelligent vehicle driving characteristics, the distance between intelligent vehicle and obstacle, and distance of intelligent vehicle and target. Secondly, behavior coordination parameters that minimize the fitness function are obtained by particle swarm optimization algorithms. Finally, the simulation results show that the optimization method and its fitness function can improve the perturbations of the vehicle planning path and real-time and reliability.

  8. Robust data reconciliation and outlier detection with swarm intelligence in a thermal reactor power calculation

    Energy Technology Data Exchange (ETDEWEB)

    Valdetaro, Eduardo Damianik, E-mail: valdtar@eletronuclear.gov.br [ELETRONUCLEAR - ELETROBRAS, Angra dos Reis, RJ (Brazil). Angra 2 Operating Dept.; Coordenacao dos Programas de Pos-Graduacao de Engenharia (PEN/COPPE/UFRJ), RJ (Brazil). Programa de Engenharia Nuclear; Schirru, Roberto, E-mail: schirru@lmp.ufrj.br [Coordenacao dos Programas de Pos-Graduacao de Engenharia (PEN/COPPE/UFRJ), RJ (Brazil). Programa de Engenharia Nuclear

    2011-07-01

    In Nuclear power plants, Data Reconciliation (DR) and Gross Errors Detection (GED) are techniques of increasing interest and are primarily used to keep mass and energy balance into account, which brings outcomes as a direct and indirect financial benefits. Data reconciliation is formulated by a constrained minimization problem, where the constraints correspond to energy and mass balance model. Statistical methods are used combined with the minimization of quadratic error form. Solving nonlinear optimization problem using conventional methods can be troublesome, because a multimodal function with differentiated solutions introduces some difficulties to search an optimal solution. Many techniques were developed to solve Data Reconciliation and Outlier Detection, some of them use, for example, Quadratic Programming, Lagrange Multipliers, Mixed-Integer Non Linear Programming and others use evolutionary algorithms like Genetic Algorithms (GA) and recently the use of the Particle Swarm Optimization (PSO) showed to be a potential tool as a global optimization algorithm when applied to data reconciliation. Robust Statistics is also increasing in interest and it is being used when measured data are contaminated by random errors and one can not assume the error is normally distributed, situation which reflects real problems situation. The aim of this work is to present a brief comparison between the classical data reconciliation technique and the robust data reconciliation and gross error detection with swarm intelligence procedure in calculating the thermal reactor power for a simplified heat circuit diagram of a steam turbine plant using real data obtained from Angra 2 Nuclear power plant. The main objective is to test the potential of the robust DR and GED method in a integrated framework using swarm intelligence and the three part redescending estimator of Hampel when applied to a real process condition. The results evaluate the potential use of the robust technique in

  9. Robust data reconciliation and outlier detection with swarm intelligence in a thermal reactor power calculation

    International Nuclear Information System (INIS)

    Valdetaro, Eduardo Damianik; Coordenacao dos Programas de Pos-Graduacao de Engenharia; Schirru, Roberto

    2011-01-01

    In Nuclear power plants, Data Reconciliation (DR) and Gross Errors Detection (GED) are techniques of increasing interest and are primarily used to keep mass and energy balance into account, which brings outcomes as a direct and indirect financial benefits. Data reconciliation is formulated by a constrained minimization problem, where the constraints correspond to energy and mass balance model. Statistical methods are used combined with the minimization of quadratic error form. Solving nonlinear optimization problem using conventional methods can be troublesome, because a multimodal function with differentiated solutions introduces some difficulties to search an optimal solution. Many techniques were developed to solve Data Reconciliation and Outlier Detection, some of them use, for example, Quadratic Programming, Lagrange Multipliers, Mixed-Integer Non Linear Programming and others use evolutionary algorithms like Genetic Algorithms (GA) and recently the use of the Particle Swarm Optimization (PSO) showed to be a potential tool as a global optimization algorithm when applied to data reconciliation. Robust Statistics is also increasing in interest and it is being used when measured data are contaminated by random errors and one can not assume the error is normally distributed, situation which reflects real problems situation. The aim of this work is to present a brief comparison between the classical data reconciliation technique and the robust data reconciliation and gross error detection with swarm intelligence procedure in calculating the thermal reactor power for a simplified heat circuit diagram of a steam turbine plant using real data obtained from Angra 2 Nuclear power plant. The main objective is to test the potential of the robust DR and GED method in a integrated framework using swarm intelligence and the three part redescending estimator of Hampel when applied to a real process condition. The results evaluate the potential use of the robust technique in

  10. SOLVING ENGINEERING OPTIMIZATION PROBLEMS WITH THE SWARM INTELLIGENCE METHODS

    Directory of Open Access Journals (Sweden)

    V. Panteleev Andrei

    2017-01-01

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

  11. 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.

  12. Effectively Tackling Reinsurance Problems by Using Evolutionary and Swarm Intelligence Algorithms

    Directory of Open Access Journals (Sweden)

    Sancho Salcedo-Sanz

    2014-04-01

    Full Text Available This paper is focused on solving different hard optimization problems that arise in the field of insurance and, more specifically, in reinsurance problems. In this area, the complexity of the models and assumptions considered in the definition of the reinsurance rules and conditions produces hard black-box optimization problems (problems in which the objective function does not have an algebraic expression, but it is the output of a system (usually a computer program, which must be solved in order to obtain the optimal output of the reinsurance. The application of traditional optimization approaches is not possible in this kind of mathematical problem, so new computational paradigms must be applied to solve these problems. In this paper, we show the performance of two evolutionary and swarm intelligence techniques (evolutionary programming and particle swarm optimization. We provide an analysis in three black-box optimization problems in reinsurance, where the proposed approaches exhibit an excellent behavior, finding the optimal solution within a fraction of the computational cost used by inspection or enumeration methods.

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

    Directory of Open Access Journals (Sweden)

    Chris W Callaghan

    2016-06-01

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

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

    Science.gov (United States)

    Paramanandham, Nirmala; Rajendiran, Kishore

    2018-01-01

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

  15. Swarm intelligence inspired shills and the evolution of cooperation

    OpenAIRE

    Duan, Haibin; Sun, Changhao

    2014-01-01

    Many hostile scenarios exist in real-life situations, where cooperation is disfavored and the collective behavior needs intervention for system efficiency improvement. Towards this end, the framework of soft control provides a powerful tool by introducing controllable agents called shills, who are allowed to follow well-designed updating rules for varying missions. Inspired by swarm intelligence emerging from flocks of birds, we explore here the dependence of the evolution of cooperation on s...

  16. 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

  17. Quantum Behaved Particle Swarm Optimization Algorithm Based on Artificial Fish Swarm

    OpenAIRE

    Yumin, Dong; Li, Zhao

    2014-01-01

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

  18. Application of computational intelligence techniques for load shedding in power systems: A review

    International Nuclear Information System (INIS)

    Laghari, J.A.; Mokhlis, H.; Bakar, A.H.A.; Mohamad, Hasmaini

    2013-01-01

    Highlights: • The power system blackout history of last two decades is presented. • Conventional load shedding techniques, their types and limitations are presented. • Applications of intelligent techniques in load shedding are presented. • Intelligent techniques include ANN, fuzzy logic, ANFIS, genetic algorithm and PSO. • The discussion and comparison between these techniques are provided. - Abstract: Recent blackouts around the world question the reliability of conventional and adaptive load shedding techniques in avoiding such power outages. To address this issue, reliable techniques are required to provide fast and accurate load shedding to prevent collapse in the power system. Computational intelligence techniques, due to their robustness and flexibility in dealing with complex non-linear systems, could be an option in addressing this problem. Computational intelligence includes techniques like artificial neural networks, genetic algorithms, fuzzy logic control, adaptive neuro-fuzzy inference system, and particle swarm optimization. Research in these techniques is being undertaken in order to discover means for more efficient and reliable load shedding. This paper provides an overview of these techniques as applied to load shedding in a power system. This paper also compares the advantages of computational intelligence techniques over conventional load shedding techniques. Finally, this paper discusses the limitation of computational intelligence techniques, which restricts their usage in load shedding in real time

  19. 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...

  20. 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.

  1. 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.

  2. Hybrid Swarm Intelligence Optimization Approach for Optimal Data Storage Position Identification in Wireless Sensor Networks

    Science.gov (United States)

    Mohanasundaram, Ranganathan; Periasamy, Pappampalayam Sanmugam

    2015-01-01

    The current high profile debate with regard to data storage and its growth have become strategic task in the world of networking. It mainly depends on the sensor nodes called producers, base stations, and also the consumers (users and sensor nodes) to retrieve and use the data. The main concern dealt here is to find an optimal data storage position in wireless sensor networks. The works that have been carried out earlier did not utilize swarm intelligence based optimization approaches to find the optimal data storage positions. To achieve this goal, an efficient swam intelligence approach is used to choose suitable positions for a storage node. Thus, hybrid particle swarm optimization algorithm has been used to find the suitable positions for storage nodes while the total energy cost of data transmission is minimized. Clustering-based distributed data storage is utilized to solve clustering problem using fuzzy-C-means algorithm. This research work also considers the data rates and locations of multiple producers and consumers to find optimal data storage positions. The algorithm is implemented in a network simulator and the experimental results show that the proposed clustering and swarm intelligence based ODS strategy is more effective than the earlier approaches. PMID:25734182

  3. Scalable Clustering of High-Dimensional Data Technique Using SPCM with Ant Colony Optimization Intelligence

    Directory of Open Access Journals (Sweden)

    Thenmozhi Srinivasan

    2015-01-01

    Full Text Available Clusters of high-dimensional data techniques are emerging, according to data noisy and poor quality challenges. This paper has been developed to cluster data using high-dimensional similarity based PCM (SPCM, with ant colony optimization intelligence which is effective in clustering nonspatial data without getting knowledge about cluster number from the user. The PCM becomes similarity based by using mountain method with it. Though this is efficient clustering, it is checked for optimization using ant colony algorithm with swarm intelligence. Thus the scalable clustering technique is obtained and the evaluation results are checked with synthetic datasets.

  4. Swarm intelligence. A whole new way to think about business.

    Science.gov (United States)

    Bonabeau, E; Meyer, C

    2001-05-01

    What do ants and bees have to do with business? A great deal, it turns out. Individually, social insects are only minimally intelligent, and their work together is largely self-organized and unsupervised. Yet collectively they're capable of finding highly efficient solutions to difficult problems and can adapt automatically to changing environments. Over the past 20 years, the authors and other researchers have developed rigorous mathematical models to describe this phenomenon, which has been dubbed "swarm intelligence," and they are now applying them to business. Their research has already helped several companies develop more efficient ways to schedule factory equipment, divide tasks among workers, organize people, and even plot strategy. Emulating the way ants find the shortest path to a new food supply, for example, has led researchers at Hewlett-Packard to develop software programs that can find the most efficient way to route phone traffic over a telecommunications network. South-west Airlines has used a similar model to efficiently route cargo. To allocate labor, honeybees appear to follow one simple but powerful rule--they seem to specialize in a particular activity unless they perceive an important need to perform another function. Using that model, researchers at Northwestern University have devised a system for painting trucks that can automatically adapt to changing conditions. In the future, the authors speculate, a company might structure its entire business using the principles of swarm intelligence. The result, they believe, would be the ultimate self-organizing enterprise--one that could adapt quickly and instinctively to fast-changing markets.

  5. A long-term risk management tool for electricity markets using swarm intelligence

    International Nuclear Information System (INIS)

    Azevedo, F.; Vale, Z.A.; Khodr, H.M.; Oliveira, P.B. Moura

    2010-01-01

    This paper addresses the optimal involvement in derivatives electricity markets of a power producer to hedge against the pool price volatility. To achieve this aim, a swarm intelligence meta-heuristic optimization technique for long-term risk management tool is proposed. This tool investigates the long-term opportunities for risk hedging available for electric power producers through the use of contracts with physical (spot and forward contracts) and financial (options contracts) settlement. The producer risk preference is formulated as a utility function (U) expressing the trade-off between the expectation and the variance of the return. Variance of return and the expectation are based on a forecasted scenario interval determined by a long-term price range forecasting model. This model also makes use of particle swarm optimization (PSO) to find the best parameters allow to achieve better forecasting results. On the other hand, the price estimation depends on load forecasting. This work also presents a regressive long-term load forecast model that make use of PSO to find the best parameters as well as in price estimation. The PSO technique performance has been evaluated by comparison with a Genetic Algorithm (GA) based approach. A case study is presented and the results are discussed taking into account the real price and load historical data from mainland Spanish electricity market demonstrating the effectiveness of the methodology handling this type of problems. Finally, conclusions are dully drawn. (author)

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

    Directory of Open Access Journals (Sweden)

    Man Ding

    2010-01-01

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

  7. 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...

  8. Swarm Intelligence-Enhanced Detection of Non-Small-Cell Lung Cancer Using Tumor-Educated Platelets.

    Science.gov (United States)

    Best, Myron G; Sol, Nik; In 't Veld, Sjors G J G; Vancura, Adrienne; Muller, Mirte; Niemeijer, Anna-Larissa N; Fejes, Aniko V; Tjon Kon Fat, Lee-Ann; Huis In 't Veld, Anna E; Leurs, Cyra; Le Large, Tessa Y; Meijer, Laura L; Kooi, Irsan E; Rustenburg, François; Schellen, Pepijn; Verschueren, Heleen; Post, Edward; Wedekind, Laurine E; Bracht, Jillian; Esenkbrink, Michelle; Wils, Leon; Favaro, Francesca; Schoonhoven, Jilian D; Tannous, Jihane; Meijers-Heijboer, Hanne; Kazemier, Geert; Giovannetti, Elisa; Reijneveld, Jaap C; Idema, Sander; Killestein, Joep; Heger, Michal; de Jager, Saskia C; Urbanus, Rolf T; Hoefer, Imo E; Pasterkamp, Gerard; Mannhalter, Christine; Gomez-Arroyo, Jose; Bogaard, Harm-Jan; Noske, David P; Vandertop, W Peter; van den Broek, Daan; Ylstra, Bauke; Nilsson, R Jonas A; Wesseling, Pieter; Karachaliou, Niki; Rosell, Rafael; Lee-Lewandrowski, Elizabeth; Lewandrowski, Kent B; Tannous, Bakhos A; de Langen, Adrianus J; Smit, Egbert F; van den Heuvel, Michel M; Wurdinger, Thomas

    2017-08-14

    Blood-based liquid biopsies, including tumor-educated blood platelets (TEPs), have emerged as promising biomarker sources for non-invasive detection of cancer. Here we demonstrate that particle-swarm optimization (PSO)-enhanced algorithms enable efficient selection of RNA biomarker panels from platelet RNA-sequencing libraries (n = 779). This resulted in accurate TEP-based detection of early- and late-stage non-small-cell lung cancer (n = 518 late-stage validation cohort, accuracy, 88%; AUC, 0.94; 95% CI, 0.92-0.96; p swarm intelligence may also benefit the optimization of diagnostics readout of other liquid biopsy biosources. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

  9. 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

  10. Parameter estimation of Lorenz chaotic system using a hybrid swarm intelligence algorithm

    International Nuclear Information System (INIS)

    Lazzús, Juan A.; Rivera, Marco; López-Caraballo, Carlos H.

    2016-01-01

    A novel hybrid swarm intelligence algorithm for chaotic system parameter estimation is present. For this purpose, the parameters estimation on Lorenz systems is formulated as a multidimensional problem, and a hybrid approach based on particle swarm optimization with ant colony optimization (PSO–ACO) is implemented to solve this problem. Firstly, the performance of the proposed PSO–ACO algorithm is tested on a set of three representative benchmark functions, and the impact of the parameter settings on PSO–ACO efficiency is studied. Secondly, the parameter estimation is converted into an optimization problem on a three-dimensional Lorenz system. Numerical simulations on Lorenz model and comparisons with results obtained by other algorithms showed that PSO–ACO is a very powerful tool for parameter estimation with high accuracy and low deviations. - Highlights: • PSO–ACO combined particle swarm optimization with ant colony optimization. • This study is the first research of PSO–ACO to estimate parameters of chaotic systems. • PSO–ACO algorithm can identify the parameters of the three-dimensional Lorenz system with low deviations. • PSO–ACO is a very powerful tool for the parameter estimation on other chaotic system.

  11. Parameter estimation of Lorenz chaotic system using a hybrid swarm intelligence algorithm

    Energy Technology Data Exchange (ETDEWEB)

    Lazzús, Juan A., E-mail: jlazzus@dfuls.cl; Rivera, Marco; López-Caraballo, Carlos H.

    2016-03-11

    A novel hybrid swarm intelligence algorithm for chaotic system parameter estimation is present. For this purpose, the parameters estimation on Lorenz systems is formulated as a multidimensional problem, and a hybrid approach based on particle swarm optimization with ant colony optimization (PSO–ACO) is implemented to solve this problem. Firstly, the performance of the proposed PSO–ACO algorithm is tested on a set of three representative benchmark functions, and the impact of the parameter settings on PSO–ACO efficiency is studied. Secondly, the parameter estimation is converted into an optimization problem on a three-dimensional Lorenz system. Numerical simulations on Lorenz model and comparisons with results obtained by other algorithms showed that PSO–ACO is a very powerful tool for parameter estimation with high accuracy and low deviations. - Highlights: • PSO–ACO combined particle swarm optimization with ant colony optimization. • This study is the first research of PSO–ACO to estimate parameters of chaotic systems. • PSO–ACO algorithm can identify the parameters of the three-dimensional Lorenz system with low deviations. • PSO–ACO is a very powerful tool for the parameter estimation on other chaotic system.

  12. 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)

  13. 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)

  14. 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.

  15. Finite-element-model updating using computational intelligence techniques applications to structural dynamics

    CERN Document Server

    Marwala, Tshilidzi

    2010-01-01

    Finite element models (FEMs) are widely used to understand the dynamic behaviour of various systems. FEM updating allows FEMs to be tuned better to reflect measured data and may be conducted using two different statistical frameworks: the maximum likelihood approach and Bayesian approaches. Finite Element Model Updating Using Computational Intelligence Techniques applies both strategies to the field of structural mechanics, an area vital for aerospace, civil and mechanical engineering. Vibration data is used for the updating process. Following an introduction a number of computational intelligence techniques to facilitate the updating process are proposed; they include: • multi-layer perceptron neural networks for real-time FEM updating; • particle swarm and genetic-algorithm-based optimization methods to accommodate the demands of global versus local optimization models; • simulated annealing to put the methodologies into a sound statistical basis; and • response surface methods and expectation m...

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

    Directory of Open Access Journals (Sweden)

    Marco Aurelio Sotelo-Figueroa

    2014-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Jianzhou Wang

    2014-01-01

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

  18. 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

  19. 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.

  20. International Conference on Frontiers of Intelligent Computing : Theory and Applications

    CERN Document Server

    Udgata, Siba; Biswal, Bhabendra

    2014-01-01

    This volume contains the papers presented at the Second International Conference on Frontiers in Intelligent Computing: Theory and Applications (FICTA-2013) held during 14-16 November 2013 organized by Bhubaneswar Engineering College (BEC), Bhubaneswar, Odisha, India. It contains 63 papers focusing on application of intelligent techniques which includes evolutionary computation techniques like genetic algorithm, particle swarm optimization techniques, teaching-learning based optimization etc  for various engineering applications such as data mining, Fuzzy systems, Machine Intelligence and ANN, Web technologies and Multimedia applications and Intelligent computing and Networking etc.

  1. Optimizing bi-objective, multi-echelon supply chain model using particle swarm intelligence algorithm

    Science.gov (United States)

    Sathish Kumar, V. R.; Anbuudayasankar, S. P.; Rameshkumar, K.

    2018-02-01

    In the current globalized scenario, business organizations are more dependent on cost effective supply chain to enhance profitability and better handle competition. Demand uncertainty is an important factor in success or failure of a supply chain. An efficient supply chain limits the stock held at all echelons to the extent of avoiding a stock-out situation. In this paper, a three echelon supply chain model consisting of supplier, manufacturing plant and market is developed and the same is optimized using particle swarm intelligence algorithm.

  2. 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.

  3. 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.

  4. Intelligent Swarm Firefly Algorithm for the Prediction of China’s National Electricity Consumption

    OpenAIRE

    Zhang, Guangfeng; Chen, Yi; Yu, Yongnian; Wu, Shaomin

    2017-01-01

    China’s energy consumption is the world’s largest and is still rising, leading to concerns of energy shortage and environmental issues. It is, therefore, necessary to estimate the energy demand and to examine the dynamic nature of the electricity consumption. In this paper, we develop a nonlinear model of energy consumption and utilise a computational intelligence approach, specifcally a swarm frefly algorithm with a variable population, to examine China’s electricity consumption with historic...

  5. A Dynamic Recommender System for Improved Web Usage Mining and CRM Using Swarm Intelligence.

    Science.gov (United States)

    Alphy, Anna; Prabakaran, S

    2015-01-01

    In modern days, to enrich e-business, the websites are personalized for each user by understanding their interests and behavior. The main challenges of online usage data are information overload and their dynamic nature. In this paper, to address these issues, a WebBluegillRecom-annealing dynamic recommender system that uses web usage mining techniques in tandem with software agents developed for providing dynamic recommendations to users that can be used for customizing a website is proposed. The proposed WebBluegillRecom-annealing dynamic recommender uses swarm intelligence from the foraging behavior of a bluegill fish. It overcomes the information overload by handling dynamic behaviors of users. Our dynamic recommender system was compared against traditional collaborative filtering systems. The results show that the proposed system has higher precision, coverage, F1 measure, and scalability than the traditional collaborative filtering systems. Moreover, the recommendations given by our system overcome the overspecialization problem by including variety in recommendations.

  6. 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.

  7. 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.

  8. Support vector machine based diagnostic system for breast cancer using swarm intelligence.

    Science.gov (United States)

    Chen, Hui-Ling; Yang, Bo; Wang, Gang; Wang, Su-Jing; Liu, Jie; Liu, Da-You

    2012-08-01

    Breast cancer is becoming a leading cause of death among women in the whole world, meanwhile, it is confirmed that the early detection and accurate diagnosis of this disease can ensure a long survival of the patients. In this paper, a swarm intelligence technique based support vector machine classifier (PSO_SVM) is proposed for breast cancer diagnosis. In the proposed PSO-SVM, the issue of model selection and feature selection in SVM is simultaneously solved under particle swarm (PSO optimization) framework. A weighted function is adopted to design the objective function of PSO, which takes into account the average accuracy rates of SVM (ACC), the number of support vectors (SVs) and the selected features simultaneously. Furthermore, time varying acceleration coefficients (TVAC) and inertia weight (TVIW) are employed to efficiently control the local and global search in PSO algorithm. The effectiveness of PSO-SVM has been rigorously evaluated against the Wisconsin Breast Cancer Dataset (WBCD), which is commonly used among researchers who use machine learning methods for breast cancer diagnosis. The proposed system is compared with the grid search method with feature selection by F-score. The experimental results demonstrate that the proposed approach not only obtains much more appropriate model parameters and discriminative feature subset, but also needs smaller set of SVs for training, giving high predictive accuracy. In addition, Compared to the existing methods in previous studies, the proposed system can also be regarded as a promising success with the excellent classification accuracy of 99.3% via 10-fold cross validation (CV) analysis. Moreover, a combination of five informative features is identified, which might provide important insights to the nature of the breast cancer disease and give an important clue for the physicians to take a closer attention. We believe the promising result can ensure that the physicians make very accurate diagnostic decision in

  9. 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.

  10. 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.

  11. Residential Consumer-Centric Demand-Side Management Based on Energy Disaggregation-Piloting Constrained Swarm Intelligence: Towards Edge Computing.

    Science.gov (United States)

    Lin, Yu-Hsiu; Hu, Yu-Chen

    2018-04-27

    The emergence of smart Internet of Things (IoT) devices has highly favored the realization of smart homes in a down-stream sector of a smart grid. The underlying objective of Demand Response (DR) schemes is to actively engage customers to modify their energy consumption on domestic appliances in response to pricing signals. Domestic appliance scheduling is widely accepted as an effective mechanism to manage domestic energy consumption intelligently. Besides, to residential customers for DR implementation, maintaining a balance between energy consumption cost and users’ comfort satisfaction is a challenge. Hence, in this paper, a constrained Particle Swarm Optimization (PSO)-based residential consumer-centric load-scheduling method is proposed. The method can be further featured with edge computing. In contrast with cloud computing, edge computing—a method of optimizing cloud computing technologies by driving computing capabilities at the IoT edge of the Internet as one of the emerging trends in engineering technology—addresses bandwidth-intensive contents and latency-sensitive applications required among sensors and central data centers through data analytics at or near the source of data. A non-intrusive load-monitoring technique proposed previously is utilized to automatic determination of physical characteristics of power-intensive home appliances from users’ life patterns. The swarm intelligence, constrained PSO, is used to minimize the energy consumption cost while considering users’ comfort satisfaction for DR implementation. The residential consumer-centric load-scheduling method proposed in this paper is evaluated under real-time pricing with inclining block rates and is demonstrated in a case study. The experimentation reported in this paper shows the proposed residential consumer-centric load-scheduling method can re-shape loads by home appliances in response to DR signals. Moreover, a phenomenal reduction in peak power consumption is achieved

  12. Residential Consumer-Centric Demand-Side Management Based on Energy Disaggregation-Piloting Constrained Swarm Intelligence: Towards Edge Computing

    Science.gov (United States)

    Hu, Yu-Chen

    2018-01-01

    The emergence of smart Internet of Things (IoT) devices has highly favored the realization of smart homes in a down-stream sector of a smart grid. The underlying objective of Demand Response (DR) schemes is to actively engage customers to modify their energy consumption on domestic appliances in response to pricing signals. Domestic appliance scheduling is widely accepted as an effective mechanism to manage domestic energy consumption intelligently. Besides, to residential customers for DR implementation, maintaining a balance between energy consumption cost and users’ comfort satisfaction is a challenge. Hence, in this paper, a constrained Particle Swarm Optimization (PSO)-based residential consumer-centric load-scheduling method is proposed. The method can be further featured with edge computing. In contrast with cloud computing, edge computing—a method of optimizing cloud computing technologies by driving computing capabilities at the IoT edge of the Internet as one of the emerging trends in engineering technology—addresses bandwidth-intensive contents and latency-sensitive applications required among sensors and central data centers through data analytics at or near the source of data. A non-intrusive load-monitoring technique proposed previously is utilized to automatic determination of physical characteristics of power-intensive home appliances from users’ life patterns. The swarm intelligence, constrained PSO, is used to minimize the energy consumption cost while considering users’ comfort satisfaction for DR implementation. The residential consumer-centric load-scheduling method proposed in this paper is evaluated under real-time pricing with inclining block rates and is demonstrated in a case study. The experimentation reported in this paper shows the proposed residential consumer-centric load-scheduling method can re-shape loads by home appliances in response to DR signals. Moreover, a phenomenal reduction in peak power consumption is achieved

  13. Residential Consumer-Centric Demand-Side Management Based on Energy Disaggregation-Piloting Constrained Swarm Intelligence: Towards Edge Computing

    Directory of Open Access Journals (Sweden)

    Yu-Hsiu Lin

    2018-04-01

    Full Text Available The emergence of smart Internet of Things (IoT devices has highly favored the realization of smart homes in a down-stream sector of a smart grid. The underlying objective of Demand Response (DR schemes is to actively engage customers to modify their energy consumption on domestic appliances in response to pricing signals. Domestic appliance scheduling is widely accepted as an effective mechanism to manage domestic energy consumption intelligently. Besides, to residential customers for DR implementation, maintaining a balance between energy consumption cost and users’ comfort satisfaction is a challenge. Hence, in this paper, a constrained Particle Swarm Optimization (PSO-based residential consumer-centric load-scheduling method is proposed. The method can be further featured with edge computing. In contrast with cloud computing, edge computing—a method of optimizing cloud computing technologies by driving computing capabilities at the IoT edge of the Internet as one of the emerging trends in engineering technology—addresses bandwidth-intensive contents and latency-sensitive applications required among sensors and central data centers through data analytics at or near the source of data. A non-intrusive load-monitoring technique proposed previously is utilized to automatic determination of physical characteristics of power-intensive home appliances from users’ life patterns. The swarm intelligence, constrained PSO, is used to minimize the energy consumption cost while considering users’ comfort satisfaction for DR implementation. The residential consumer-centric load-scheduling method proposed in this paper is evaluated under real-time pricing with inclining block rates and is demonstrated in a case study. The experimentation reported in this paper shows the proposed residential consumer-centric load-scheduling method can re-shape loads by home appliances in response to DR signals. Moreover, a phenomenal reduction in peak power

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

    Directory of Open Access Journals (Sweden)

    Xiaomin Xu

    2015-01-01

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

  15. Application of Swarm Intelligence Based Routingprotocols for Wireless Adhoc Sensor Network

    Directory of Open Access Journals (Sweden)

    Mrutyunjaya PANDA

    2011-07-01

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

  16. A Game Theoretic Approach to Swarm Robotics

    Directory of Open Access Journals (Sweden)

    S. N. Givigi

    2006-01-01

    Full Text Available In this article, we discuss some techniques for achieving swarm intelligent robots through the use of traits of personality. Traits of personality are characteristics of each robot that, altogether, define the robot's behaviours. We discuss the use of evolutionary psychology to select a set of traits of personality that will evolve due to a learning process based on reinforcement learning. The use of Game Theory is introduced, and some simulations showing its potential are reported.

  17. Hybrid Swarm Intelligence Energy Efficient Clustered Routing Algorithm for Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Rajeev Kumar

    2016-01-01

    Full Text Available Currently, wireless sensor networks (WSNs are used in many applications, namely, environment monitoring, disaster management, industrial automation, and medical electronics. Sensor nodes carry many limitations like low battery life, small memory space, and limited computing capability. To create a wireless sensor network more energy efficient, swarm intelligence technique has been applied to resolve many optimization issues in WSNs. In many existing clustering techniques an artificial bee colony (ABC algorithm is utilized to collect information from the field periodically. Nevertheless, in the event based applications, an ant colony optimization (ACO is a good solution to enhance the network lifespan. In this paper, we combine both algorithms (i.e., ABC and ACO and propose a new hybrid ABCACO algorithm to solve a Nondeterministic Polynomial (NP hard and finite problem of WSNs. ABCACO algorithm is divided into three main parts: (i selection of optimal number of subregions and further subregion parts, (ii cluster head selection using ABC algorithm, and (iii efficient data transmission using ACO algorithm. We use a hierarchical clustering technique for data transmission; the data is transmitted from member nodes to the subcluster heads and then from subcluster heads to the elected cluster heads based on some threshold value. Cluster heads use an ACO algorithm to discover the best route for data transmission to the base station (BS. The proposed approach is very useful in designing the framework for forest fire detection and monitoring. The simulation results show that the ABCACO algorithm enhances the stability period by 60% and also improves the goodput by 31% against LEACH and WSNCABC, respectively.

  18. 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".

  19. 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.

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

    International Nuclear Information System (INIS)

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

    2015-01-01

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

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2015-11-15

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

  2. 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.

  3. Optimization of Transformation Coefficients Using Direct Search and Swarm Intelligence

    Directory of Open Access Journals (Sweden)

    Manusov V.Z.

    2017-04-01

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

  4. Celestial Navigation Fix Based on Particle Swarm Optimization

    Directory of Open Access Journals (Sweden)

    Tsou Ming-Cheng

    2015-09-01

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

  5. International Conference on Frontiers of Intelligent Computing : Theory and Applications

    CERN Document Server

    Udgata, Siba; Biswal, Bhabendra

    2013-01-01

    The volume contains the papers presented at FICTA 2012: International Conference on Frontiers in Intelligent Computing: Theory and Applications held on December 22-23, 2012 in Bhubaneswar engineering College, Bhubaneswar, Odissa, India. It contains 86 papers contributed by authors from the globe. These research papers mainly focused on application of intelligent techniques which includes evolutionary computation techniques like genetic algorithm, particle swarm optimization techniques, teaching-learning based optimization etc  for various engineering applications such as data mining, image processing, cloud computing, networking etc.

  6. Improved Cat Swarm Optimization for Simultaneous Allocation of DSTATCOM and DGs in Distribution Systems

    Directory of Open Access Journals (Sweden)

    Neeraj Kanwar

    2015-01-01

    Full Text Available This paper addresses a new methodology for the simultaneous optimal allocation of DSTATCOM and DG in radial distribution systems to maximize power loss reduction while maintaining better node voltage profiles under multilevel load profile. Cat Swarm Optimization (CSO is one of the recently developed powerful swarm intelligence-based optimization techniques that mimics the natural behavior of cats but usually suffers from poor convergence and accuracy while subjected to large dimension problem. Therefore, an Improved CSO (ICSO technique is proposed to efficiently solve the problem where the seeking mode of CSO is modified to enhance its exploitation potential. In addition, the problem search space is virtually squeezed by suggesting an intelligent search approach which smartly scans the problem search space. Further, the effect of network reconfiguration has also been investigated after optimally placing DSTATCOMs and DGs in the distribution network. The suggested measures enhance the convergence and accuracy of the algorithm without loss of diversity. The proposed method is investigated on 69-bus test distribution system and the application results are very promising for the operation of smart distribution systems.

  7. 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

  8. A quantum particle swarm optimizer with chaotic mutation operator

    International Nuclear Information System (INIS)

    Coelho, Leandro dos Santos

    2008-01-01

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

  9. Four-dimensional parameter estimation of plane waves using swarming intelligence

    International Nuclear Information System (INIS)

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

    2014-01-01

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

  10. 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.

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

    Directory of Open Access Journals (Sweden)

    Evans BAIDOO

    2016-12-01

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

  12. Application of Advanced Particle Swarm Optimization Techniques to Wind-thermal Coordination

    DEFF Research Database (Denmark)

    Singh, Sri Niwas; Østergaard, Jacob; Yadagiri, J.

    2009-01-01

    wind-thermal coordination algorithm is necessary to determine the optimal proportion of wind and thermal generator capacity that can be integrated into the system. In this paper, four versions of Particle Swarm Optimization (PSO) techniques are proposed for solving wind-thermal coordination problem...

  13. Application of ant colony Algorithm and particle swarm optimization in architectural design

    Science.gov (United States)

    Song, Ziyi; Wu, Yunfa; Song, Jianhua

    2018-02-01

    By studying the development of ant colony algorithm and particle swarm algorithm, this paper expounds the core idea of the algorithm, explores the combination of algorithm and architectural design, sums up the application rules of intelligent algorithm in architectural design, and combines the characteristics of the two algorithms, obtains the research route and realization way of intelligent algorithm in architecture design. To establish algorithm rules to assist architectural design. Taking intelligent algorithm as the beginning of architectural design research, the authors provide the theory foundation of ant colony Algorithm and particle swarm algorithm in architectural design, popularize the application range of intelligent algorithm in architectural design, and provide a new idea for the architects.

  14. 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

  15. Business and Social Behaviour Intelligence Analysis Using PSO

    OpenAIRE

    Vinay S Bhaskar; Abhishek Kumar Singh; Jyoti Dhruw; Anubha Parashar; Mradula Sharma

    2014-01-01

    The goal of this paper is to elaborate swarm intelligence for business intelligence decision making and the business rules management improvement. The paper introduces the decision making model which is based on the application of Artificial Neural Networks (ANNs) and Particle Swarm Optimization (PSO) algorithm. Essentially the business spatial data illustrate the group behaviors. The swarm optimization, which is highly influenced by the behavior of creature, performs in group. The Spatial dat...

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

    Directory of Open Access Journals (Sweden)

    Kazem Mohammadi- Aghdam

    2015-10-01

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

  17. 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

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

    International Nuclear Information System (INIS)

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

    2017-01-01

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

  19. Artificial intelligence techniques in Prolog

    CERN Document Server

    Shoham, Yoav

    1993-01-01

    Artificial Intelligence Techniques in Prolog introduces the reader to the use of well-established algorithmic techniques in the field of artificial intelligence (AI), with Prolog as the implementation language. The techniques considered cover general areas such as search, rule-based systems, and truth maintenance, as well as constraint satisfaction and uncertainty management. Specific application domains such as temporal reasoning, machine learning, and natural language are also discussed.Comprised of 10 chapters, this book begins with an overview of Prolog, paying particular attention to Prol

  20. Glowworm swarm optimization theory, algorithms, and applications

    CERN Document Server

    Kaipa, Krishnanand N

    2017-01-01

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

  1. Generating a Multiphase Equation of State with Swarm Intelligence

    Science.gov (United States)

    Cox, Geoffrey

    2017-06-01

    Hydrocode calculations require knowledge of the variation of pressure of a material with density and temperature, which is given by the equation of state. An accurate model needs to account for discontinuities in energy, density and properties of a material across a phase boundary. When generating a multiphase equation of state the modeller attempts to balance the agreement between the available data for compression, expansion and phase boundary location. However, this can prove difficult because minor adjustments in the equation of state for a single phase can have a large impact on the overall phase diagram. Recently, Cox and Christie described a method for combining statistical-mechanics-based condensed matter physics models with a stochastic analysis technique called particle swarm optimisation. The models produced show good agreement with experiment over a wide range of pressure-temperature space. This talk details the general implementation of this technique, shows example results, and describes the types of analysis that can be performed with this method.

  2. A novel e-shape communication antenna design using particle swarm optimization (PSO)

    Science.gov (United States)

    Mohanageetha, D.; Pavithra, R.

    2013-01-01

    An E-shape patch antenna is designed and demonstrated their effectiveness using Particle Swarm Optimization (PSO), which is used for wireless applications. The concept of PSO is briefly introduced in the design procedure and the design parameters are explained. This work focuses on identifying the increasing popularity of swarm intelligence specifically among the electromagnetic community. It is implemented using PSO combined with numerical algorithms for electromagnetic solutions, such as the Finite Element Method (FEM) and the Method of Moments (MOM). In both the realizations, the PSO technique drives the design variables such as antenna dimensions and geometrical features. The fitness function is evaluated for the optimizer. This is achieved by using CAD FEKO 6.1, electromagnetic simulation software. The model is designed with a resonant frequency of 2.65GHz.

  3. 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.

  4. 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.

  5. Computing Nash equilibria through computational intelligence methods

    Science.gov (United States)

    Pavlidis, N. G.; Parsopoulos, K. E.; Vrahatis, M. N.

    2005-03-01

    Nash equilibrium constitutes a central solution concept in game theory. The task of detecting the Nash equilibria of a finite strategic game remains a challenging problem up-to-date. This paper investigates the effectiveness of three computational intelligence techniques, namely, covariance matrix adaptation evolution strategies, particle swarm optimization, as well as, differential evolution, to compute Nash equilibria of finite strategic games, as global minima of a real-valued, nonnegative function. An issue of particular interest is to detect more than one Nash equilibria of a game. The performance of the considered computational intelligence methods on this problem is investigated using multistart and deflection.

  6. Cooperative Behaviours with Swarm Intelligence in Multirobot Systems for Safety Inspections in Underground Terrains

    Directory of Open Access Journals (Sweden)

    Chika Yinka-Banjo

    2014-01-01

    Full Text Available Underground mining operations are carried out in hazardous environments. To prevent disasters from occurring, as often as they do in underground mines, and to prevent safety routine checkers from disasters during safety inspection checks, multirobots are suggested to do the job of safety inspection rather than human beings and single robots. Multirobots are preferred because the inspection task will be done in the minimum amount of time. This paper proposes a cooperative behaviour for a multirobot system (MRS to achieve a preentry safety inspection in underground terrains. A hybrid QLACS swarm intelligent model based on Q-Learning (QL and the Ant Colony System (ACS was proposed to achieve this cooperative behaviour in MRS. The intelligent model was developed by harnessing the strengths of both QL and ACS algorithms. The ACS optimizes the routes used for each robot while the QL algorithm enhances the cooperation between the autonomous robots. A description of a communicating variation within the QLACS model for cooperative behavioural purposes is presented. The performance of the algorithms in terms of without communication, with communication, computation time, path costs, and the number of robots used was evaluated by using a simulation approach. Simulation results show achieved cooperative behaviour between robots.

  7. 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.

  8. 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.

  9. 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

  10. Adaptive Remote-Sensing Techniques Implementing Swarms of Mobile Agents

    Energy Technology Data Exchange (ETDEWEB)

    Asher, R.B.; Cameron, S.M.; Loubriel, G.M.; Robinett, R.D.; Stantz, K.M.; Trahan, M.W.; Wagner, J.S.

    1998-11-25

    In many situations, stand-off remote-sensing and hazard-interdiction techniques over realistic operational areas are often impractical "and difficult to characterize. An alternative approach is to implement an adap- tively deployable array of sensitive agent-specific devices. Our group has been studying the collective be- havior of an autonomous, multi-agent system applied to chedbio detection and related emerging threat applications, The current physics-based models we are using coordinate a sensor array for mukivanate sig- nal optimization and coverage as re,alized by a swarm of robots or mobile vehicles. These intelligent control systems integrate'glob"ally operating decision-making systems and locally cooperative learning neural net- works to enhance re+-timp operational responses to dynarnical environments examples of which include obstacle avoidance, res~onding to prevailing wind patterns, and overcoming other natural obscurants or in- terferences. Collectively',tkensor nefirons with simple properties, interacting according to basic community rules, can accomplish complex interconnecting functions such as generalization, error correction, pattern recognition, sensor fusion, and localization. Neural nets provide a greater degree of robusmess and fault tolerance than conventional systems in that minor variations or imperfections do not impair performance. The robotic platforms would be equipped with sensor devices that perform opticaI detection of biologicais in combination with multivariate chemical analysis tools based on genetic and neural network algorithms, laser-diode LIDAR analysis, ultra-wideband short-pulsed transmitting and receiving antennas, thermal im- a:ing sensors, and optical Communication technology providing robust data throughput pathways. Mission scenarios under consideration include ground penetrating radar (GPR) for detection of underground struc- tures, airborne systems, and plume migration and mitigation. We will describe our

  11. Memory Based Machine Intelligence Techniques in VLSI hardware

    OpenAIRE

    James, Alex Pappachen

    2012-01-01

    We briefly introduce the memory based approaches to emulate machine intelligence in VLSI hardware, describing the challenges and advantages. Implementation of artificial intelligence techniques in VLSI hardware is a practical and difficult problem. Deep architectures, hierarchical temporal memories and memory networks are some of the contemporary approaches in this area of research. The techniques attempt to emulate low level intelligence tasks and aim at providing scalable solutions to high ...

  12. USE OF ARTIFICIAL INTELLIGENCE TECHNIQUES IN QUALITY IMPROVING PROCESS

    OpenAIRE

    KALİTE İYİLEŞTİRME SÜRECİNDE YAPAY ZEKÃ KAYA; Orhan ENGİN

    2005-01-01

    Today, changing of competition conditions and customer preferences caused to happen many differences in the viewpoint of firms' quality studies. At the same time, improvements in computer technologies accelerated use of artificial intelligence. Artificial intelligence technologies are being used to solve many industry problems. In this paper, we investigated the use of artificial intelligence techniques to solve quality problems. The artificial intelligence techniques, which are used in quali...

  13. A decoupled power flow algorithm using particle swarm optimization technique

    International Nuclear Information System (INIS)

    Acharjee, P.; Goswami, S.K.

    2009-01-01

    A robust, nondivergent power flow method has been developed using the particle swarm optimization (PSO) technique. The decoupling properties between the power system quantities have been exploited in developing the power flow algorithm. The speed of the power flow algorithm has been improved using a simple perturbation technique. The basic power flow algorithm and the improvement scheme have been designed to retain the simplicity of the evolutionary approach. The power flow is rugged, can determine the critical loading conditions and also can handle the flexible alternating current transmission system (FACTS) devices efficiently. Test results on standard test systems show that the proposed method can find the solution when the standard power flows fail.

  14. 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.

  15. Artificial Intelligence Techniques and Methodology

    OpenAIRE

    Carbonell, Jaime G.; Sleeman, Derek

    1982-01-01

    Two closely related aspects of artificial intelligence that have received comparatively little attention in the recent literature are research methodology, and the analysis of computational techniques that span multiple application areas. We believe both issues to be increasingly significant as Artificial Intelligence matures into a science and spins off major application efforts. It is imperative to analyze the repertoire of AI methods with respect to past experience, utility in new domains,...

  16. Optimal sensor placement for large structures using the nearest neighbour index and a hybrid swarm intelligence algorithm

    International Nuclear Information System (INIS)

    Lian, Jijian; He, Longjun; Ma, Bin; Peng, Wenxiang; Li, Huokun

    2013-01-01

    Research on optimal sensor placement (OSP) has become very important due to the need to obtain effective testing results with limited testing resources in health monitoring. In this study, a new methodology is proposed to select the best sensor locations for large structures. First, a novel fitness function derived from the nearest neighbour index is proposed to overcome the drawbacks of the effective independence method for OSP for large structures. This method maximizes the contribution of each sensor to modal observability and simultaneously avoids the redundancy of information between the selected degrees of freedom. A hybrid algorithm combining the improved discrete particle swarm optimization (DPSO) with the clonal selection algorithm is then implemented to optimize the proposed fitness function effectively. Finally, the proposed method is applied to an arch dam for performance verification. The results show that the proposed hybrid swarm intelligence algorithm outperforms a genetic algorithm with decimal two-dimension array encoding and DPSO in the capability of global optimization. The new fitness function is advantageous in terms of sensor distribution and ensuring a well-conditioned information matrix and orthogonality of modes, indicating that this method may be used to provide guidance for OSP in various large structures. (paper)

  17. 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

  18. A new inertia weight control strategy for particle swarm optimization

    Science.gov (United States)

    Zhu, Xianming; Wang, Hongbo

    2018-04-01

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

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

    Science.gov (United States)

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

    2014-06-01

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

  20. Frameworks for Performing on Cloud Automated Software Testing Using Swarm Intelligence Algorithm: Brief Survey

    Directory of Open Access Journals (Sweden)

    Mohammad Hossain

    2018-04-01

    Full Text Available This paper surveys on Cloud Based Automated Testing Software that is able to perform Black-box testing, White-box testing, as well as Unit and Integration Testing as a whole. In this paper, we discuss few of the available automated software testing frameworks on the cloud. These frameworks are found to be more efficient and cost effective because they execute test suites over a distributed cloud infrastructure. One of the framework effectiveness was attributed to having a module that accepts manual test cases from users and it prioritize them accordingly. Software testing, in general, accounts for as much as 50% of the total efforts of the software development project. To lessen the efforts, one the frameworks discussed in this paper used swarm intelligence algorithms. It uses the Ant Colony Algorithm for complete path coverage to minimize time and the Bee Colony Optimization (BCO for regression testing to ensure backward compatibility.

  1. Intelligent techniques in engineering management theory and applications

    CERN Document Server

    Onar, Sezi

    2015-01-01

    This book presents recently developed intelligent techniques with applications and theory in the area of engineering management. The involved applications of intelligent techniques such as neural networks, fuzzy sets, Tabu search, genetic algorithms, etc. will be useful for engineering managers, postgraduate students, researchers, and lecturers. The book has been written considering the contents of a classical engineering management book but intelligent techniques are used for handling the engineering management problem areas. This comprehensive characteristics of the book makes it an excellent reference for the solution of complex problems of engineering management. The authors of the chapters are well-known researchers with their previous works in the area of engineering management.

  2. A Robotic Swarm for Spill Finding and Perimeter Formation

    National Research Council Canada - National Science Library

    Bruemmer, David J; Dudenhoeffer, Donald D; McKay, Mark D; Anderson, Matthew O

    2002-01-01

    ... intelligence as seen in a colony of ants or swarm of bees. A suite of C2 tools, AgentTools, has been developed to enable an operator to inject high-level domain knowledge and guidance into the behavior of the otherwise autonomous robots...

  3. Elephant swarm water search algorithm for global optimization

    Indian Academy of Sciences (India)

    S Mandal

    2018-02-07

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

  4. Construction of Gene Regulatory Networks Using Recurrent Neural Networks and Swarm Intelligence.

    Science.gov (United States)

    Khan, Abhinandan; Mandal, Sudip; Pal, Rajat Kumar; Saha, Goutam

    2016-01-01

    We have proposed a methodology for the reverse engineering of biologically plausible gene regulatory networks from temporal genetic expression data. We have used established information and the fundamental mathematical theory for this purpose. We have employed the Recurrent Neural Network formalism to extract the underlying dynamics present in the time series expression data accurately. We have introduced a new hybrid swarm intelligence framework for the accurate training of the model parameters. The proposed methodology has been first applied to a small artificial network, and the results obtained suggest that it can produce the best results available in the contemporary literature, to the best of our knowledge. Subsequently, we have implemented our proposed framework on experimental (in vivo) datasets. Finally, we have investigated two medium sized genetic networks (in silico) extracted from GeneNetWeaver, to understand how the proposed algorithm scales up with network size. Additionally, we have implemented our proposed algorithm with half the number of time points. The results indicate that a reduction of 50% in the number of time points does not have an effect on the accuracy of the proposed methodology significantly, with a maximum of just over 15% deterioration in the worst case.

  5. Conformational sampling enhancement of replica exchange molecular dynamics simulations using swarm particle intelligence

    International Nuclear Information System (INIS)

    Kamberaj, Hiqmet

    2015-01-01

    In this paper, we present a new method based on swarm particle social intelligence for use in replica exchange molecular dynamics simulations. In this method, the replicas (representing the different system configurations) are allowed communicating with each other through the individual and social knowledge, in additional to considering them as a collection of real particles interacting through the Newtonian forces. The new method is based on the modification of the equations of motion in such way that the replicas are driven towards the global energy minimum. The method was tested for the Lennard-Jones clusters of N = 4,  5, and 6 atoms. Our results showed that the new method is more efficient than the conventional replica exchange method under the same practical conditions. In particular, the new method performed better on optimizing the distribution of the replicas among the thermostats with time and, in addition, ergodic convergence is observed to be faster. We also introduce a weighted histogram analysis method allowing analyzing the data from simulations by combining data from all of the replicas and rigorously removing the inserted bias

  6. Computational intelligence techniques in health care

    CERN Document Server

    Zhou, Wengang; Satheesh, P

    2016-01-01

    This book presents research on emerging computational intelligence techniques and tools, with a particular focus on new trends and applications in health care. Healthcare is a multi-faceted domain, which incorporates advanced decision-making, remote monitoring, healthcare logistics, operational excellence and modern information systems. In recent years, the use of computational intelligence methods to address the scale and the complexity of the problems in healthcare has been investigated. This book discusses various computational intelligence methods that are implemented in applications in different areas of healthcare. It includes contributions by practitioners, technology developers and solution providers.

  7. Particle swarm optimization of a neural network model in a ...

    Indian Academy of Sciences (India)

    . Since tool life is critically affected by the tool wear, accurate prediction of this wear ... In their work, they established an improvement in the quality ... objective optimization of hard turning using neural network modelling and swarm intelligence ...

  8. 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

  9. 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.

  10. 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.

  11. THE COMPUTATIONAL INTELLIGENCE TECHNIQUES FOR PREDICTIONS - ARTIFICIAL NEURAL NETWORKS

    OpenAIRE

    Mary Violeta Bar

    2014-01-01

    The computational intelligence techniques are used in problems which can not be solved by traditional techniques when there is insufficient data to develop a model problem or when they have errors.Computational intelligence, as he called Bezdek (Bezdek, 1992) aims at modeling of biological intelligence. Artificial Neural Networks( ANNs) have been applied to an increasing number of real world problems of considerable complexity. Their most important advantage is solving problems that are too c...

  12. Improving Energy Saving Techniques by Ambient Intelligence Scheduling

    DEFF Research Database (Denmark)

    Cristani, Matteo; Karafili, Erisa; Tomazzoli, Claudio

    2015-01-01

    Energy saving is one of the most challenging aspects of modern ambient intelligence technologies, for both domestic and business usages. In this paper we show how to combine Ambient Intelligence and Artificial Intelligence techniques to solve the problem of scheduling a set of devices under a given...... for Ambient Intelligence to a specific framework and exhibit a sample usage for a real life system, Elettra, that is in use in an industrial context....

  13. Triaxial Accelerometer Error Coefficients Identification with a Novel Artificial Fish Swarm Algorithm

    Directory of Open Access Journals (Sweden)

    Yanbin Gao

    2015-01-01

    Full Text Available Artificial fish swarm algorithm (AFSA is one of the state-of-the-art swarm intelligence techniques, which is widely utilized for optimization purposes. Triaxial accelerometer error coefficients are relatively unstable with the environmental disturbances and aging of the instrument. Therefore, identifying triaxial accelerometer error coefficients accurately and being with lower costs are of great importance to improve the overall performance of triaxial accelerometer-based strapdown inertial navigation system (SINS. In this study, a novel artificial fish swarm algorithm (NAFSA that eliminated the demerits (lack of using artificial fishes’ previous experiences, lack of existing balance between exploration and exploitation, and high computational cost of AFSA is introduced at first. In NAFSA, functional behaviors and overall procedure of AFSA have been improved with some parameters variations. Second, a hybrid accelerometer error coefficients identification algorithm has been proposed based on NAFSA and Monte Carlo simulation (MCS approaches. This combination leads to maximum utilization of the involved approaches for triaxial accelerometer error coefficients identification. Furthermore, the NAFSA-identified coefficients are testified with 24-position verification experiment and triaxial accelerometer-based SINS navigation experiment. The priorities of MCS-NAFSA are compared with that of conventional calibration method and optimal AFSA. Finally, both experiments results demonstrate high efficiency of MCS-NAFSA on triaxial accelerometer error coefficients identification.

  14. E-learning systems intelligent techniques for personalization

    CERN Document Server

    Klašnja-Milićević, Aleksandra; Ivanović, Mirjana; Budimac, Zoran; Jain, Lakhmi C

    2017-01-01

    This monograph provides a comprehensive research review of intelligent techniques for personalisation of e-learning systems. Special emphasis is given to intelligent tutoring systems as a particular class of e-learning systems, which support and improve the learning and teaching of domain-specific knowledge. A new approach to perform effective personalization based on Semantic web technologies achieved in a tutoring system is presented. This approach incorporates a recommender system based on collaborative tagging techniques that adapts to the interests and level of students' knowledge. These innovations are important contributions of this monograph. Theoretical models and techniques are illustrated on a real personalised tutoring system for teaching Java programming language. The monograph is directed to, students and researchers interested in the e-learning and personalization techniques. .

  15. APPLYING ARTIFICIAL INTELLIGENCE TECHNIQUES TO HUMAN-COMPUTER INTERFACES

    DEFF Research Database (Denmark)

    Sonnenwald, Diane H.

    1988-01-01

    A description is given of UIMS (User Interface Management System), a system using a variety of artificial intelligence techniques to build knowledge-based user interfaces combining functionality and information from a variety of computer systems that maintain, test, and configure customer telephone...... and data networks. Three artificial intelligence (AI) techniques used in UIMS are discussed, namely, frame representation, object-oriented programming languages, and rule-based systems. The UIMS architecture is presented, and the structure of the UIMS is explained in terms of the AI techniques....

  16. An adaptive image enhancement technique by combining cuckoo search and particle swarm optimization algorithm.

    Science.gov (United States)

    Ye, Zhiwei; Wang, Mingwei; Hu, Zhengbing; Liu, Wei

    2015-01-01

    Image enhancement is an important procedure of image processing and analysis. This paper presents a new technique using a modified measure and blending of cuckoo search and particle swarm optimization (CS-PSO) for low contrast images to enhance image adaptively. In this way, contrast enhancement is obtained by global transformation of the input intensities; it employs incomplete Beta function as the transformation function and a novel criterion for measuring image quality considering three factors which are threshold, entropy value, and gray-level probability density of the image. The enhancement process is a nonlinear optimization problem with several constraints. CS-PSO is utilized to maximize the objective fitness criterion in order to enhance the contrast and detail in an image by adapting the parameters of a novel extension to a local enhancement technique. The performance of the proposed method has been compared with other existing techniques such as linear contrast stretching, histogram equalization, and evolutionary computing based image enhancement methods like backtracking search algorithm, differential search algorithm, genetic algorithm, and particle swarm optimization in terms of processing time and image quality. Experimental results demonstrate that the proposed method is robust and adaptive and exhibits the better performance than other methods involved in the paper.

  17. An Adaptive Image Enhancement Technique by Combining Cuckoo Search and Particle Swarm Optimization Algorithm

    Directory of Open Access Journals (Sweden)

    Zhiwei Ye

    2015-01-01

    Full Text Available Image enhancement is an important procedure of image processing and analysis. This paper presents a new technique using a modified measure and blending of cuckoo search and particle swarm optimization (CS-PSO for low contrast images to enhance image adaptively. In this way, contrast enhancement is obtained by global transformation of the input intensities; it employs incomplete Beta function as the transformation function and a novel criterion for measuring image quality considering three factors which are threshold, entropy value, and gray-level probability density of the image. The enhancement process is a nonlinear optimization problem with several constraints. CS-PSO is utilized to maximize the objective fitness criterion in order to enhance the contrast and detail in an image by adapting the parameters of a novel extension to a local enhancement technique. The performance of the proposed method has been compared with other existing techniques such as linear contrast stretching, histogram equalization, and evolutionary computing based image enhancement methods like backtracking search algorithm, differential search algorithm, genetic algorithm, and particle swarm optimization in terms of processing time and image quality. Experimental results demonstrate that the proposed method is robust and adaptive and exhibits the better performance than other methods involved in the paper.

  18. Location Prediction-Based Data Dissemination Using Swarm Intelligence in Opportunistic Cognitive Networks

    Directory of Open Access Journals (Sweden)

    Jie Li

    2014-01-01

    Full Text Available Swarm intelligence is widely used in the application of communication networks. In this paper we adopt a biologically inspired strategy to investigate the data dissemination problem in the opportunistic cognitive networks (OCNs. We model the system as a centralized and distributed hybrid system including a location prediction server and a pervasive environment deploying the large-scale human-centric devices. To exploit such environment, data gathering and dissemination are fundamentally based on the contact opportunities. To tackle the lack of contemporaneous end-to-end connectivity in opportunistic networks, we apply ant colony optimization as a cognitive heuristic technology to formulate a self-adaptive dissemination-based routing scheme in opportunistic cognitive networks. This routing strategy has attempted to find the most appropriate nodes conveying messages to the destination node based on the location prediction information and intimacy between nodes, which uses the online unsupervised learning on geographical locations and the biologically inspired algorithm on the relationship of nodes to estimate the delivery probability. Extensive simulation is carried out on the real-world traces to evaluate the accuracy of the location prediction and the proposed scheme in terms of transmission cost, delivery ratio, average hops, and delivery latency, which achieves better routing performances compared to the typical routing schemes in OCNs.

  19. Swarm intelligence for multi-objective optimization of synthesis gas production

    Science.gov (United States)

    Ganesan, T.; Vasant, P.; Elamvazuthi, I.; Ku Shaari, Ku Zilati

    2012-11-01

    In the chemical industry, the production of methanol, ammonia, hydrogen and higher hydrocarbons require synthesis gas (or syn gas). The main three syn gas production methods are carbon dioxide reforming (CRM), steam reforming (SRM) and partial-oxidation of methane (POM). In this work, multi-objective (MO) optimization of the combined CRM and POM was carried out. The empirical model and the MO problem formulation for this combined process were obtained from previous works. The central objectives considered in this problem are methane conversion, carbon monoxide selectivity and the hydrogen to carbon monoxide ratio. The MO nature of the problem was tackled using the Normal Boundary Intersection (NBI) method. Two techniques (Gravitational Search Algorithm (GSA) and Particle Swarm Optimization (PSO)) were then applied in conjunction with the NBI method. The performance of the two algorithms and the quality of the solutions were gauged by using two performance metrics. Comparative studies and results analysis were then carried out on the optimization results.

  20. Artificial Intelligence Techniques: Applications for Courseware Development.

    Science.gov (United States)

    Dear, Brian L.

    1986-01-01

    Introduces some general concepts and techniques of artificial intelligence (natural language interfaces, expert systems, knowledge bases and knowledge representation, heuristics, user-interface metaphors, and object-based environments) and investigates ways these techniques might be applied to analysis, design, development, implementation, and…

  1. Multi-intelligence critical rating assessment of fusion techniques (MiCRAFT)

    Science.gov (United States)

    Blasch, Erik

    2015-06-01

    Assessment of multi-intelligence fusion techniques includes credibility of algorithm performance, quality of results against mission needs, and usability in a work-domain context. Situation awareness (SAW) brings together low-level information fusion (tracking and identification), high-level information fusion (threat and scenario-based assessment), and information fusion level 5 user refinement (physical, cognitive, and information tasks). To measure SAW, we discuss the SAGAT (Situational Awareness Global Assessment Technique) technique for a multi-intelligence fusion (MIF) system assessment that focuses on the advantages of MIF against single intelligence sources. Building on the NASA TLX (Task Load Index), SAGAT probes, SART (Situational Awareness Rating Technique) questionnaires, and CDM (Critical Decision Method) decision points; we highlight these tools for use in a Multi-Intelligence Critical Rating Assessment of Fusion Techniques (MiCRAFT). The focus is to measure user refinement of a situation over the information fusion quality of service (QoS) metrics: timeliness, accuracy, confidence, workload (cost), and attention (throughput). A key component of any user analysis includes correlation, association, and summarization of data; so we also seek measures of product quality and QuEST of information. Building a notion of product quality from multi-intelligence tools is typically subjective which needs to be aligned with objective machine metrics.

  2. Business and Social Behaviour Intelligence Analysis Using PSO

    Directory of Open Access Journals (Sweden)

    Vinay S Bhaskar

    2014-06-01

    Full Text Available The goal of this paper is to elaborate swarm intelligence for business intelligence decision making and the business rules management improvement. The paper introduces the decision making model which is based on the application of Artificial Neural Networks (ANNs and Particle Swarm Optimization (PSO algorithm. Essentially the business spatial data illustrate the group behaviors. The swarm optimization, which is highly influenced by the behavior of creature, performs in group. The Spatial data is defined as data that is represented by 2D or 3D images. SQL Server supports only 2D images till now. As we know that location is an essential part of any organizational data as well as business data: enterprises maintain customer address lists, own property, ship goods from and to warehouses, manage transport flows among their workforce, and perform many other activities. By means to say a lot of spatial data is used and processed by enterprises, organizations and other bodies in order to make the things more visible and self-descriptive. From the experiments, we found that PSO is can facilitate the intelligence in social and business behaviour

  3. THD Minimization from H-Bridge Cascaded Multilevel Inverter Using Particle Swarm Optimization Technique

    Directory of Open Access Journals (Sweden)

    MUDASIR AHMED MEMON

    2017-01-01

    Full Text Available In this paper, PSO (Particle Swarm Optimization based technique is proposed to derive optimized switching angles that minimizes the THD (Total Harmonic Distortion and reduces the effect of selected low order non-triple harmonics from the output of the multilevel inverter. Conventional harmonic elimination techniques have plenty of limitations, and other heuristic techniques also not provide the satisfactory results. In this paper, single phase symmetrical cascaded H-Bridge 11-Level multilevel inverter is considered, and proposed algorithm is utilized to obtain the optimized switching angles that reduced the effect of 5th, 7th, 11th and 13th non-triplen harmonics from the output voltage of the multilevel inverter. A simulation result indicates that this technique outperforms other methods in terms of minimizing THD and provides high-quality output voltage waveform.

  4. THD minimization from h-bridge cascaded multilevel inverter using particle swarm optimization technique

    International Nuclear Information System (INIS)

    Memon, M.A.; Memon, S.; Khan, S.

    2017-01-01

    In this paper, PSO (Particle Swarm Optimization) based technique is proposed to derive optimized switching angles that minimizes the THD (Total Harmonic Distortion) and reduces the effect of selected low order non-triple harmonics from the output of the multilevel inverter. Conventional harmonic elimination techniques have plenty of limitations, and other heuristic techniques also not provide the satisfactory results. In this paper, single phase symmetrical cascaded H-Bridge 11-Level multilevel inverter is considered, and proposed algorithm is utilized to obtain the optimized switching angles that reduced the effect of 5th, 7th, 11th and 13th non-triplen harmonics from the output voltage of the multilevel inverter. A simulation result indicates that this technique outperforms other methods in terms of minimizing THD and provides high-quality output voltage waveform. (author)

  5. Intelligent bioinformatics : the application of artificial intelligence techniques to bioinformatics problems

    National Research Council Canada - National Science Library

    Keedwell, Edward

    2005-01-01

    ... Intelligence and Computer Science 3.1 Introduction to search 3.2 Search algorithms 3.3 Heuristic search methods 3.4 Optimal search strategies 3.5 Problems with search techniques 3.6 Complexity of...

  6. Operator support system using computational intelligence techniques

    International Nuclear Information System (INIS)

    Bueno, Elaine Inacio; Pereira, Iraci Martinez

    2015-01-01

    Computational Intelligence Systems have been widely applied in Monitoring and Fault Detection Systems in several processes and in different kinds of applications. These systems use interdependent components ordered in modules. It is a typical behavior of such systems to ensure early detection and diagnosis of faults. Monitoring and Fault Detection Techniques can be divided into two categories: estimative and pattern recognition methods. The estimative methods use a mathematical model, which describes the process behavior. The pattern recognition methods use a database to describe the process. In this work, an operator support system using Computational Intelligence Techniques was developed. This system will show the information obtained by different CI techniques in order to help operators to take decision in real time and guide them in the fault diagnosis before the normal alarm limits are reached. (author)

  7. Operator support system using computational intelligence techniques

    Energy Technology Data Exchange (ETDEWEB)

    Bueno, Elaine Inacio, E-mail: ebueno@ifsp.edu.br [Instituto Federal de Educacao, Ciencia e Tecnologia de Sao Paulo (IFSP), Sao Paulo, SP (Brazil); Pereira, Iraci Martinez, E-mail: martinez@ipen.br [Instituto de Pesquisas Energeticas e Nucleares (IPEN/CNEN-SP), Sao Paulo, SP (Brazil)

    2015-07-01

    Computational Intelligence Systems have been widely applied in Monitoring and Fault Detection Systems in several processes and in different kinds of applications. These systems use interdependent components ordered in modules. It is a typical behavior of such systems to ensure early detection and diagnosis of faults. Monitoring and Fault Detection Techniques can be divided into two categories: estimative and pattern recognition methods. The estimative methods use a mathematical model, which describes the process behavior. The pattern recognition methods use a database to describe the process. In this work, an operator support system using Computational Intelligence Techniques was developed. This system will show the information obtained by different CI techniques in order to help operators to take decision in real time and guide them in the fault diagnosis before the normal alarm limits are reached. (author)

  8. 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

  9. VAR control in distribution systems by using artificial intelligence techniques

    Energy Technology Data Exchange (ETDEWEB)

    Golkar, M.A. [Curtin Univ. of Technology, Sarawak (Malaysia). School of Engineering and Science

    2005-07-01

    This paper reviewed artificial intelligence techniques used in VAR control systems. Reactive power controls in distribution systems were also reviewed. While artificial intelligence methods are widely used in power control systems, the techniques require extensive human knowledge bases and experiences in order to operate correctly. Expert systems use knowledge and interface procedures to solve problems that often require human expertise. Expert systems often cause knowledge bottlenecks as they are unable to learn or adopt to new situations. While neural networks possess learning ability, they are computationally expensive. However, test results in recent neural network studies have demonstrated that they work well in a variety of loading conditions. Fuzzy logic techniques are used to accurately represent the operational constraints of power systems. Fuzzy logic has an advantage over other artificial intelligence techniques as it is able to remedy uncertainties in data. Evolutionary computing algorithms use probabilistic transition rules which can search complicated data to determine optimal constraints and parameters. Over 95 per cent of all papers published on power systems use genetic algorithms. It was concluded that hybrid systems using various artificial intelligence techniques are now being used by researchers. 69 refs.

  10. 1st International Conference on Intelligent Computing and Communication

    CERN Document Server

    Satapathy, Suresh; Sanyal, Manas; Bhateja, Vikrant

    2017-01-01

    The book covers a wide range of topics in Computer Science and Information Technology including swarm intelligence, artificial intelligence, evolutionary algorithms, and bio-inspired algorithms. It is a collection of papers presented at the First International Conference on Intelligent Computing and Communication (ICIC2) 2016. The prime areas of the conference are Intelligent Computing, Intelligent Communication, Bio-informatics, Geo-informatics, Algorithm, Graphics and Image Processing, Graph Labeling, Web Security, Privacy and e-Commerce, Computational Geometry, Service Orient Architecture, and Data Engineering.

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

    Directory of Open Access Journals (Sweden)

    Shaolong Chen

    2016-01-01

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

  12. Hybrid Taguchi DNA Swarm Intelligence for Optimal Inverse Kinematics Redundancy Resolution of Six-DOF Humanoid Robot Arms

    Directory of Open Access Journals (Sweden)

    Hsu-Chih Huang

    2014-01-01

    Full Text Available This paper presents a hybrid Taguchi deoxyribonucleic acid (DNA swarm intelligence for solving the inverse kinematics redundancy problem of six degree-of-freedom (DOF humanoid robot arms. The inverse kinematics problem of the multi-DOF humanoid robot arm is redundant and has no general closed-form solutions or analytical solutions. The optimal joint configurations are obtained by minimizing the predefined performance index in DNA algorithm for real-world humanoid robotics application. The Taguchi method is employed to determine the DNA parameters to search for the joint solutions of the six-DOF robot arms more efficiently. This approach circumvents the disadvantage of time-consuming tuning procedure in conventional DNA computing. Simulation results are conducted to illustrate the effectiveness and merit of the proposed methods. This Taguchi-based DNA (TDNA solver outperforms the conventional solvers, such as geometric solver, Jacobian-based solver, genetic algorithm (GA solver and ant, colony optimization (ACO solver.

  13. Modeling and Flocking Consensus Analysis for Large-Scale UAV Swarms

    Directory of Open Access Journals (Sweden)

    Li Bing

    2013-01-01

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

  14. Swarmie User Manual: A Rover Used for Multi-agent Swarm Research

    Science.gov (United States)

    Montague, Gilbert

    2014-01-01

    The ability to create multiple functional yet cost effective robots is crucial for conducting swarming robotics research. The Center Innovation Fund (CIF) swarming robotics project is a collaboration among the KSC Granular Mechanics and Regolith Operations (GMRO) group, the University of New Mexico Biological Computation Lab, and the NASA Ames Intelligent Robotics Group (IRG) that uses rovers, dubbed "Swarmies", as test platforms for genetic search algorithms. This fall, I assisted in the development of the software modules used on the Swarmies and created this guide to provide thorough instructions on how to configure your workspace to operate a Swarmie both in simulation and out in the field.

  15. New approaches in intelligent control techniques, methodologies and applications

    CERN Document Server

    Kountchev, Roumen

    2016-01-01

    This volume introduces new approaches in intelligent control area from both the viewpoints of theory and application. It consists of eleven contributions by prominent authors from all over the world and an introductory chapter. This volume is strongly connected to another volume entitled "New Approaches in Intelligent Image Analysis" (Eds. Roumen Kountchev and Kazumi Nakamatsu). The chapters of this volume are self-contained and include summary, conclusion and future works. Some of the chapters introduce specific case studies of various intelligent control systems and others focus on intelligent theory based control techniques with applications. A remarkable specificity of this volume is that three chapters are dealing with intelligent control based on paraconsistent logics.

  16. 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

  17. Artificial intelligence techniques in power systems

    Energy Technology Data Exchange (ETDEWEB)

    Laughton, M.A.

    1997-12-31

    Since the early to mid 1980s much of the effort in power systems analysis has turned away from the methodology of formal mathematical modelling which came from the fields of operations research, control theory and numerical analysis to the less rigorous techniques of artificial intelligence (AI). Today the main AI techniques found in power systems applications are those utilising the logic and knowledge representations of expert systems, fuzzy systems, artificial neural networks (ANN) and, more recently, evolutionary computing. These techniques will be outlined in this chapter and the power system applications indicated. (Author)

  18. A Novel Artificial Fish Swarm Algorithm for Recalibration of Fiber Optic Gyroscope Error Parameters

    Directory of Open Access Journals (Sweden)

    Yanbin Gao

    2015-05-01

    Full Text Available The artificial fish swarm algorithm (AFSA is one of the state-of-the-art swarm intelligent techniques, which is widely utilized for optimization purposes. Fiber optic gyroscope (FOG error parameters such as scale factors, biases and misalignment errors are relatively unstable, especially with the environmental disturbances and the aging of fiber coils. These uncalibrated error parameters are the main reasons that the precision of FOG-based strapdown inertial navigation system (SINS degraded. This research is mainly on the application of a novel artificial fish swarm algorithm (NAFSA on FOG error coefficients recalibration/identification. First, the NAFSA avoided the demerits (e.g., lack of using artificial fishes’ pervious experiences, lack of existing balance between exploration and exploitation, and high computational cost of the standard AFSA during the optimization process. To solve these weak points, functional behaviors and the overall procedures of AFSA have been improved with some parameters eliminated and several supplementary parameters added. Second, a hybrid FOG error coefficients recalibration algorithm has been proposed based on NAFSA and Monte Carlo simulation (MCS approaches. This combination leads to maximum utilization of the involved approaches for FOG error coefficients recalibration. After that, the NAFSA is verified with simulation and experiments and its priorities are compared with that of the conventional calibration method and optimal AFSA. Results demonstrate high efficiency of the NAFSA on FOG error coefficients recalibration.

  19. A novel artificial fish swarm algorithm for recalibration of fiber optic gyroscope error parameters.

    Science.gov (United States)

    Gao, Yanbin; Guan, Lianwu; Wang, Tingjun; Sun, Yunlong

    2015-05-05

    The artificial fish swarm algorithm (AFSA) is one of the state-of-the-art swarm intelligent techniques, which is widely utilized for optimization purposes. Fiber optic gyroscope (FOG) error parameters such as scale factors, biases and misalignment errors are relatively unstable, especially with the environmental disturbances and the aging of fiber coils. These uncalibrated error parameters are the main reasons that the precision of FOG-based strapdown inertial navigation system (SINS) degraded. This research is mainly on the application of a novel artificial fish swarm algorithm (NAFSA) on FOG error coefficients recalibration/identification. First, the NAFSA avoided the demerits (e.g., lack of using artificial fishes' pervious experiences, lack of existing balance between exploration and exploitation, and high computational cost) of the standard AFSA during the optimization process. To solve these weak points, functional behaviors and the overall procedures of AFSA have been improved with some parameters eliminated and several supplementary parameters added. Second, a hybrid FOG error coefficients recalibration algorithm has been proposed based on NAFSA and Monte Carlo simulation (MCS) approaches. This combination leads to maximum utilization of the involved approaches for FOG error coefficients recalibration. After that, the NAFSA is verified with simulation and experiments and its priorities are compared with that of the conventional calibration method and optimal AFSA. Results demonstrate high efficiency of the NAFSA on FOG error coefficients recalibration.

  20. A critical comparison of electron scattering cross sections measured by single collision and swarm techniques

    International Nuclear Information System (INIS)

    Buckman, S.J.; Brunger, M.J.

    1996-07-01

    Electron scattering cross sections (elastic, rotational and vibrational excitation) for a number of atomic and (relatively) single molecular systems are examined. Particular reference is made to the level of agreement which is obtained from the application of the completely different measurement philosophies embodied in 'beam' and 'swarm' techniques. The range of energies considered is generally restricted to the region below 5 eV. 142 refs., 1 tab., 12 figs

  1. New development thoughts on the bio-inspired intelligence based control for unmanned combat aerial vehicle

    Institute of Scientific and Technical Information of China (English)

    2010-01-01

    Bio-inspired intelligence is in the spotlight in the field of international artificial intelligence,and unmanned combat aerial vehicle(UCAV),owing to its potential to perform dangerous,repetitive tasks in remote and hazardous,is very promising for the technological leadership of the nation and essential for improving the security of society.On the basis of introduction of bioinspired intelligence and UCAV,a series of new development thoughts on UCAV control are proposed,including artificial brain based high-level autonomous control for UCAV,swarm intelligence based cooperative control for multiple UCAVs,hy-brid swarm intelligence and Bayesian network based situation assessment under complicated combating environments, bio-inspired hardware based high-level autonomous control for UCAV,and meta-heuristic intelligence based heterogeneous cooperative control for multiple UCAVs and unmanned combat ground vehicles(UCGVs).The exact realization of the proposed new development thoughts can enhance the effectiveness of combat,while provide a series of novel breakthroughs for the intelligence,integration and advancement of future UCAV systems.

  2. A reduced scale two loop PWR core designed with particle swarm optimization technique

    International Nuclear Information System (INIS)

    Lima Junior, Carlos A. Souza; Pereira, Claudio M.N.A; Lapa, Celso M.F.; Cunha, Joao J.; Alvim, Antonio C.M.

    2007-01-01

    Reduced scale experiments are often employed in engineering projects because they are much cheaper than real scale testing. Unfortunately, designing reduced scale thermal-hydraulic circuit or equipment, with the capability of reproducing, both accurately and simultaneously, all physical phenomena that occur in real scale and at operating conditions, is a difficult task. To solve this problem, advanced optimization techniques, such as Genetic Algorithms, have been applied. Following this research line, we have performed investigations, using the Particle Swarm Optimization (PSO) Technique, to design a reduced scale two loop Pressurized Water Reactor (PWR) core, considering 100% of nominal power and non accidental operating conditions. Obtained results show that the proposed methodology is a promising approach for forced flow reduced scale experiments. (author)

  3. An efficient swarm intelligence approach to feature selection based on invasive weed optimization: Application to multivariate calibration and classification using spectroscopic data

    Science.gov (United States)

    Sheykhizadeh, Saheleh; Naseri, Abdolhossein

    2018-04-01

    Variable selection plays a key role in classification and multivariate calibration. Variable selection methods are aimed at choosing a set of variables, from a large pool of available predictors, relevant to the analyte concentrations estimation, or to achieve better classification results. Many variable selection techniques have now been introduced among which, those which are based on the methodologies of swarm intelligence optimization have been more respected during a few last decades since they are mainly inspired by nature. In this work, a simple and new variable selection algorithm is proposed according to the invasive weed optimization (IWO) concept. IWO is considered a bio-inspired metaheuristic mimicking the weeds ecological behavior in colonizing as well as finding an appropriate place for growth and reproduction; it has been shown to be very adaptive and powerful to environmental changes. In this paper, the first application of IWO, as a very simple and powerful method, to variable selection is reported using different experimental datasets including FTIR and NIR data, so as to undertake classification and multivariate calibration tasks. Accordingly, invasive weed optimization - linear discrimination analysis (IWO-LDA) and invasive weed optimization- partial least squares (IWO-PLS) are introduced for multivariate classification and calibration, respectively.

  4. PARALLEL IMPLEMENTATION OF CROSS-LAYER OPTIMIZATION - A PERFORMANCE EVALUATION BASED ON SWARM INTELLIGENCE

    Directory of Open Access Journals (Sweden)

    Vanaja Gokul

    2012-01-01

    Full Text Available In distributed systems real time optimizations need to be performed dynamically for better utilization of the network resources. Real time optimizations can be performed effectively by using Cross Layer Optimization (CLO within the network operating system. This paper presents the performance evaluation of Cross Layer Optimization (CLO in comparison with the traditional approach of Single-Layer Optimization (SLO. In the parallel implementation of the approaches the experimental study carried out indicates that the CLO results in a significant improvement in network utilization when compared to SLO. A variant of the Particle Swarm Optimization technique that utilizes Digital Pheromones (PSODP for better performance has been used here. A significantly higher speed up in performance was observed from the parallel implementation of CLO that used PSODP on a cluster of nodes.

  5. Application of Artificial Intelligence and Data Mining Techniques to Financial Markets

    OpenAIRE

    Katarína Hilovska; Peter Koncz

    2012-01-01

    The aim of artificial intelligence is to discover mechanisms of adaptation in a changing environment with utilisation of intelligence, for instance in the ability to exclude unlikely solutions. Artificial intelligence methods have extensive application in different fields such as medicine, games, transportation, or heavy industry. This paper deals with interdisciplinary issues – interconnection of artificial intelligence and finance. The paper briefly describes techniques of data mining, expe...

  6. Event tree analysis using artificial intelligence techniques

    International Nuclear Information System (INIS)

    Dixon, B.W.; Hinton, M.F.

    1985-01-01

    Artificial Intelligence (AI) techniques used in Expert Systems and Object Oriented Programming are discussed as they apply to Event Tree Analysis. A SeQUence IMPortance calculator, SQUIMP, is presented to demonstrate the implementation of these techniques. Benefits of using AI methods include ease of programming, efficiency of execution, and flexibility of application. The importance of an appropriate user interface is stressed. 5 figs

  7. 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

  8. 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

  9. International Conference on Computational Intelligence 2015

    CERN Document Server

    Saha, Sujan

    2017-01-01

    This volume comprises the proceedings of the International Conference on Computational Intelligence 2015 (ICCI15). This book aims to bring together work from leading academicians, scientists, researchers and research scholars from across the globe on all aspects of computational intelligence. The work is composed mainly of original and unpublished results of conceptual, constructive, empirical, experimental, or theoretical work in all areas of computational intelligence. Specifically, the major topics covered include classical computational intelligence models and artificial intelligence, neural networks and deep learning, evolutionary swarm and particle algorithms, hybrid systems optimization, constraint programming, human-machine interaction, computational intelligence for the web analytics, robotics, computational neurosciences, neurodynamics, bioinspired and biomorphic algorithms, cross disciplinary topics and applications. The contents of this volume will be of use to researchers and professionals alike....

  10. 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

  11. A NOVEL APPROACH TO FIND OPTIMIZED NEUTRON ENERGY GROUP STRUCTURE IN MOX THERMAL LATTICES USING SWARM INTELLIGENCE

    Directory of Open Access Journals (Sweden)

    M. AKBARI

    2013-12-01

    Full Text Available Energy group structure has a significant effect on the results of multigroup transport calculations. It is known that UO2–PUO2 (MOX is a recently developed fuel which consumes recycled plutonium. For such fuel which contains various resonant nuclides, the selection of energy group structure is more crucial comparing to the UO2 fuels. In this paper, in order to improve the accuracy of the integral results in MOX thermal lattices calculated by WIMSD-5B code, a swarm intelligence method is employed to optimize the energy group structure of WIMS library. In this process, the NJOY code system is used to generate the 69 group cross sections of WIMS code for the specified energy structure. In addition, the multiplication factor and spectral indices are compared against the results of continuous energy MCNP-4C code for evaluating the energy group structure. Calculations performed in four different types of H2O moderated UO2–PuO2 (MOX lattices show that the optimized energy structure obtains more accurate results in comparison with the WIMS original structure.

  12. A review on economic emission dispatch problems using quantum computational intelligence

    Science.gov (United States)

    Mahdi, Fahad Parvez; Vasant, Pandian; Kallimani, Vish; Abdullah-Al-Wadud, M.

    2016-11-01

    Economic emission dispatch (EED) problems are one of the most crucial problems in power systems. Growing energy demand, limitation of natural resources and global warming make this topic into the center of discussion and research. This paper reviews the use of Quantum Computational Intelligence (QCI) in solving Economic Emission Dispatch problems. QCI techniques like Quantum Genetic Algorithm (QGA) and Quantum Particle Swarm Optimization (QPSO) algorithm are discussed here. This paper will encourage the researcher to use more QCI based algorithm to get better optimal result for solving EED problems.

  13. A chaotic quantum-behaved particle swarm approach applied to optimization of heat exchangers

    International Nuclear Information System (INIS)

    Mariani, Viviana Cocco; Klassen Duck, Anderson Rodrigo; Guerra, Fabio Alessandro; Santos Coelho, Leandro dos; Rao, Ravipudi Venkata

    2012-01-01

    Particle swarm optimization (PSO) method is a population-based optimization technique of swarm intelligence field in which each solution called “particle” flies around in a multidimensional problem search space. During the flight, every particle adjusts its position according to its own experience, as well as the experience of neighboring particles, using the best position encountered by itself and its neighbors. In this paper, a new quantum particle swarm optimization (QPSO) approach combined with Zaslavskii chaotic map sequences (QPSOZ) to shell and tube heat exchanger optimization is presented based on the minimization from economic view point. The results obtained in this paper for two case studies using the proposed QPSOZ approach, are compared with those obtained by using genetic algorithm, PSO and classical QPSO showing the best performance of QPSOZ. In order to verify the capability of the proposed method, two case studies are also presented showing that significant cost reductions are feasible with respect to traditionally designed exchangers. Referring to the literature test cases, reduction of capital investment up to 20% and 6% for the first and second cases, respectively, were obtained. Therefore, the annual pumping cost decreased markedly 72% and 75%, with an overall decrease of total cost up to 30% and 27%, respectively, for the cases 1 and 2, respectively, showing the improvement potential of the proposed method, QPSOZ. - Highlights: ► Shell and tube heat exchanger is minimized from economic view point. ► A new quantum particle swarm optimization (QPSO) combined with Zaslavskii chaotic map sequences (QPSOZ) is proposed. ► Reduction of capital investment up to 20% and 6% for the first and second cases was obtained. ► Annual pumping cost decreased 72% and 75%, with an overall decrease of total cost up to 30% and 27% using QPSOZ.

  14. Optimisation of Software-Defined Networks Performance Using a Hybrid Intelligent System

    Directory of Open Access Journals (Sweden)

    Ann Sabih

    2017-06-01

    Full Text Available This paper proposes a novel intelligent technique that has been designed to optimise the performance of Software Defined Networks (SDN. The proposed hybrid intelligent system has employed integration of intelligence-based optimisation approaches with the artificial neural network. These heuristic optimisation methods include Genetic Algorithms (GA and Particle Swarm Optimisation (PSO. These methods were utilised separately in order to select the best inputs to maximise SDN performance. In order to identify SDN behaviour, the neural network model is trained and applied. The maximal optimisation approach has been identified using an analytical approach that considered SDN performance and the computational time as objective functions. Initially, the general model of the neural network was tested with unseen data before implementing the model using GA and PSO to determine the optimal performance of SDN. The results showed that the SDN represented by Artificial Neural Network ANN, and optmised by PSO, generated a better configuration with regards to computational efficiency and performance index.

  15. Efficiency of particle swarm optimization applied on fuzzy logic DC motor speed control

    Directory of Open Access Journals (Sweden)

    Allaoua Boumediene

    2008-01-01

    Full Text Available This paper presents the application of Fuzzy Logic for DC motor speed control using Particle Swarm Optimization (PSO. Firstly, the controller designed according to Fuzzy Logic rules is such that the systems are fundamentally robust. Secondly, the Fuzzy Logic controller (FLC used earlier was optimized with PSO so as to obtain optimal adjustment of the membership functions only. Finally, the FLC is completely optimized by Swarm Intelligence Algorithms. Digital simulation results demonstrate that in comparison with the FLC the designed FLC-PSO speed controller obtains better dynamic behavior and superior performance of the DC motor, as well as perfect speed tracking with no overshoot.

  16. Computational Intelligence Techniques for New Product Design

    CERN Document Server

    Chan, Kit Yan; Dillon, Tharam S

    2012-01-01

    Applying computational intelligence for product design is a fast-growing and promising research area in computer sciences and industrial engineering. However, there is currently a lack of books, which discuss this research area. This book discusses a wide range of computational intelligence techniques for implementation on product design. It covers common issues on product design from identification of customer requirements in product design, determination of importance of customer requirements, determination of optimal design attributes, relating design attributes and customer satisfaction, integration of marketing aspects into product design, affective product design, to quality control of new products. Approaches for refinement of computational intelligence are discussed, in order to address different issues on product design. Cases studies of product design in terms of development of real-world new products are included, in order to illustrate the design procedures, as well as the effectiveness of the com...

  17. Auto-correlation based intelligent technique for complex waveform presentation and measurement

    International Nuclear Information System (INIS)

    Rana, K P S; Singh, R; Sayann, K S

    2009-01-01

    Waveform acquisition and presentation forms the heart of many measurement systems. Particularly, data acquisition and presentation of repeating complex signals like sine sweep and frequency-modulated signals introduces the challenge of waveform time period estimation and live waveform presentation. This paper presents an intelligent technique, for waveform period estimation of both the complex and simple waveforms, based on the normalized auto-correlation method. The proposed technique is demonstrated using LabVIEW based intensive simulations on several simple and complex waveforms. Implementation of the technique is successfully demonstrated using LabVIEW based virtual instrumentation. Sine sweep vibration waveforms are successfully presented and measured for electrodynamic shaker system generated vibrations. The proposed method is also suitable for digital storage oscilloscope (DSO) triggering, for complex signals acquisition and presentation. This intelligence can be embodied into the DSO, making it an intelligent measurement system, catering wide varieties of the waveforms. The proposed technique, simulation results, robustness study and implementation results are presented in this paper.

  18. 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.

  19. Application of Artificial Intelligence Techniques in Unmanned Aerial Vehicle Flight

    Science.gov (United States)

    Bauer, Frank H. (Technical Monitor); Dufrene, Warren R., Jr.

    2003-01-01

    This paper describes the development of an application of Artificial Intelligence for Unmanned Aerial Vehicle (UAV) control. The project was done as part of the requirements for a class in Artificial Intelligence (AI) at Nova southeastern University and as an adjunct to a project at NASA Goddard Space Flight Center's Wallops Flight Facility for a resilient, robust, and intelligent UAV flight control system. A method is outlined which allows a base level application for applying an AI method, Fuzzy Logic, to aspects of Control Logic for UAV flight. One element of UAV flight, automated altitude hold, has been implemented and preliminary results displayed. A low cost approach was taken using freeware, gnu, software, and demo programs. The focus of this research has been to outline some of the AI techniques used for UAV flight control and discuss some of the tools used to apply AI techniques. The intent is to succeed with the implementation of applying AI techniques to actually control different aspects of the flight of an UAV.

  20. Intelligent computing systems emerging application areas

    CERN Document Server

    Virvou, Maria; Jain, Lakhmi

    2016-01-01

    This book at hand explores emerging scientific and technological areas in which Intelligent Computing Systems provide efficient solutions and, thus, may play a role in the years to come. It demonstrates how Intelligent Computing Systems make use of computational methodologies that mimic nature-inspired processes to address real world problems of high complexity for which exact mathematical solutions, based on physical and statistical modelling, are intractable. Common intelligent computational methodologies are presented including artificial neural networks, evolutionary computation, genetic algorithms, artificial immune systems, fuzzy logic, swarm intelligence, artificial life, virtual worlds and hybrid methodologies based on combinations of the previous. The book will be useful to researchers, practitioners and graduate students dealing with mathematically-intractable problems. It is intended for both the expert/researcher in the field of Intelligent Computing Systems, as well as for the general reader in t...

  1. 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.

  2. 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.

  3. 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)

  4. Application of artificial intelligence techniques to TRR operation

    International Nuclear Information System (INIS)

    Ho, L.; Tseng, C.; Chang, S.

    1986-01-01

    It has been over ten years since TRR had its initial critical. To collect the experiences of shift operators and technique staffs and transfer these experts' knowledge to a computer and build an expert system is a typical application of artificial intelligence techniques to nuclear business. The system can provide the correct information of TRR operation for shift personnel, new staffs and other technical people

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

    Science.gov (United States)

    Huang, Yueh-Min; Liu, Chien-Hung

    2009-01-01

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

  6. 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.

  7. 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.

  8. Swarm Intelligence: New Techniques for Adaptive Systems to Provide Learning Support

    Science.gov (United States)

    Wong, Lung-Hsiang; Looi, Chee-Kit

    2012-01-01

    The notion of a system adapting itself to provide support for learning has always been an important issue of research for technology-enabled learning. One approach to provide adaptivity is to use social navigation approaches and techniques which involve analysing data of what was previously selected by a cluster of users or what worked for…

  9. 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.

  10. Modeling of biological intelligence for SCM system optimization.

    Science.gov (United States)

    Chen, Shengyong; Zheng, Yujun; Cattani, Carlo; Wang, Wanliang

    2012-01-01

    This article summarizes some methods from biological intelligence for modeling and optimization of supply chain management (SCM) systems, including genetic algorithms, evolutionary programming, differential evolution, swarm intelligence, artificial immune, and other biological intelligence related methods. An SCM system is adaptive, dynamic, open self-organizing, which is maintained by flows of information, materials, goods, funds, and energy. Traditional methods for modeling and optimizing complex SCM systems require huge amounts of computing resources, and biological intelligence-based solutions can often provide valuable alternatives for efficiently solving problems. The paper summarizes the recent related methods for the design and optimization of SCM systems, which covers the most widely used genetic algorithms and other evolutionary algorithms.

  11. Modeling of Biological Intelligence for SCM System Optimization

    Directory of Open Access Journals (Sweden)

    Shengyong Chen

    2012-01-01

    Full Text Available This article summarizes some methods from biological intelligence for modeling and optimization of supply chain management (SCM systems, including genetic algorithms, evolutionary programming, differential evolution, swarm intelligence, artificial immune, and other biological intelligence related methods. An SCM system is adaptive, dynamic, open self-organizing, which is maintained by flows of information, materials, goods, funds, and energy. Traditional methods for modeling and optimizing complex SCM systems require huge amounts of computing resources, and biological intelligence-based solutions can often provide valuable alternatives for efficiently solving problems. The paper summarizes the recent related methods for the design and optimization of SCM systems, which covers the most widely used genetic algorithms and other evolutionary algorithms.

  12. Modeling of Biological Intelligence for SCM System Optimization

    Science.gov (United States)

    Chen, Shengyong; Zheng, Yujun; Cattani, Carlo; Wang, Wanliang

    2012-01-01

    This article summarizes some methods from biological intelligence for modeling and optimization of supply chain management (SCM) systems, including genetic algorithms, evolutionary programming, differential evolution, swarm intelligence, artificial immune, and other biological intelligence related methods. An SCM system is adaptive, dynamic, open self-organizing, which is maintained by flows of information, materials, goods, funds, and energy. Traditional methods for modeling and optimizing complex SCM systems require huge amounts of computing resources, and biological intelligence-based solutions can often provide valuable alternatives for efficiently solving problems. The paper summarizes the recent related methods for the design and optimization of SCM systems, which covers the most widely used genetic algorithms and other evolutionary algorithms. PMID:22162724

  13. Application of Genetic Algorithm and Particle Swarm Optimization techniques for improved image steganography systems

    Directory of Open Access Journals (Sweden)

    Jude Hemanth Duraisamy

    2016-01-01

    Full Text Available Image steganography is one of the ever growing computational approaches which has found its application in many fields. The frequency domain techniques are highly preferred for image steganography applications. However, there are significant drawbacks associated with these techniques. In transform based approaches, the secret data is embedded in random manner in the transform coefficients of the cover image. These transform coefficients may not be optimal in terms of the stego image quality and embedding capacity. In this work, the application of Genetic Algorithm (GA and Particle Swarm Optimization (PSO have been explored in the context of determining the optimal coefficients in these transforms. Frequency domain transforms such as Bandelet Transform (BT and Finite Ridgelet Transform (FRIT are used in combination with GA and PSO to improve the efficiency of the image steganography system.

  14. Intelligent transportation systems data compression using wavelet decomposition technique.

    Science.gov (United States)

    2009-12-01

    Intelligent Transportation Systems (ITS) generates massive amounts of traffic data, which posts : challenges for data storage, transmission and retrieval. Data compression and reconstruction technique plays an : important role in ITS data procession....

  15. Multimedia techniques for device and ambient intelligence: A continuing endeavor

    NARCIS (Netherlands)

    van den Broek, Egon

    2011-01-01

    The edited volume "Multimedia techniques for device and ambient intelligence" consists of two parts: i) an introduction to a variety of basic low level image processing techniques, leaving aside other modalities, and ii) work on high level, knowledge based processing, including interesting chapters

  16. Controller Design of DFIG Based Wind Turbine by Using Evolutionary Soft Computational Techniques

    Directory of Open Access Journals (Sweden)

    O. P. Bharti

    2017-06-01

    Full Text Available This manuscript illustrates the controller design for a doubly fed induction generator based variable speed wind turbine by using a bioinspired scheme. This methodology is based on exploiting two proficient swarm intelligence based evolutionary soft computational procedures. The particle swarm optimization (PSO and bacterial foraging optimization (BFO techniques are employed to design the controller intended for small damping plant of the DFIG. Wind energy overview and DFIG operating principle along with the equivalent circuit model is adequately discussed in this paper. The controller design for DFIG based WECS using PSO and BFO are described comparatively in detail. The responses of the DFIG system regarding terminal voltage, current, active-reactive power, and DC-Link voltage have slightly improved with the evolutionary soft computational procedure. Lastly, the obtained output is equated with a standard technique for performance improvement of DFIG based wind energy conversion system.

  17. 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

  18. 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.

  19. Simplified particle swarm optimization algorithm - doi: 10.4025/actascitechnol.v34i1.9679

    Directory of Open Access Journals (Sweden)

    Ricardo Paupitz Barbosa dos Santos

    2011-11-01

    Full Text Available Real ants and bees are considered social insects, which present some remarkable characteristics that can be used, as inspiration, to solve complex optimization problems. This field of study is known as swarm intelligence. Therefore, this paper presents a new algorithm that can be understood as a simplified version of the well known Particle Swarm Optimization (PSO. The proposed algorithm allows saving some computational effort and obtains a considerable performance in the optimization of nonlinear functions. We employed four nonlinear benchmark functions, Sphere, Schwefel, Schaffer and Ackley functions, to test and validate the new proposal. Some simulated results were used in order to clarify the efficiency of the proposed algorithm.

  20. Computational Intelligence based techniques for islanding detection of distributed generation in distribution network: A review

    International Nuclear Information System (INIS)

    Laghari, J.A.; Mokhlis, H.; Karimi, M.; Bakar, A.H.A.; Mohamad, Hasmaini

    2014-01-01

    Highlights: • Unintentional and intentional islanding, their causes, and solutions are presented. • Remote, passive, active and hybrid islanding detection techniques are discussed. • The limitation of these techniques in accurately detect islanding are discussed. • Computational intelligence techniques ability in detecting islanding is discussed. • Review of ANN, fuzzy logic control, ANFIS, Decision tree techniques is provided. - Abstract: Accurate and fast islanding detection of distributed generation is highly important for its successful operation in distribution networks. Up to now, various islanding detection technique based on communication, passive, active and hybrid methods have been proposed. However, each technique suffers from certain demerits that cause inaccuracies in islanding detection. Computational intelligence based techniques, due to their robustness and flexibility in dealing with complex nonlinear systems, is an option that might solve this problem. This paper aims to provide a comprehensive review of computational intelligence based techniques applied for islanding detection of distributed generation. Moreover, the paper compares the accuracies of computational intelligence based techniques over existing techniques to provide a handful of information for industries and utility researchers to determine the best method for their respective system

  1. 7th International Symposium on Intelligent Distributed Computing

    CERN Document Server

    Jung, Jason; Badica, Costin

    2014-01-01

    This book represents the combined peer-reviewed proceedings of the Seventh International Symposium on Intelligent Distributed Computing - IDC-2013, of the Second Workshop on Agents for Clouds - A4C-2013, of the Fifth International Workshop on Multi-Agent Systems Technology and Semantics - MASTS-2013, and of the International Workshop on Intelligent Robots - iR-2013. All the events were held in Prague, Czech Republic during September 4-6, 2013. The 41 contributions published in this book address many topics related to theory and applications of intelligent distributed computing and multi-agent systems, including: agent-based data processing, ambient intelligence, bio-informatics, collaborative systems, cryptography and security, distributed algorithms, grid and cloud computing, information extraction, intelligent robotics, knowledge management, linked data, mobile agents, ontologies, pervasive computing, self-organizing systems, peer-to-peer computing, social networks and trust, and swarm intelligence.  .

  2. 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

  3. 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)

  4. 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

  5. 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.

  6. Recent Advances in Intelligent Engineering Systems

    CERN Document Server

    Klempous, Ryszard; Araujo, Carmen

    2012-01-01

    This volume is a collection of 19 chapters on intelligent engineering systems written by respectable experts of the fields. The book consists of three parts. The first part is devoted to the foundational aspects of computational intelligence. It consists of 8 chapters that include studies in genetic algorithms, fuzzy logic connectives, enhanced intelligence in product models, nature-inspired optimization technologies, particle swarm optimization, evolution algorithms, model complexity of neural networks, and fitness landscape analysis. The second part contains contributions to intelligent computation in networks, presented in 5 chapters. The covered subjects include the application of self-organizing maps for early detection of denial of service attacks, combating security threats via immunity and adaptability in cognitive radio networks, novel modifications in WSN network design for improved SNR and reliability, a conceptual framework for the design of audio based cognitive infocommunication channels, and a ...

  7. New approaches in intelligent image analysis techniques, methodologies and applications

    CERN Document Server

    Nakamatsu, Kazumi

    2016-01-01

    This book presents an Introduction and 11 independent chapters, which are devoted to various new approaches of intelligent image processing and analysis. The book also presents new methods, algorithms and applied systems for intelligent image processing, on the following basic topics: Methods for Hierarchical Image Decomposition; Intelligent Digital Signal Processing and Feature Extraction; Data Clustering and Visualization via Echo State Networks; Clustering of Natural Images in Automatic Image Annotation Systems; Control System for Remote Sensing Image Processing; Tissue Segmentation of MR Brain Images Sequence; Kidney Cysts Segmentation in CT Images; Audio Visual Attention Models in Mobile Robots Navigation; Local Adaptive Image Processing; Learning Techniques for Intelligent Access Control; Resolution Improvement in Acoustic Maps. Each chapter is self-contained with its own references. Some of the chapters are devoted to the theoretical aspects while the others are presenting the practical aspects and the...

  8. 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.

  9. A Suboptimal PTS Algorithm Based on Particle Swarm Optimization Technique for PAPR Reduction in OFDM Systems

    Directory of Open Access Journals (Sweden)

    Ho-Lung Hung

    2008-08-01

    Full Text Available A suboptimal partial transmit sequence (PTS based on particle swarm optimization (PSO algorithm is presented for the low computation complexity and the reduction of the peak-to-average power ratio (PAPR of an orthogonal frequency division multiplexing (OFDM system. In general, PTS technique can improve the PAPR statistics of an OFDM system. However, it will come with an exhaustive search over all combinations of allowed phase weighting factors and the search complexity increasing exponentially with the number of subblocks. In this paper, we work around potentially computational intractability; the proposed PSO scheme exploits heuristics to search the optimal combination of phase factors with low complexity. Simulation results show that the new technique can effectively reduce the computation complexity and PAPR reduction.

  10. A Suboptimal PTS Algorithm Based on Particle Swarm Optimization Technique for PAPR Reduction in OFDM Systems

    Directory of Open Access Journals (Sweden)

    Lee Shu-Hong

    2008-01-01

    Full Text Available Abstract A suboptimal partial transmit sequence (PTS based on particle swarm optimization (PSO algorithm is presented for the low computation complexity and the reduction of the peak-to-average power ratio (PAPR of an orthogonal frequency division multiplexing (OFDM system. In general, PTS technique can improve the PAPR statistics of an OFDM system. However, it will come with an exhaustive search over all combinations of allowed phase weighting factors and the search complexity increasing exponentially with the number of subblocks. In this paper, we work around potentially computational intractability; the proposed PSO scheme exploits heuristics to search the optimal combination of phase factors with low complexity. Simulation results show that the new technique can effectively reduce the computation complexity and PAPR reduction.

  11. Artificial intelligence based models for stream-flow forecasting: 2000-2015

    Science.gov (United States)

    Yaseen, Zaher Mundher; El-shafie, Ahmed; Jaafar, Othman; Afan, Haitham Abdulmohsin; Sayl, Khamis Naba

    2015-11-01

    The use of Artificial Intelligence (AI) has increased since the middle of the 20th century as seen in its application in a wide range of engineering and science problems. The last two decades, for example, has seen a dramatic increase in the development and application of various types of AI approaches for stream-flow forecasting. Generally speaking, AI has exhibited significant progress in forecasting and modeling non-linear hydrological applications and in capturing the noise complexity in the dataset. This paper explores the state-of-the-art application of AI in stream-flow forecasting, focusing on defining the data-driven of AI, the advantages of complementary models, as well as the literature and their possible future application in modeling and forecasting stream-flow. The review also identifies the major challenges and opportunities for prospective research, including, a new scheme for modeling the inflow, a novel method for preprocessing time series frequency based on Fast Orthogonal Search (FOS) techniques, and Swarm Intelligence (SI) as an optimization approach.

  12. 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.

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

    International Nuclear Information System (INIS)

    Huang, Chia-Ling

    2015-01-01

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

  14. Swarm Intelligence-Enhanced Detection of Non-Small-Cell Lung Cancer Using Tumor-Educated Platelets

    NARCIS (Netherlands)

    Best, Myron G.; Sol, Nik; In ‘t Veld, Sjors G.J.G.; Vancura, Adrienne; Muller, Mirte; Niemeijer, Anna Larissa N.; Fejes, Aniko V.; Tjon Kon Fat, Lee Ann; Huis in 't Veld, Anna E; Leurs, Cyra; Le Large, Tessa Y.; Meijer, Laura L.; Kooi, Irsan E.; Rustenburg, François; Schellen, Pepijn; Verschueren, Heleen; Post, Edward; Wedekind, Laurine E.; Bracht, Jillian; Esenkbrink, Michelle; Wils, Leon; Favaro, Francesca; Schoonhoven, Jilian D.; Tannous, Jihane; Meijers-Heijboer, Hanne; Kazemier, Geert; Giovannetti, Elisa; Reijneveld, Jaap C.; Idema, Sander; Killestein, Joep; Heger, Michal; de Jager, Saskia C.; Urbanus, Rolf T.; Hoefer, Imo E.; Pasterkamp, Gerard; Mannhalter, Christine; Gomez-Arroyo, Jose; Bogaard, Harm-Jan; Noske, David P.; Vandertop, W. Peter; van den Broek, Daan; Ylstra, Bauke; Nilsson, R. Jonas A; Wesseling, Pieter; Karachaliou, Niki; Rosell, Rafael; Lee-Lewandrowski, Elizabeth; Lewandrowski, Kent B.; Tannous, Bakhos A.; de Langen, Adrianus J.; Smit, Egbert F.; van den Heuvel, Michel M; Wurdinger, Thomas

    2017-01-01

    Blood-based liquid biopsies, including tumor-educated blood platelets (TEPs), have emerged as promising biomarker sources for non-invasive detection of cancer. Here we demonstrate that particle-swarm optimization (PSO)-enhanced algorithms enable efficient selection of RNA biomarker panels from

  15. 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...

  16. 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...

  17. The foundations of plant intelligence.

    Science.gov (United States)

    Trewavas, Anthony

    2017-06-06

    Intelligence is defined for wild plants and its role in fitness identified. Intelligent behaviour exhibited by single cells and systems similarity between the interactome and connectome indicates neural systems are not necessary for intelligent capabilities. Plants sense and respond to many environmental signals that are assessed to competitively optimize acquisition of patchily distributed resources. Situations of choice engender motivational states in goal-directed plant behaviour; consequent intelligent decisions enable efficient gain of energy over expenditure. Comparison of swarm intelligence and plant behaviour indicates the origins of plant intelligence lie in complex communication and is exemplified by cambial control of branch function. Error correction in behaviours indicates both awareness and intention as does the ability to count to five. Volatile organic compounds are used as signals in numerous plant interactions. Being complex in composition and often species and individual specific, they may represent the plant language and account for self and alien recognition between individual plants. Game theory has been used to understand competitive and cooperative interactions between plants and microbes. Some unexpected cooperative behaviour between individuals and potential aliens has emerged. Behaviour profiting from experience, another simple definition of intelligence, requires both learning and memory and is indicated in the priming of herbivory, disease and abiotic stresses.

  18. Support Vector Machine Based on Adaptive Acceleration Particle Swarm Optimization

    Science.gov (United States)

    Abdulameer, Mohammed Hasan; Othman, Zulaiha Ali

    2014-01-01

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

  19. Support Vector Machine Based on Adaptive Acceleration Particle Swarm Optimization

    Directory of Open Access Journals (Sweden)

    Mohammed Hasan Abdulameer

    2014-01-01

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

  20. Automation of neutral beam source conditioning with artificial intelligence techniques

    International Nuclear Information System (INIS)

    Johnson, R.R.; Canales, T.W.; Lager, D.L.

    1985-01-01

    This paper describes a system that automates neutral beam source conditioning. The system achieves this with artificial intelligence techniques. The architecture of the system is presented followed by a description of its performance

  1. Automation of neutral beam source conditioning with artificial intelligence techniques

    International Nuclear Information System (INIS)

    Johnson, R.R.; Canales, T.; Lager, D.

    1986-01-01

    This paper describes a system that automates neutral beam source conditioning. The system achieves this with artificial intelligence techniques. The architecture of the system is presented followed by a description of its performance

  2. 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.

  3. 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

  4. Determination of rock depth using artificial intelligence techniques

    Institute of Scientific and Technical Information of China (English)

    R. Viswanathan; Pijush Samui

    2016-01-01

    This article adopts three artificial intelligence techniques, Gaussian Process Regression (GPR), Least Square Support Vector Machine (LSSVM) and Extreme Learning Machine (ELM), for prediction of rock depth (d) at any point in Chennai. GPR, ELM and LSSVM have been used as regression techniques. Latitude and longitude are also adopted as inputs of the GPR, ELM and LSSVM models. The performance of the ELM, GPR and LSSVM models has been compared. The developed ELM, GPR and LSSVM models produce spatial variability of rock depth and offer robust models for the prediction of rock depth.

  5. The Application of Collaborative Business Intelligence Technology in the Hospital SPD Logistics Management Model.

    Science.gov (United States)

    Liu, Tongzhu; Shen, Aizong; Hu, Xiaojian; Tong, Guixian; Gu, Wei

    2017-06-01

    We aimed to apply collaborative business intelligence (BI) system to hospital supply, processing and distribution (SPD) logistics management model. We searched Engineering Village database, China National Knowledge Infrastructure (CNKI) and Google for articles (Published from 2011 to 2016), books, Web pages, etc., to understand SPD and BI related theories and recent research status. For the application of collaborative BI technology in the hospital SPD logistics management model, we realized this by leveraging data mining techniques to discover knowledge from complex data and collaborative techniques to improve the theories of business process. For the application of BI system, we: (i) proposed a layered structure of collaborative BI system for intelligent management in hospital logistics; (ii) built data warehouse for the collaborative BI system; (iii) improved data mining techniques such as supporting vector machines (SVM) and swarm intelligence firefly algorithm to solve key problems in hospital logistics collaborative BI system; (iv) researched the collaborative techniques oriented to data and business process optimization to improve the business processes of hospital logistics management. Proper combination of SPD model and BI system will improve the management of logistics in the hospitals. The successful implementation of the study requires: (i) to innovate and improve the traditional SPD model and make appropriate implement plans and schedules for the application of BI system according to the actual situations of hospitals; (ii) the collaborative participation of internal departments in hospital including the department of information, logistics, nursing, medical and financial; (iii) timely response of external suppliers.

  6. Training Software in Artificial-Intelligence Computing Techniques

    Science.gov (United States)

    Howard, Ayanna; Rogstad, Eric; Chalfant, Eugene

    2005-01-01

    The Artificial Intelligence (AI) Toolkit is a computer program for training scientists, engineers, and university students in three soft-computing techniques (fuzzy logic, neural networks, and genetic algorithms) used in artificial-intelligence applications. The program promotes an easily understandable tutorial interface, including an interactive graphical component through which the user can gain hands-on experience in soft-computing techniques applied to realistic example problems. The tutorial provides step-by-step instructions on the workings of soft-computing technology, whereas the hands-on examples allow interaction and reinforcement of the techniques explained throughout the tutorial. In the fuzzy-logic example, a user can interact with a robot and an obstacle course to verify how fuzzy logic is used to command a rover traverse from an arbitrary start to the goal location. For the genetic algorithm example, the problem is to determine the minimum-length path for visiting a user-chosen set of planets in the solar system. For the neural-network example, the problem is to decide, on the basis of input data on physical characteristics, whether a person is a man, woman, or child. The AI Toolkit is compatible with the Windows 95,98, ME, NT 4.0, 2000, and XP operating systems. A computer having a processor speed of at least 300 MHz, and random-access memory of at least 56MB is recommended for optimal performance. The program can be run on a slower computer having less memory, but some functions may not be executed properly.

  7. Analysis of lineament swarms in a Precambrian metamorphic rocks

    Indian Academy of Sciences (India)

    Addressing the geologic significance of lineaments and their correlation with joints/fractures is still unclear. The present study attempts to analyse the lineament swarms developed in a Precambrian metamorphic terrain in India using both unfiltered and filtered techniques. The unfiltered analysis technique shows that the ...

  8. 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

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

    Science.gov (United States)

    Paasche, H.; Tronicke, J.

    2012-04-01

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

  10. Enhanced intelligent water drops algorithm for multi-depot vehicle routing problem.

    Science.gov (United States)

    Ezugwu, Absalom E; Akutsah, Francis; Olusanya, Micheal O; Adewumi, Aderemi O

    2018-01-01

    The intelligent water drop algorithm is a swarm-based metaheuristic algorithm, inspired by the characteristics of water drops in the river and the environmental changes resulting from the action of the flowing river. Since its appearance as an alternative stochastic optimization method, the algorithm has found applications in solving a wide range of combinatorial and functional optimization problems. This paper presents an improved intelligent water drop algorithm for solving multi-depot vehicle routing problems. A simulated annealing algorithm was introduced into the proposed algorithm as a local search metaheuristic to prevent the intelligent water drop algorithm from getting trapped into local minima and also improve its solution quality. In addition, some of the potential problematic issues associated with using simulated annealing that include high computational runtime and exponential calculation of the probability of acceptance criteria, are investigated. The exponential calculation of the probability of acceptance criteria for the simulated annealing based techniques is computationally expensive. Therefore, in order to maximize the performance of the intelligent water drop algorithm using simulated annealing, a better way of calculating the probability of acceptance criteria is considered. The performance of the proposed hybrid algorithm is evaluated by using 33 standard test problems, with the results obtained compared with the solutions offered by four well-known techniques from the subject literature. Experimental results and statistical tests show that the new method possesses outstanding performance in terms of solution quality and runtime consumed. In addition, the proposed algorithm is suitable for solving large-scale problems.

  11. The application of neural networks with artificial intelligence technique in the modeling of industrial processes

    International Nuclear Information System (INIS)

    Saini, K. K.; Saini, Sanju

    2008-01-01

    Neural networks are a relatively new artificial intelligence technique that emulates the behavior of biological neural systems in digital software or hardware. These networks can 'learn', automatically, complex relationships among data. This feature makes the technique very useful in modeling processes for which mathematical modeling is difficult or impossible. The work described here outlines some examples of the application of neural networks with artificial intelligence technique in the modeling of industrial processes.

  12. 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.

  13. Advances in chaos theory and intelligent control

    CERN Document Server

    Vaidyanathan, Sundarapandian

    2016-01-01

    The book reports on the latest advances in and applications of chaos theory and intelligent control. Written by eminent scientists and active researchers and using a clear, matter-of-fact style, it covers advanced theories, methods, and applications in a variety of research areas, and explains key concepts in modeling, analysis, and control of chaotic and hyperchaotic systems. Topics include fractional chaotic systems, chaos control, chaos synchronization, memristors, jerk circuits, chaotic systems with hidden attractors, mechanical and biological chaos, and circuit realization of chaotic systems. The book further covers fuzzy logic controllers, evolutionary algorithms, swarm intelligence, and petri nets among other topics. Not only does it provide the readers with chaos fundamentals and intelligent control-based algorithms; it also discusses key applications of chaos as well as multidisciplinary solutions developed via intelligent control. The book is a timely and comprehensive reference guide for graduate s...

  14. 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

  15. Intelligence systems in environmental management theory and applications

    CERN Document Server

    Sari, İrem

    2017-01-01

    This book offers a comprehensive reference guide to intelligence systems in environmental management. It provides readers with all the necessary tools for solving complex environmental problems, where classical techniques cannot be applied. The respective chapters, written by prominent researchers, explain a wealth of both basic and advanced concepts including ant colony, genetic algorithms, evolutionary algorithms, fuzzy multi-criteria decision making tools, particle swarm optimization, agent-based modelling, artificial neural networks, simulated annealing, Tabu search, fuzzy multi-objective optimization, fuzzy rules, support vector machines, fuzzy cognitive maps, cumulative belief degrees, and many others. To foster a better understanding, all the chapters include relevant numerical examples or case studies. Taken together, they form an excellent reference guide for researchers, lecturers and postgraduate students pursuing research on complex environmental problems. Moreover, by extending all the main aspec...

  16. Diagnosis of class using swarm intelligence applied to problem of identification of nuclear transient

    Energy Technology Data Exchange (ETDEWEB)

    Villas Boas Junior, Manoel; Strauss, Edilberto, E-mail: junior@lmp.ufrj.b [Instituto Federal de Educacao, Ciencia e Tecnologia do Ceara/ Universidade do Estado do Ceara, Itaperi, CE (Brazil). Mestrado Integrado em Computacao Aplicada; Nicolau, Andressa dos Santos; Schirru, Roberto, E-mail: andressa@lmp.ufrj.b [Coordenacao dos Programas de Pos-Graduacao de Engenharia (PEN/COPPE/UFRJ), Rio de Janeiro, RJ (Brazil). Programa de Engenharia Nuclear; Mello, Flavio Luis de [Universidade Federal do Rio de Janeiro (POLI/UFRJ), RJ (Brazil). Escola Politecnica. Dept. de Engenharia Eletronica e Computacao

    2011-07-01

    This article presents a computational model of the diagnostic system of transient. The model makes use of segmentation techniques applied to support decision making, based on identification of classes and optimized by Particle Swarm Optimization algorithm (PSO). The method proposed aims to classify an anomalous event in the signatures of three classes of the design basis transients postulated for the Angra 2 nuclear plant, where the PSO algorithm is used as a method of separation of classes, being responsible for finding the best centroid prototype vector of each accident/transient, ie equivalent to Voronoi vector that maximizes the number of correct classifications. To make the calculation of similarity between the set of the variables anomalous event in a given time t, and the prototype vector of variables of accident/transients, the metrics of Manhattan, Euclidean and Minkowski were used. The results obtained by the method proposed were compatible with others methods reported in the literature, allowing a solution that approximates the ideal solution, ie the Voronoi vectors. (author)

  17. Multipeak Mean Based Optimized Histogram Modification Framework Using Swarm Intelligence for Image Contrast Enhancement

    Directory of Open Access Journals (Sweden)

    P. Babu

    2015-01-01

    Full Text Available A novel approach, Multipeak mean based optimized histogram modification framework (MMOHM is introduced for the purpose of enhancing the contrast as well as preserving essential details for any given gray scale and colour images. The basic idea of this technique is the calculation of multiple peaks (local maxima from the original histogram. The mean value of multiple peaks is computed and the input image’s histogram is segmented into two subhistograms based on this multipeak mean (mmean value. Then, a bicriteria optimization problem is formulated and the subhistograms are modified by selecting optimal contrast enhancement parameters. While formulating the enhancement parameters, particle swarm optimization is employed to find optimal values of them. Finally, the union of the modified subhistograms produces a contrast enhanced and details preserved output image. This mechanism enhances the contrast of the input image better than the existing contemporary HE methods. The performance of the proposed method is well supported by the contrast enhancement quantitative metrics such as discrete entropy, natural image quality evaluator, and absolute mean brightness error.

  18. Diagnosis of class using swarm intelligence applied to problem of identification of nuclear transient

    International Nuclear Information System (INIS)

    Villas Boas Junior, Manoel; Strauss, Edilberto; Nicolau, Andressa dos Santos; Schirru, Roberto; Mello, Flavio Luis de

    2011-01-01

    This article presents a computational model of the diagnostic system of transient. The model makes use of segmentation techniques applied to support decision making, based on identification of classes and optimized by Particle Swarm Optimization algorithm (PSO). The method proposed aims to classify an anomalous event in the signatures of three classes of the design basis transients postulated for the Angra 2 nuclear plant, where the PSO algorithm is used as a method of separation of classes, being responsible for finding the best centroid prototype vector of each accident/transient, ie equivalent to Voronoi vector that maximizes the number of correct classifications. To make the calculation of similarity between the set of the variables anomalous event in a given time t, and the prototype vector of variables of accident/transients, the metrics of Manhattan, Euclidean and Minkowski were used. The results obtained by the method proposed were compatible with others methods reported in the literature, allowing a solution that approximates the ideal solution, ie the Voronoi vectors. (author)

  19. Automation of fusion first wall design using artificial intelligence technique

    International Nuclear Information System (INIS)

    Yoshimura, Shinobu; Yagawa, Genki; Mochizuki, Yoshihiko

    1990-01-01

    This paper describes the application of artificial intelligence techniques to a design automation of the fusion first wall to be operated in the complex environment where huge electromagnetic and thermal loading as well as heavy neutron irradiation occur. As a basic strategy of designing structure shape considering many coupled phenomena, an ordinary design procedure based on the generate and test strategy is adopted because of its simplicity and broad applicability. To automate the design procedure with maintaining its flexibility, extensibility and efficiency, artificial intelligence techniques are utilized in the following. An object-oriented knowledge representation technique is adopted to store knowledge modules, that is, objects, related to the first wall design, while a data-flow processing technique is utilized as an inference mechanism among the knowledge modules. These techniques realize the flexibility and extensibility of the system. Moreover, as an efficient design modification mechanism, which is essential in a design process, an empirical approach based on experts' empirical knowledge and a mathematical approach based on a kind of numerical sensitivity analysis are introduced. The developed system is applied to a simple example of the design of a two-dimensional model of the first wall with a cooling channel, and its fundamental performance is clearly demonstrated. (author)

  20. An empirical study on collective intelligence algorithms for video games problem-solving

    OpenAIRE

    González-Pardo, Antonio; Palero, Fernando; Camacho, David

    2015-01-01

    Computational intelligence (CI), such as evolutionary computation or swarm intelligence methods, is a set of bio-inspired algorithms that have been widely used to solve problems in areas like planning, scheduling or constraint satisfaction problems. Constrained satisfaction problems (CSP) have taken an important attention from the research community due to their applicability to real problems. Any CSP problem is usually modelled as a constrained graph where the edges represent a set of restri...

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

    International Nuclear Information System (INIS)

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

    2012-01-01

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

  2. 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.

  3. A Review of Particle Swarm Optimization

    Science.gov (United States)

    Jain, N. K.; Nangia, Uma; Jain, Jyoti

    2018-03-01

    This paper presents an overview of the research progress in Particle Swarm Optimization (PSO) during 1995-2017. Fifty two papers have been reviewed. They have been categorized into nine categories based on various aspects. This technique has attracted many researchers because of its simplicity which led to many improvements and modifications of the basic PSO. Some researchers carried out the hybridization of PSO with other evolutionary techniques. This paper discusses the progress of PSO, its improvements, modifications and applications.

  4. Artificial Intelligence techniques for big data analysis

    OpenAIRE

    Aditya Khatri

    2017-01-01

    During my stay in Salamanca (Spain), I was fortunate enough to participate in the BISITE Research Group of the University of Salamanca. The University of Salamanca is the oldest university in Spain and in 2018 it celebrates its 8th centenary. As a computer science researcher, I participated in one of the many international projects that the research group has active, especially in big data analysis using Artificial Intelligence (AI) techniques. AI is one of BISITE's main lines of rese...

  5. Particle swarm as optimization tool in complex nuclear engineering problems

    International Nuclear Information System (INIS)

    Medeiros, Jose Antonio Carlos Canedo

    2005-06-01

    Due to its low computational cost, gradient-based search techniques associated to linear programming techniques are being used as optimization tools. These techniques, however, when applied to multimodal search spaces, can lead to local optima. When finding solutions for complex multimodal domains, random search techniques are being used with great efficacy. In this work we exploit the swarm optimization algorithm search power capacity as an optimization tool for the solution of complex high dimension and multimodal search spaces of nuclear problems. Due to its easy and natural representation of high dimension domains, the particle swarm optimization was applied with success for the solution of complex nuclear problems showing its efficacy in the search of solutions in high dimension and complex multimodal spaces. In one of these applications it enabled a natural and trivial solution in a way not obtained with other methods confirming the validity of its application. (author)

  6. Improved particle swarm optimization combined with chaos

    International Nuclear Information System (INIS)

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

    2005-01-01

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

  7. 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.

  8. 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

  9. 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

  10. 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...

  11. 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.

  12. 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...

  13. 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.

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

    Directory of Open Access Journals (Sweden)

    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.

  15. 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.

  16. Air Quality Forecasting through Different Statistical and Artificial Intelligence Techniques

    Science.gov (United States)

    Mishra, D.; Goyal, P.

    2014-12-01

    Urban air pollution forecasting has emerged as an acute problem in recent years because there are sever environmental degradation due to increase in harmful air pollutants in the ambient atmosphere. In this study, there are different types of statistical as well as artificial intelligence techniques are used for forecasting and analysis of air pollution over Delhi urban area. These techniques are principle component analysis (PCA), multiple linear regression (MLR) and artificial neural network (ANN) and the forecasting are observed in good agreement with the observed concentrations through Central Pollution Control Board (CPCB) at different locations in Delhi. But such methods suffers from disadvantages like they provide limited accuracy as they are unable to predict the extreme points i.e. the pollution maximum and minimum cut-offs cannot be determined using such approach. Also, such methods are inefficient approach for better output forecasting. But with the advancement in technology and research, an alternative to the above traditional methods has been proposed i.e. the coupling of statistical techniques with artificial Intelligence (AI) can be used for forecasting purposes. The coupling of PCA, ANN and fuzzy logic is used for forecasting of air pollutant over Delhi urban area. The statistical measures e.g., correlation coefficient (R), normalized mean square error (NMSE), fractional bias (FB) and index of agreement (IOA) of the proposed model are observed in better agreement with the all other models. Hence, the coupling of statistical and artificial intelligence can be use for the forecasting of air pollutant over urban area.

  17. 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.

  18. On the role of the plasmodial cytoskeleton in facilitating intelligent behavior in slime mold Physarum polycephalum

    Science.gov (United States)

    Mayne, Richard; Adamatzky, Andrew; Jones, Jeff

    2015-01-01

    The plasmodium of slime mold Physarum polycephalum behaves as an amorphous reaction-diffusion computing substrate and is capable of apparently ‘intelligent’ behavior. But how does intelligence emerge in an acellular organism? Through a range of laboratory experiments, we visualize the plasmodial cytoskeleton—a ubiquitous cellular protein scaffold whose functions are manifold and essential to life—and discuss its putative role as a network for transducing, transmitting and structuring data streams within the plasmodium. Through a range of computer modeling techniques, we demonstrate how emergent behavior, and hence computational intelligence, may occur in cytoskeletal communications networks. Specifically, we model the topology of both the actin and tubulin cytoskeletal networks and discuss how computation may occur therein. Furthermore, we present bespoke cellular automata and particle swarm models for the computational process within the cytoskeleton and observe the incidence of emergent patterns in both. Our work grants unique insight into the origins of natural intelligence; the results presented here are therefore readily transferable to the fields of natural computation, cell biology and biomedical science. We conclude by discussing how our results may alter our biological, computational and philosophical understanding of intelligence and consciousness. PMID:26478782

  19. 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

  20. 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.

  1. Implementation and Validation of Artificial Intelligence Techniques for Robotic Surgery

    OpenAIRE

    Aarshay Jain; Deepansh Jagotra; Vijayant Agarwal

    2014-01-01

    The primary focus of this study is implementation of Artificial Intelligence (AI) technique for developing an inverse kinematics solution for the Raven-IITM surgical research robot [1]. First, the kinematic model of the Raven-IITM robot was analysed along with the proposed analytical solution [2] for inverse kinematics problem. Next, The Artificial Neural Network (ANN) techniques was implemented. The training data for the same was careful selected by keeping manipulability constraints in mind...

  2. The Application of Collaborative Business Intelligence Technology in the Hospital SPD Logistics Management Model

    Science.gov (United States)

    LIU, Tongzhu; SHEN, Aizong; HU, Xiaojian; TONG, Guixian; GU, Wei

    2017-01-01

    Background: We aimed to apply collaborative business intelligence (BI) system to hospital supply, processing and distribution (SPD) logistics management model. Methods: We searched Engineering Village database, China National Knowledge Infrastructure (CNKI) and Google for articles (Published from 2011 to 2016), books, Web pages, etc., to understand SPD and BI related theories and recent research status. For the application of collaborative BI technology in the hospital SPD logistics management model, we realized this by leveraging data mining techniques to discover knowledge from complex data and collaborative techniques to improve the theories of business process. Results: For the application of BI system, we: (i) proposed a layered structure of collaborative BI system for intelligent management in hospital logistics; (ii) built data warehouse for the collaborative BI system; (iii) improved data mining techniques such as supporting vector machines (SVM) and swarm intelligence firefly algorithm to solve key problems in hospital logistics collaborative BI system; (iv) researched the collaborative techniques oriented to data and business process optimization to improve the business processes of hospital logistics management. Conclusion: Proper combination of SPD model and BI system will improve the management of logistics in the hospitals. The successful implementation of the study requires: (i) to innovate and improve the traditional SPD model and make appropriate implement plans and schedules for the application of BI system according to the actual situations of hospitals; (ii) the collaborative participation of internal departments in hospital including the department of information, logistics, nursing, medical and financial; (iii) timely response of external suppliers. PMID:28828316

  3. 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

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

    Directory of Open Access Journals (Sweden)

    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.

  5. 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)

  6. 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.)

  7. Research on UAV Intelligent Obstacle Avoidance Technology During Inspection of Transmission Line

    Science.gov (United States)

    Wei, Chuanhu; Zhang, Fei; Yin, Chaoyuan; Liu, Yue; Liu, Liang; Li, Zongyu; Wang, Wanguo

    Autonomous obstacle avoidance of unmanned aerial vehicle (hereinafter referred to as UAV) in electric power line inspection process has important significance for operation safety and economy for UAV intelligent inspection system of transmission line as main content of UAV intelligent inspection system on transmission line. In the paper, principles of UAV inspection obstacle avoidance technology of transmission line are introduced. UAV inspection obstacle avoidance technology based on particle swarm global optimization algorithm is proposed after common obstacle avoidance technologies are studied. Stimulation comparison is implemented with traditional UAV inspection obstacle avoidance technology which adopts artificial potential field method. Results show that UAV inspection strategy of particle swarm optimization algorithm, adopted in the paper, is prominently better than UAV inspection strategy of artificial potential field method in the aspects of obstacle avoidance effect and the ability of returning to preset inspection track after passing through the obstacle. An effective method is provided for UAV inspection obstacle avoidance of transmission line.

  8. Intelligent Techniques Using Molecular Data Analysis in Leukaemia: An Opportunity for Personalized Medicine Support System.

    Science.gov (United States)

    Banjar, Haneen; Adelson, David; Brown, Fred; Chaudhri, Naeem

    2017-01-01

    The use of intelligent techniques in medicine has brought a ray of hope in terms of treating leukaemia patients. Personalized treatment uses patient's genetic profile to select a mode of treatment. This process makes use of molecular technology and machine learning, to determine the most suitable approach to treating a leukaemia patient. Until now, no reviews have been published from a computational perspective concerning the development of personalized medicine intelligent techniques for leukaemia patients using molecular data analysis. This review studies the published empirical research on personalized medicine in leukaemia and synthesizes findings across studies related to intelligence techniques in leukaemia, with specific attention to particular categories of these studies to help identify opportunities for further research into personalized medicine support systems in chronic myeloid leukaemia. A systematic search was carried out to identify studies using intelligence techniques in leukaemia and to categorize these studies based on leukaemia type and also the task, data source, and purpose of the studies. Most studies used molecular data analysis for personalized medicine, but future advancement for leukaemia patients requires molecular models that use advanced machine-learning methods to automate decision-making in treatment management to deliver supportive medical information to the patient in clinical practice.

  9. Artificial intelligence techniques applied to hourly global irradiance estimation from satellite-derived cloud index

    Energy Technology Data Exchange (ETDEWEB)

    Zarzalejo, L.F.; Ramirez, L.; Polo, J. [DER-CIEMAT, Madrid (Spain). Renewable Energy Dept.

    2005-07-01

    Artificial intelligence techniques, such as fuzzy logic and neural networks, have been used for estimating hourly global radiation from satellite images. The models have been fitted to measured global irradiance data from 15 Spanish terrestrial stations. Both satellite imaging data and terrestrial information from the years 1994, 1995 and 1996 were used. The results of these artificial intelligence models were compared to a multivariate regression based upon Heliosat I model. A general better behaviour was observed for the artificial intelligence models. (author)

  10. Artificial intelligence techniques applied to hourly global irradiance estimation from satellite-derived cloud index

    International Nuclear Information System (INIS)

    Zarzalejo, Luis F.; Ramirez, Lourdes; Polo, Jesus

    2005-01-01

    Artificial intelligence techniques, such as fuzzy logic and neural networks, have been used for estimating hourly global radiation from satellite images. The models have been fitted to measured global irradiance data from 15 Spanish terrestrial stations. Both satellite imaging data and terrestrial information from the years 1994, 1995 and 1996 were used. The results of these artificial intelligence models were compared to a multivariate regression based upon Heliosat I model. A general better behaviour was observed for the artificial intelligence models

  11. Adaptation and hybridization in computational intelligence

    CERN Document Server

    Jr, Iztok

    2015-01-01

      This carefully edited book takes a walk through recent advances in adaptation and hybridization in the Computational Intelligence (CI) domain. It consists of ten chapters that are divided into three parts. The first part illustrates background information and provides some theoretical foundation tackling the CI domain, the second part deals with the adaptation in CI algorithms, while the third part focuses on the hybridization in CI. This book can serve as an ideal reference for researchers and students of computer science, electrical and civil engineering, economy, and natural sciences that are confronted with solving the optimization, modeling and simulation problems. It covers the recent advances in CI that encompass Nature-inspired algorithms, like Artificial Neural networks, Evolutionary Algorithms and Swarm Intelligence –based algorithms.  

  12. Artificial Intelligence techniques for mission planning for mobile robots

    International Nuclear Information System (INIS)

    Martinez, J.M.; Nomine, J.P.

    1990-01-01

    This work focuses on Spatial Modelization Techniques and on Control Software Architectures, in order to deal efficiently with the Navigation and Perception problems encountered in Mobile Autonomous Robotics. After a brief survey of the current various approaches for these techniques, we expose ongoing simulation works for a specific mission in robotics. Studies in progress used for Spatial Reasoning are based on new approaches combining Artificial Intelligence and Geometrical techniques. These methods deal with the problem of environment modelization using three types of models: geometrical topological and semantic models at different levels. The decision making processes of control are presented as the result of cooperation between a group of decentralized agents that communicate by sending messages. (author)

  13. Fuzzy forecasting based on two-factors second-order fuzzy-trend logical relationship groups and particle swarm optimization techniques.

    Science.gov (United States)

    Chen, Shyi-Ming; Manalu, Gandhi Maruli Tua; Pan, Jeng-Shyang; Liu, Hsiang-Chuan

    2013-06-01

    In this paper, we present a new method for fuzzy forecasting based on two-factors second-order fuzzy-trend logical relationship groups and particle swarm optimization (PSO) techniques. First, we fuzzify the historical training data of the main factor and the secondary factor, respectively, to form two-factors second-order fuzzy logical relationships. Then, we group the two-factors second-order fuzzy logical relationships into two-factors second-order fuzzy-trend logical relationship groups. Then, we obtain the optimal weighting vector for each fuzzy-trend logical relationship group by using PSO techniques to perform the forecasting. We also apply the proposed method to forecast the Taiwan Stock Exchange Capitalization Weighted Stock Index and the NTD/USD exchange rates. The experimental results show that the proposed method gets better forecasting performance than the existing methods.

  14. Virtual Enterprise Risk Management Using Artificial Intelligence

    Directory of Open Access Journals (Sweden)

    Hanning Chen

    2010-01-01

    Full Text Available Virtual enterprise (VE has to manage its risk effectively in order to guarantee the profit. However, restricting the risk in a VE to the acceptable level is considered difficult due to the agility and diversity of its distributed characteristics. First, in this paper, an optimization model for VE risk management based on distributed decision making model is introduced. This optimization model has two levels, namely, the top model and the base model, which describe the decision processes of the owner and the partners of the VE, respectively. In order to solve the proposed model effectively, this work then applies two powerful artificial intelligence optimization techniques known as evolutionary algorithms (EA and swarm intelligence (SI. Experiments present comparative studies on the VE risk management problem for one EA and three state-of-the-art SI algorithms. All of the algorithms are evaluated against a test scenario, in which the VE is constructed by one owner and different partners. The simulation results show that the PS2O algorithm, which is a recently developed SI paradigm simulating symbiotic coevolution behavior in nature, obtains the superior solution for VE risk management problem than the other algorithms in terms of optimization accuracy and computation robustness.

  15. 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.

  16. 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)

  17. Proceedings of the International Conference on Information Systems Design and Intelligent Applications 2012

    CERN Document Server

    Avadhani, P; Abraham, Ajith

    2012-01-01

    This volume contains the papers presented at INDIA-2012: International conference on  Information system Design and Intelligent Applications held on January 5-7, 2012 in Vishakhapatnam, India. This conference was organized by Computer Society of India (CSI), Vishakhapatnam chapter well supported by Vishakhapatnam Steel, RINL, Govt of India. It contains 108 papers contributed by authors from six different countries across four continents. These research papers mainly focused on intelligent applications and various system design issues. The papers cover a wide range of topics of computer science and information technology discipline ranging from image processing, data base application, data mining, grid and cloud computing, bioinformatics among many others. The various intelligent tools like swarm intelligence, artificial intelligence, evolutionary algorithms, bio-inspired algorithms have been applied in different papers for solving various challenging IT related problems.

  18. 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

  19. Artificial intelligence techniques for scheduling Space Shuttle missions

    Science.gov (United States)

    Henke, Andrea L.; Stottler, Richard H.

    1994-01-01

    Planning and scheduling of NASA Space Shuttle missions is a complex, labor-intensive process requiring the expertise of experienced mission planners. We have developed a planning and scheduling system using combinations of artificial intelligence knowledge representations and planning techniques to capture mission planning knowledge and automate the multi-mission planning process. Our integrated object oriented and rule-based approach reduces planning time by orders of magnitude and provides planners with the flexibility to easily modify planning knowledge and constraints without requiring programming expertise.

  20. Artificial intelligence techniques for embryo and oocyte classification.

    Science.gov (United States)

    Manna, Claudio; Nanni, Loris; Lumini, Alessandra; Pappalardo, Sebastiana

    2013-01-01

    One of the most relevant aspects in assisted reproduction technology is the possibility of characterizing and identifying the most viable oocytes or embryos. In most cases, embryologists select them by visual examination and their evaluation is totally subjective. Recently, due to the rapid growth in the capacity to extract texture descriptors from a given image, a growing interest has been shown in the use of artificial intelligence methods for embryo or oocyte scoring/selection in IVF programmes. This work concentrates the efforts on the possible prediction of the quality of embryos and oocytes in order to improve the performance of assisted reproduction technology, starting from their images. The artificial intelligence system proposed in this work is based on a set of Levenberg-Marquardt neural networks trained using textural descriptors (the local binary patterns). The proposed system was tested on two data sets of 269 oocytes and 269 corresponding embryos from 104 women and compared with other machine learning methods already proposed in the past for similar classification problems. Although the results are only preliminary, they show an interesting classification performance. This technique may be of particular interest in those countries where legislation restricts embryo selection. One of the most relevant aspects in assisted reproduction technology is the possibility of characterizing and identifying the most viable oocytes or embryos. In most cases, embryologists select them by visual examination and their evaluation is totally subjective. Recently, due to the rapid growth in our capacity to extract texture descriptors from a given image, a growing interest has been shown in the use of artificial intelligence methods for embryo or oocyte scoring/selection in IVF programmes. In this work, we concentrate our efforts on the possible prediction of the quality of embryos and oocytes in order to improve the performance of assisted reproduction technology

  1. Short-term Local Forecasting by Artificial Intelligence Techniques and Assess Related Social Effects from Heterogeneous Data

    OpenAIRE

    Gong, Bing

    2017-01-01

    This work aims to use the sophisticated artificial intelligence and statistic techniques to forecast pollution and assess its social impact. To achieve the target of the research, this study is divided into several research sub-objectives as follows: First research sub-objective: propose a framework for relocating and reconfiguring the existing pollution monitoring networks by using feature selection, artificial intelligence techniques, and information theory. Second research sub-objective: c...

  2. Identification of transformer fault based on dissolved gas analysis using hybrid support vector machine-modified evolutionary particle swarm optimisation

    Science.gov (United States)

    2018-01-01

    Early detection of power transformer fault is important because it can reduce the maintenance cost of the transformer and it can ensure continuous electricity supply in power systems. Dissolved Gas Analysis (DGA) technique is commonly used to identify oil-filled power transformer fault type but utilisation of artificial intelligence method with optimisation methods has shown convincing results. In this work, a hybrid support vector machine (SVM) with modified evolutionary particle swarm optimisation (EPSO) algorithm was proposed to determine the transformer fault type. The superiority of the modified PSO technique with SVM was evaluated by comparing the results with the actual fault diagnosis, unoptimised SVM and previous reported works. Data reduction was also applied using stepwise regression prior to the training process of SVM to reduce the training time. It was found that the proposed hybrid SVM-Modified EPSO (MEPSO)-Time Varying Acceleration Coefficient (TVAC) technique results in the highest correct identification percentage of faults in a power transformer compared to other PSO algorithms. Thus, the proposed technique can be one of the potential solutions to identify the transformer fault type based on DGA data on site. PMID:29370230

  3. Identification of transformer fault based on dissolved gas analysis using hybrid support vector machine-modified evolutionary particle swarm optimisation.

    Directory of Open Access Journals (Sweden)

    Hazlee Azil Illias

    Full Text Available Early detection of power transformer fault is important because it can reduce the maintenance cost of the transformer and it can ensure continuous electricity supply in power systems. Dissolved Gas Analysis (DGA technique is commonly used to identify oil-filled power transformer fault type but utilisation of artificial intelligence method with optimisation methods has shown convincing results. In this work, a hybrid support vector machine (SVM with modified evolutionary particle swarm optimisation (EPSO algorithm was proposed to determine the transformer fault type. The superiority of the modified PSO technique with SVM was evaluated by comparing the results with the actual fault diagnosis, unoptimised SVM and previous reported works. Data reduction was also applied using stepwise regression prior to the training process of SVM to reduce the training time. It was found that the proposed hybrid SVM-Modified EPSO (MEPSO-Time Varying Acceleration Coefficient (TVAC technique results in the highest correct identification percentage of faults in a power transformer compared to other PSO algorithms. Thus, the proposed technique can be one of the potential solutions to identify the transformer fault type based on DGA data on site.

  4. Identification of transformer fault based on dissolved gas analysis using hybrid support vector machine-modified evolutionary particle swarm optimisation.

    Science.gov (United States)

    Illias, Hazlee Azil; Zhao Liang, Wee

    2018-01-01

    Early detection of power transformer fault is important because it can reduce the maintenance cost of the transformer and it can ensure continuous electricity supply in power systems. Dissolved Gas Analysis (DGA) technique is commonly used to identify oil-filled power transformer fault type but utilisation of artificial intelligence method with optimisation methods has shown convincing results. In this work, a hybrid support vector machine (SVM) with modified evolutionary particle swarm optimisation (EPSO) algorithm was proposed to determine the transformer fault type. The superiority of the modified PSO technique with SVM was evaluated by comparing the results with the actual fault diagnosis, unoptimised SVM and previous reported works. Data reduction was also applied using stepwise regression prior to the training process of SVM to reduce the training time. It was found that the proposed hybrid SVM-Modified EPSO (MEPSO)-Time Varying Acceleration Coefficient (TVAC) technique results in the highest correct identification percentage of faults in a power transformer compared to other PSO algorithms. Thus, the proposed technique can be one of the potential solutions to identify the transformer fault type based on DGA data on site.

  5. 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

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

    International Nuclear Information System (INIS)

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

    2015-01-01

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

  7. Inter-cooperative collective intelligence techniques and applications

    CERN Document Server

    Bessis, Nik

    2014-01-01

    This book covers the latest advances in the rapid growing field of inter-cooperative collective intelligence aiming the integration and cooperation of various computational resources, networks and intelligent processing paradigms to collectively build intelligence and advanced decision support and interfaces for end-users. The book brings a comprehensive view of the state-of-the-art in the field of integration of sensor networks, IoT and Cloud computing, massive and intelligent querying and processing of data. As a result, the book presents lessons learned so far and identifies new research issues, challenges and opportunities for further research and development agendas. Emerging areas of applications are also identified and usefulness of inter-cooperative collective intelligence is envisaged.   Researchers, software developers, practitioners and students interested in the field of inter-cooperative collective intelligence will find the comprehensive coverage of this book useful for their research, academic...

  8. Adaptive swarm cluster-based dynamic multi-objective synthetic minority oversampling technique algorithm for tackling binary imbalanced datasets in biomedical data classification.

    Science.gov (United States)

    Li, Jinyan; Fong, Simon; Sung, Yunsick; Cho, Kyungeun; Wong, Raymond; Wong, Kelvin K L

    2016-01-01

    An imbalanced dataset is defined as a training dataset that has imbalanced proportions of data in both interesting and uninteresting classes. Often in biomedical applications, samples from the stimulating class are rare in a population, such as medical anomalies, positive clinical tests, and particular diseases. Although the target samples in the primitive dataset are small in number, the induction of a classification model over such training data leads to poor prediction performance due to insufficient training from the minority class. In this paper, we use a novel class-balancing method named adaptive swarm cluster-based dynamic multi-objective synthetic minority oversampling technique (ASCB_DmSMOTE) to solve this imbalanced dataset problem, which is common in biomedical applications. The proposed method combines under-sampling and over-sampling into a swarm optimisation algorithm. It adaptively selects suitable parameters for the rebalancing algorithm to find the best solution. Compared with the other versions of the SMOTE algorithm, significant improvements, which include higher accuracy and credibility, are observed with ASCB_DmSMOTE. Our proposed method tactfully combines two rebalancing techniques together. It reasonably re-allocates the majority class in the details and dynamically optimises the two parameters of SMOTE to synthesise a reasonable scale of minority class for each clustered sub-imbalanced dataset. The proposed methods ultimately overcome other conventional methods and attains higher credibility with even greater accuracy of the classification model.

  9. 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...

  10. 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.

  11. 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).

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

    International Nuclear Information System (INIS)

    Jiang, Hui; Michette, Alan G.

    2013-01-01

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

  13. 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.

  14. Intelligent techniques in signal processing for multimedia security

    CERN Document Server

    Santhi, V

    2017-01-01

    This book proposes new algorithms to ensure secured communications and prevent unauthorized data exchange in secured multimedia systems. Focusing on numerous applications’ algorithms and scenarios, it offers an in-depth analysis of data hiding technologies including watermarking, cryptography, encryption, copy control, and authentication. The authors present a framework for visual data hiding technologies that resolves emerging problems of modern multimedia applications in several contexts including the medical, healthcare, education, and wireless communication networking domains. Further, it introduces several intelligent security techniques with real-time implementation. As part of its comprehensive coverage, the book discusses contemporary multimedia authentication and fingerprinting techniques, while also proposing personal authentication/recognition systems based on hand images, surveillance system security using gait recognition, face recognition under restricted constraints such as dry/wet face condi...

  15. 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)

  16. 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...

  17. 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 ...

  18. 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...

  19. 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.

  20. 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

  1. Automatized Parameterization of DFTB Using Particle Swarm Optimization.

    Science.gov (United States)

    Chou, Chien-Pin; Nishimura, Yoshifumi; Fan, Chin-Chai; Mazur, Grzegorz; Irle, Stephan; Witek, Henryk A

    2016-01-12

    We present a novel density-functional tight-binding (DFTB) parametrization toolkit developed to optimize the parameters of various DFTB models in a fully automatized fashion. The main features of the algorithm, based on the particle swarm optimization technique, are discussed, and a number of initial pilot applications of the developed methodology to molecular and solid systems are presented.

  2. 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.

  3. On the spatial dynamics and oscillatory behavior of a predator-prey model based on cellular automata and local particle swarm optimization.

    Science.gov (United States)

    Molina, Mario Martínez; Moreno-Armendáriz, Marco A; Carlos Seck Tuoh Mora, Juan

    2013-11-07

    A two-dimensional lattice model based on Cellular Automata theory and swarm intelligence is used to study the spatial and population dynamics of a theoretical ecosystem. It is found that the social interactions among predators provoke the formation of clusters, and that by increasing the mobility of predators the model enters into an oscillatory behavior. © 2013 Elsevier Ltd. All rights reserved.

  4. Utilization of artificial intelligence techniques for the Space Station power system

    Science.gov (United States)

    Evatt, Thomas C.; Gholdston, Edward W.

    1988-01-01

    Due to the complexity of the Space Station Electrical Power System (EPS) as currently envisioned, artificial intelligence/expert system techniques are being investigated to automate operations, maintenance, and diagnostic functions. A study was conducted to investigate this technology as it applies to failure detection, isolation, and reconfiguration (FDIR) and health monitoring of power system components and of the total system. Control system utilization of expert systems for load scheduling and shedding operations was also researched. A discussion of the utilization of artificial intelligence/expert systems for Initial Operating Capability (IOC) for the Space Station effort is presented along with future plans at Rocketdyne for the utilization of this technology for enhanced Space Station power capability.

  5. 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.

  6. 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....

  7. 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...

  8. Express: the reliability of complex systems and artificial intelligence techniques

    International Nuclear Information System (INIS)

    Ancelin, C.; Le, P.; Saint-Quentin, S. de

    1987-01-01

    The probabilistic safety study for the Paluel nuclear power station, commissioned by EDF in 1986, involved development of data processing methods and equipment which was to be given an entirely new impetus by the use of artificial intelligence techniques. The authors describe the salient features of the approach which was adopted and the lessons learnt from the way it was applied in practice [fr

  9. Operation optimization of distributed generation using artificial intelligent techniques

    Directory of Open Access Journals (Sweden)

    Mahmoud H. Elkazaz

    2016-06-01

    Full Text Available Future smart grids will require an observable, controllable and flexible network architecture for reliable and efficient energy delivery. The use of artificial intelligence and advanced communication technologies is essential in building a fully automated system. This paper introduces a new technique for online optimal operation of distributed generation (DG resources, i.e. a hybrid fuel cell (FC and photovoltaic (PV system for residential applications. The proposed technique aims to minimize the total daily operating cost of a group of residential homes by managing the operation of embedded DG units remotely from a control centre. The target is formed as an objective function that is solved using genetic algorithm (GA optimization technique. The optimal settings of the DG units obtained from the optimization process are sent to each DG unit through a fully automated system. The results show that the proposed technique succeeded in defining the optimal operating points of the DGs that affect directly the total operating cost of the entire system.

  10. 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.

  11. Brain Emotional Learning Based Intelligent Decoupler for Nonlinear Multi-Input Multi-Output Distillation Columns

    Directory of Open Access Journals (Sweden)

    M. H. El-Saify

    2017-01-01

    Full Text Available The distillation process is vital in many fields of chemical industries, such as the two-coupled distillation columns that are usually highly nonlinear Multi-Input Multi-Output (MIMO coupled processes. The control of MIMO process is usually implemented via a decentralized approach using a set of Single-Input Single-Output (SISO loop controllers. Decoupling the MIMO process into group of single loops requires proper input-output pairing and development of decoupling compensator unit. This paper proposes a novel intelligent decoupling approach for MIMO processes based on new MIMO brain emotional learning architecture. A MIMO architecture of Brain Emotional Learning Based Intelligent Controller (BELBIC is developed and applied as a decoupler for 4 input/4 output highly nonlinear coupled distillation columns process. Moreover, the performance of the proposed Brain Emotional Learning Based Intelligent Decoupler (BELBID is enhanced using Particle Swarm Optimization (PSO technique. The performance is compared with the PSO optimized steady state decoupling compensation matrix. Mathematical models of the distillation columns and the decouplers are built and tested in simulation environment by applying the same inputs. The results prove remarkable success of the BELBID in minimizing the loops interactions without degrading the output that every input has been paired with.

  12. 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.

  13. 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

  14. Application of the Particle Swarm Optimization (PSO) technique to the thermal-hydraulics project of a PWR reactor core in reduced scale

    International Nuclear Information System (INIS)

    Lima Junior, Carlos Alberto de Souza

    2008-09-01

    The reduced scale models design have been employed by engineers from several different industries fields such as offshore, spatial, oil extraction, nuclear industries and others. Reduced scale models are used in experiments because they are economically attractive than its own prototype (real scale) because in many cases they are cheaper than a real scale one and most of time they are also easier to build providing a way to lead the real scale design allowing indirect investigations and analysis to the real scale system (prototype). A reduced scale model (or experiment) must be able to represent all physical phenomena that occurs and further will do in the real scale one under operational conditions, e.g., in this case the reduced scale model is called similar. There are some different methods to design a reduced scale model and from those two are basic: the empiric method based on the expert's skill to determine which physical measures are relevant to the desired model; and the differential equation method that is based on a mathematical description of the prototype (real scale system) to model. Applying a mathematical technique to the differential equation that describes the prototype then highlighting the relevant physical measures so the reduced scale model design problem may be treated as an optimization problem. Many optimization techniques as Genetic Algorithm (GA), for example, have been developed to solve this class of problems and have also been applied to the reduced scale model design problem as well. In this work, Particle Swarm Optimization (PSO) technique is investigated as an alternative optimization tool for such problem. In this investigation a computational approach, based on particle swarm optimization technique (PSO), is used to perform a reduced scale two loop Pressurized Water Reactor (PWR) core, considering 100% of nominal power operation on a forced flow cooling circulation and non-accidental operating conditions. A performance comparison

  15. 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.

  16. 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

  17. 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.

  18. 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)

  19. 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

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

    Science.gov (United States)

    Pozzobon, Victor; Perre, Patrick

    2018-01-21

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

  1. Assessing the Value of Structured Analytic Techniques in the U.S. Intelligence Community

    Science.gov (United States)

    2016-01-01

    Analytic Techniques, and Why Do Analysts Use Them? SATs are methods of organizing and stimulating thinking about intelligence problems. These methods... thinking ; and imaginative thinking techniques encourage new perspectives, insights, and alternative scenarios. Among the many SATs in use today, the...more transparent, so that other analysts and customers can bet - ter understand how the judgments were reached. SATs also facilitate group involvement

  2. 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

  3. The First Workshop on Artificial Intelligence Techniques for Ambient Intelligence (AITAmI '06)

    OpenAIRE

    Augusto, Juan Carlos; Shapiro, Daniel

    2007-01-01

    The first annual workshop on the role of AI in ambient intelligence was held in Riva de Garda, Italy, on August 29, 2006. The workshop was colocated with the European Conference on Artificial Intelligence (ECAI 2006). It provided an opportunity for researchers in a variety of AI subfields together with representatives of commercial interests to explore ambient intelligence technology and applications.

  4. 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...

  5. Optimization of DNA Sensor Model Based Nanostructured Graphene Using Particle Swarm Optimization Technique

    Directory of Open Access Journals (Sweden)

    Hediyeh Karimi

    2013-01-01

    Full Text Available It has been predicted that the nanomaterials of graphene will be among the candidate materials for postsilicon electronics due to their astonishing properties such as high carrier mobility, thermal conductivity, and biocompatibility. Graphene is a semimetal zero gap nanomaterial with demonstrated ability to be employed as an excellent candidate for DNA sensing. Graphene-based DNA sensors have been used to detect the DNA adsorption to examine a DNA concentration in an analyte solution. In particular, there is an essential need for developing the cost-effective DNA sensors holding the fact that it is suitable for the diagnosis of genetic or pathogenic diseases. In this paper, particle swarm optimization technique is employed to optimize the analytical model of a graphene-based DNA sensor which is used for electrical detection of DNA molecules. The results are reported for 5 different concentrations, covering a range from 0.01 nM to 500 nM. The comparison of the optimized model with the experimental data shows an accuracy of more than 95% which verifies that the optimized model is reliable for being used in any application of the graphene-based DNA sensor.

  6. Comparison of AI techniques to solve combined economic emission dispatch problem with line flow constraints

    Energy Technology Data Exchange (ETDEWEB)

    Jacob Raglend, I. [School of Electrical Sciences, Noorul Islam University, Kumaracoil 629 180 (India); Veeravalli, Sowjanya; Sailaja, Kasanur; Sudheera, B. [School of Electrical Sciences, Vellore Institute of Technology, Vellore 632 004 (India); Kothari, D.P. [FNAE, FNASC, SMIEEE, Vellore Institute of Technology University, Vellore 632 014 (India)

    2010-07-15

    A comparative study has been made on the solutions obtained using combined economic emission dispatch (CEED) problem considering line flow constraints using different intelligent techniques for the regulated power system to ensure a practical, economical and secure generation schedule. The objective of the paper is to minimize the total production cost of the power generation. Economic load dispatch (ELD) and economic emission dispatch (EED) have been applied to obtain optimal fuel cost of generating units. Combined economic emission dispatch (CEED) is obtained by considering both the economic and emission objectives. This bi-objective CEED problem is converted into single objective function using price penalty factor approach. In this paper, intelligent techniques such as genetic algorithm (GA), evolutionary programming (EP), particle swarm optimization (PSO), differential evolution (DE) are applied to obtain CEED solutions for the IEEE 30-bus system and 15-unit system. This proposed algorithm introduces an efficient CEED approach that obtains the minimum operating cost satisfying unit, emission and network constraints. The proposed algorithm has been tested on two sample systems viz the IEEE 30-bus system and a 15-unit system. The results obtained by the various artificial intelligent techniques are compared with respect to the solution time, total production cost and convergence criteria. The solutions obtained are quite encouraging and useful in the economic emission environment. The algorithm and simulation are carried out using Matlab software. (author)

  7. Comparison of AI techniques to solve combined economic emission dispatch problem with line flow constraints

    International Nuclear Information System (INIS)

    Jacob Raglend, I.; Veeravalli, Sowjanya; Sailaja, Kasanur; Sudheera, B.; Kothari, D.P.

    2010-01-01

    A comparative study has been made on the solutions obtained using combined economic emission dispatch (CEED) problem considering line flow constraints using different intelligent techniques for the regulated power system to ensure a practical, economical and secure generation schedule. The objective of the paper is to minimize the total production cost of the power generation. Economic load dispatch (ELD) and economic emission dispatch (EED) have been applied to obtain optimal fuel cost of generating units. Combined economic emission dispatch (CEED) is obtained by considering both the economic and emission objectives. This bi-objective CEED problem is converted into single objective function using price penalty factor approach. In this paper, intelligent techniques such as genetic algorithm (GA), evolutionary programming (EP), particle swarm optimization (PSO), differential evolution (DE) are applied to obtain CEED solutions for the IEEE 30-bus system and 15-unit system. This proposed algorithm introduces an efficient CEED approach that obtains the minimum operating cost satisfying unit, emission and network constraints. The proposed algorithm has been tested on two sample systems viz the IEEE 30-bus system and a 15-unit system. The results obtained by the various artificial intelligent techniques are compared with respect to the solution time, total production cost and convergence criteria. The solutions obtained are quite encouraging and useful in the economic emission environment. The algorithm and simulation are carried out using Matlab software. (author)

  8. 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

  9. Smart Collections: Can Artificial Intelligence Tools and Techniques Assist with Discovering, Evaluating and Tagging Digital Learning Resources?

    Science.gov (United States)

    Leibbrandt, Richard; Yang, Dongqiang; Pfitzner, Darius; Powers, David; Mitchell, Pru; Hayman, Sarah; Eddy, Helen

    2010-01-01

    This paper reports on a joint proof of concept project undertaken by researchers from the Flinders University Artificial Intelligence Laboratory in partnership with information managers from the Education Network Australia (edna) team at Education Services Australia to address the question of whether artificial intelligence techniques could be…

  10. 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

  11. 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.

  12. Development of a Car Racing Simulator Game Using Artificial Intelligence Techniques

    Directory of Open Access Journals (Sweden)

    Marvin T. Chan

    2015-01-01

    Full Text Available This paper presents a car racing simulator game called Racer, in which the human player races a car against three game-controlled cars in a three-dimensional environment. The objective of the game is not to defeat the human player, but to provide the player with a challenging and enjoyable experience. To ensure that this objective can be accomplished, the game incorporates artificial intelligence (AI techniques, which enable the cars to be controlled in a manner that mimics natural driving. The paper provides a brief history of AI techniques in games, presents the use of AI techniques in contemporary video games, and discusses the AI techniques that were implemented in the development of Racer. A comparison of the AI techniques implemented in the Unity platform with traditional AI search techniques is also included in the discussion.

  13. 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.

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

    Directory of Open Access Journals (Sweden)

    Zhehuang Huang

    2015-01-01

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

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

    Science.gov (United States)

    Huang, Zhehuang; Chen, Yidong

    2015-01-01

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

  16. Competitive Intelligence.

    Science.gov (United States)

    Bergeron, Pierrette; Hiller, Christine A.

    2002-01-01

    Reviews the evolution of competitive intelligence since 1994, including terminology and definitions and analytical techniques. Addresses the issue of ethics; explores how information technology supports the competitive intelligence process; and discusses education and training opportunities for competitive intelligence, including core competencies…

  17. 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.

  18. PSO Based Optimal Design of Fractional Order Controller for Industrial Application

    OpenAIRE

    Rohit Gupta; Ruchika

    2016-01-01

    In this paper, a PSO based fractional order PID (FOPID) controller is proposed for concentration control of an isothermal Continuous Stirred Tank Reactor (CSTR) problem. CSTR is used to carry out chemical reactions in industries, which possesses complex nonlinear dynamic characteristics. Particle Swarm Optimization algorithm technique, which is an evolutionary optimization technique based on the movement and intelligence of swarm is proposed for tuning of the controller for this system. Compa...

  19. 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

  20. Improved Particle Swarm Optimization with a Collective Local Unimodal Search for Continuous Optimization Problems

    Science.gov (United States)

    Arasomwan, Martins Akugbe; Adewumi, Aderemi Oluyinka

    2014-01-01

    A new local search technique is proposed and used to improve the performance of particle swarm optimization algorithms by addressing the problem of premature convergence. In the proposed local search technique, a potential particle position in the solution search space is collectively constructed by a number of randomly selected particles in the swarm. The number of times the selection is made varies with the dimension of the optimization problem and each selected particle donates the value in the location of its randomly selected dimension from its personal best. After constructing the potential particle position, some local search is done around its neighbourhood in comparison with the current swarm global best position. It is then used to replace the global best particle position if it is found to be better; otherwise no replacement is made. Using some well-studied benchmark problems with low and high dimensions, numerical simulations were used to validate the performance of the improved algorithms. Comparisons were made with four different PSO variants, two of the variants implement different local search technique while the other two do not. Results show that the improved algorithms could obtain better quality solution while demonstrating better convergence velocity and precision, stability, robustness, and global-local search ability than the competing variants. PMID:24723827

  1. 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.

  2. 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

  3. 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...

  4. 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...

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

    Directory of Open Access Journals (Sweden)

    M. F. Anop

    2015-01-01

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

  6. 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.

  7. MO-FG-BRA-08: Swarm Intelligence-Based Personalized Respiratory Gating in Lung SAbR

    Energy Technology Data Exchange (ETDEWEB)

    Modiri, A; Sabouri, P; Sawant, A [University of Maryland in Baltimore, Baltimore, MD (United States); Gu, X; Timmerman, R [University of Texas Southwestern Medical Center, Dallas, TX (United States)

    2016-06-15

    Purpose: Respiratory gating is widely deployed as a clinical motion-management strategy in lung radiotherapy. In conventional gating, the beam is turned on during a pre-determined phase window; typically, around end-exhalation. In this work, we challenge the notion that end-exhalation is always the optimal gating phase. Specifically, we use a swarm-intelligence-based, inverse planning approach to determine the optimal respiratory phase and MU for each beam with respect to (i) the state of the anatomy at each phase and (ii) the time spent in that state, estimated from long-term monitoring of the patient’s breathing motion. Methods: In a retrospective study of five lung cancer patients, we compared the dosimetric performance of our proposed personalized gating (PG) with that of conventional end-of-exhale gating (CEG) and a previously-developed, fully 4D-optimized plan (combined with MLC tracking delivery). For each patient, respiratory phase probabilities (indicative of the time duration of the phase) were estimated over 2 minutes from lung tumor motion traces recorded previously using the Synchrony system (Accuray Inc.). Based on this information, inverse planning optimization was performed to calculate the optimal respiratory gating phase and MU for each beam. To ensure practical deliverability, each PG beam was constrained to deliver the assigned MU over a time duration comparable to that of CEG delivery. Results: Maximum OAR sparing for the five patients achieved by the PG and the 4D plans compared to CEG plans was: Esophagus Dmax [PG:57%, 4D:37%], Heart Dmax [PG:71%, 4D:87%], Spinal cord Dmax [PG:18%, 4D:68%] and Lung V13 [PG:16%, 4D:31%]. While patients spent the most time in exhalation, the PG-optimization chose end-exhale only for 28% of beams. Conclusion: Our novel gating strategy achieved significant dosimetric improvements over conventional gating, and approached the upper limit represented by fully 4D optimized planning while being significantly simpler

  8. MO-FG-BRA-08: Swarm Intelligence-Based Personalized Respiratory Gating in Lung SAbR

    International Nuclear Information System (INIS)

    Modiri, A; Sabouri, P; Sawant, A; Gu, X; Timmerman, R

    2016-01-01

    Purpose: Respiratory gating is widely deployed as a clinical motion-management strategy in lung radiotherapy. In conventional gating, the beam is turned on during a pre-determined phase window; typically, around end-exhalation. In this work, we challenge the notion that end-exhalation is always the optimal gating phase. Specifically, we use a swarm-intelligence-based, inverse planning approach to determine the optimal respiratory phase and MU for each beam with respect to (i) the state of the anatomy at each phase and (ii) the time spent in that state, estimated from long-term monitoring of the patient’s breathing motion. Methods: In a retrospective study of five lung cancer patients, we compared the dosimetric performance of our proposed personalized gating (PG) with that of conventional end-of-exhale gating (CEG) and a previously-developed, fully 4D-optimized plan (combined with MLC tracking delivery). For each patient, respiratory phase probabilities (indicative of the time duration of the phase) were estimated over 2 minutes from lung tumor motion traces recorded previously using the Synchrony system (Accuray Inc.). Based on this information, inverse planning optimization was performed to calculate the optimal respiratory gating phase and MU for each beam. To ensure practical deliverability, each PG beam was constrained to deliver the assigned MU over a time duration comparable to that of CEG delivery. Results: Maximum OAR sparing for the five patients achieved by the PG and the 4D plans compared to CEG plans was: Esophagus Dmax [PG:57%, 4D:37%], Heart Dmax [PG:71%, 4D:87%], Spinal cord Dmax [PG:18%, 4D:68%] and Lung V13 [PG:16%, 4D:31%]. While patients spent the most time in exhalation, the PG-optimization chose end-exhale only for 28% of beams. Conclusion: Our novel gating strategy achieved significant dosimetric improvements over conventional gating, and approached the upper limit represented by fully 4D optimized planning while being significantly simpler

  9. 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...

  10. Intelligible Artificial Intelligence

    OpenAIRE

    Weld, Daniel S.; Bansal, Gagan

    2018-01-01

    Since Artificial Intelligence (AI) software uses techniques like deep lookahead search and stochastic optimization of huge neural networks to fit mammoth datasets, it often results in complex behavior that is difficult for people to understand. Yet organizations are deploying AI algorithms in many mission-critical settings. In order to trust their behavior, we must make it intelligible --- either by using inherently interpretable models or by developing methods for explaining otherwise overwh...

  11. Detection of Carious Lesions and Restorations Using Particle Swarm Optimization Algorithm

    Directory of Open Access Journals (Sweden)

    Mohammad Naebi

    2016-01-01

    Full Text Available Background/Purpose. In terms of the detection of tooth diagnosis, no intelligent detection has been done up till now. Dentists just look at images and then they can detect the diagnosis position in tooth based on their experiences. Using new technologies, scientists will implement detection and repair of tooth diagnosis intelligently. In this paper, we have introduced one intelligent method for detection using particle swarm optimization (PSO and our mathematical formulation. This method was applied to 2D special images. Using developing of our method, we can detect tooth diagnosis for all of 2D and 3D images. Materials and Methods. In recent years, it is possible to implement intelligent processing of images by high efficiency optimization algorithms in many applications especially for detection of dental caries and restoration without human intervention. In the present work, we explain PSO algorithm with our detection formula for detection of dental caries and restoration. Also image processing helped us to implement our method. And to do so, pictures taken by digital radiography systems of tooth are used. Results and Conclusion. We implement some mathematics formula for fitness of PSO. Our results show that this method can detect dental caries and restoration in digital radiography pictures with the good convergence. In fact, the error rate of this method was 8%, so that it can be implemented for detection of dental caries and restoration. Using some parameters, it is possible that the error rate can be even reduced below 0.5%.

  12. Multidimensional particle swarm optimization for machine learning and pattern recognition

    CERN Document Server

    Kiranyaz, Serkan; Gabbouj, Moncef

    2013-01-01

    For many engineering problems we require optimization processes with dynamic adaptation as we aim to establish the dimension of the search space where the optimum solution resides and develop robust techniques to avoid the local optima usually associated with multimodal problems. This book explores multidimensional particle swarm optimization, a technique developed by the authors that addresses these requirements in a well-defined algorithmic approach.  After an introduction to the key optimization techniques, the authors introduce their unified framework and demonstrate its advantages in chal

  13. 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.

  14. 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.

  15. Convergence Analysis of a Class of Computational Intelligence Approaches

    Directory of Open Access Journals (Sweden)

    Junfeng Chen

    2013-01-01

    Full Text Available Computational intelligence approaches is a relatively new interdisciplinary field of research with many promising application areas. Although the computational intelligence approaches have gained huge popularity, it is difficult to analyze the convergence. In this paper, a computational model is built up for a class of computational intelligence approaches represented by the canonical forms of generic algorithms, ant colony optimization, and particle swarm optimization in order to describe the common features of these algorithms. And then, two quantification indices, that is, the variation rate and the progress rate, are defined, respectively, to indicate the variety and the optimality of the solution sets generated in the search process of the model. Moreover, we give four types of probabilistic convergence for the solution set updating sequences, and their relations are discussed. Finally, the sufficient conditions are derived for the almost sure weak convergence and the almost sure strong convergence of the model by introducing the martingale theory into the Markov chain analysis.

  16. 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.

  17. 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.

  18. Using a Combined Platform of Swarm Intelligence Algorithms and GIS to Provide Land Suitability Maps for Locating Cardiac Rehabilitation Defibrillators

    Science.gov (United States)

    KAFFASH-CHARANDABI, Neda; SADEGHI-NIARAKI, Abolghasem; PARK, Dong-Kyun

    2015-01-01

    Background: Cardiac arrest is a condition in which the heart is completely stopped and is not pumping any blood. Although most cardiac arrest cases are reported from homes or hospitals, about 20% occur in public areas. Therefore, these areas need to be investigated in terms of cardiac arrest incidence so that places of high incidence can be identified and cardiac rehabilitation defibrillators installed there. Methods: In order to investigate a study area in Petersburg, Pennsylvania State, and to determine appropriate places for installing defibrillators with 5-year period data, swarm intelligence algorithms were used. Moreover, the location of the defibrillators was determined based on the following five evaluation criteria: land use, altitude of the area, economic conditions, distance from hospitals and approximate areas of reported cases of cardiac arrest for public places that were created in geospatial information system (GIS). Results: The A-P HADEL algorithm results were more precise about 27.36%. The validation results indicated a wider coverage of real values and the verification results confirmed the faster and more exact optimization of the cost function in the PSO method. Conclusion: The study findings emphasize the necessity of applying optimal optimization methods along with GIS and precise selection of criteria in the selection of optimal locations for installing medical facilities because the selected algorithm and criteria dramatically affect the final responses. Meanwhile, providing land suitability maps for installing facilities across hot and risky spots has the potential to save many lives. PMID:26587471

  19. An intelligent content discovery technique for health portal content management.

    Science.gov (United States)

    De Silva, Daswin; Burstein, Frada

    2014-04-23

    Continuous content management of health information portals is a feature vital for its sustainability and widespread acceptance. Knowledge and experience of a domain expert is essential for content management in the health domain. The rate of generation of online health resources is exponential and thereby manual examination for relevance to a specific topic and audience is a formidable challenge for domain experts. Intelligent content discovery for effective content management is a less researched topic. An existing expert-endorsed content repository can provide the necessary leverage to automatically identify relevant resources and evaluate qualitative metrics. This paper reports on the design research towards an intelligent technique for automated content discovery and ranking for health information portals. The proposed technique aims to improve efficiency of the current mostly manual process of portal content management by utilising an existing expert-endorsed content repository as a supporting base and a benchmark to evaluate the suitability of new content A model for content management was established based on a field study of potential users. The proposed technique is integral to this content management model and executes in several phases (ie, query construction, content search, text analytics and fuzzy multi-criteria ranking). The construction of multi-dimensional search queries with input from Wordnet, the use of multi-word and single-word terms as representative semantics for text analytics and the use of fuzzy multi-criteria ranking for subjective evaluation of quality metrics are original contributions reported in this paper. The feasibility of the proposed technique was examined with experiments conducted on an actual health information portal, the BCKOnline portal. Both intermediary and final results generated by the technique are presented in the paper and these help to establish benefits of the technique and its contribution towards effective

  20. Collective motion of predictive swarms.

    Directory of Open Access Journals (Sweden)

    Nathaniel Rupprecht

    Full Text Available Theoretical models of populations and swarms typically start with the assumption that the motion of agents is governed by the local stimuli. However, an intelligent agent, with some understanding of the laws that govern its habitat, can anticipate the future, and make predictions to gather resources more efficiently. Here we study a specific model of this kind, where agents aim to maximize their consumption of a diffusing resource, by attempting to predict the future of a resource field and the actions of other agents. Once the agents make a prediction, they are attracted to move towards regions that have, and will have, denser resources. We find that the further the agents attempt to see into the future, the more their attempts at prediction fail, and the less resources they consume. We also study the case where predictive agents compete against non-predictive agents and find the predictors perform better than the non-predictors only when their relative numbers are very small. We conclude that predictivity pays off either when the predictors do not see too far into the future or the number of predictors is small.

  1. Hybrid glowworm swarm optimization for task scheduling in the cloud environment

    Science.gov (United States)

    Zhou, Jing; Dong, Shoubin

    2018-06-01

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

  2. Solving Multi-Pollutant Emission Dispatch Problem Using Computational Intelligence Technique

    Directory of Open Access Journals (Sweden)

    Nur Azzammudin Rahmat

    2016-06-01

    Full Text Available Economic dispatch is a crucial process conducted by the utilities to correctly determine the satisfying amount of power to be generated and distributed to the consumers. During the process, the utilities also consider pollutant emission as the consequences of fossil-fuel consumption. Fossil-fuel includes petroleum, coal, and natural gas; each has its unique chemical composition of pollutants i.e. sulphur oxides (SOX, nitrogen oxides (NOX and carbon oxides (COX. This paper presents multi-pollutant emission dispatch problem using computational intelligence technique. In this study, a novel emission dispatch technique is formulated to determine the amount of the pollutant level. It utilizes a pre-developed optimization technique termed as differential evolution immunized ant colony optimization (DEIANT for the emission dispatch problem. The optimization results indicated high level of COX level, regardless of any type of fossil fuel consumed.

  3. 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.

  4. On new developments in the physics of positron swarms

    International Nuclear Information System (INIS)

    Petrovic, Z Lj; Bankovic, A; Dujko, S; Marjanovic, S; Suvakov, M; Malovic, G; Marler, J P; Buckman, S J; White, R D; Robson, R E

    2010-01-01

    Recently a new wave of swarm studies of positrons was initiated based on more complete scattering cross section sets. Initially some interesting and new physics was discovered, most importantly negative differential conductivity (NDC) that occurs only for the bulk drift velocity while it does not exist for the flux property. However the ultimate goal was to develop tools to model positron transport in realistic applications and the work that is progressing along these lines is reviewed here. It includes studies of positron transport in molecular gases, thermalization in generic swarm situations and in realistic gas filled traps and transport of positrons in crossed electric and magnetic fields. Finally we have extended the same technique of simulation (Monte Carlo) to studies of thermalization of positronium molecule. In addition, recently published first steps towards including effects of dense media on positron transport are summarized here.

  5. 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.

  6. 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.

  7. A novel chaotic particle swarm optimization approach using Henon map and implicit filtering local search for economic load dispatch

    International Nuclear Information System (INIS)

    Coelho, Leandro dos Santos; Mariani, Viviana Cocco

    2009-01-01

    Particle swarm optimization (PSO) is a population-based swarm intelligence algorithm driven by the simulation of a social psychological metaphor instead of the survival of the fittest individual. Based on the chaotic systems theory, this paper proposed a novel chaotic PSO combined with an implicit filtering (IF) local search method to solve economic dispatch problems. Since chaotic mapping enjoys certainty, ergodicity and the stochastic property, the proposed PSO introduces chaos mapping using Henon map sequences which increases its convergence rate and resulting precision. The chaotic PSO approach is used to produce good potential solutions, and the IF is used to fine-tune of final solution of PSO. The hybrid methodology is validated for a test system consisting of 13 thermal units whose incremental fuel cost function takes into account the valve-point loading effects. Simulation results are promising and show the effectiveness of the proposed approach.

  8. 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.

  9. 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

  10. 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.

  11. Univariate and multivariate forecasting of hourly solar radiation with artificial intelligence techniques

    Energy Technology Data Exchange (ETDEWEB)

    Sfetsos, A. [7 Pirsou Str., Athens (Greece); Coonick, A.H. [Imperial Coll. of Science Technology and Medicine, Dept. of Electrical and Electronic Engineering, London (United Kingdom)

    2000-07-01

    This paper introduces a new approach for the forecasting of mean hourly global solar radiation received by a horizontal surface. In addition to the traditional linear methods, several artificial-intelligence-based techniques are studied. These include linear, feed-forward, recurrent Elman and Radial Basis neural networks alongside the adaptive neuro-fuzzy inference scheme. The problem is examined initially for the univariate case, and is extended to include additional meteorological parameters in the process of estimating the optimum model. The results indicate that the developed artificial intelligence models predict the solar radiation time series more effectively compared to the conventional procedures based on the clearness index. The forecasting ability of some models can be further enhanced with the use of additional meteorological parameters. (Author)

  12. 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.

  13. Particle swarm optimization of ascent trajectories of multistage launch vehicles

    Science.gov (United States)

    Pontani, Mauro

    2014-02-01

    Multistage launch vehicles are commonly employed to place spacecraft and satellites in their operational orbits. If the rocket characteristics are specified, the optimization of its ascending trajectory consists of determining the optimal control law that leads to maximizing the final mass at orbit injection. The numerical solution of a similar problem is not trivial and has been pursued with different methods, for decades. This paper is concerned with an original approach based on the joint use of swarming theory and the necessary conditions for optimality. The particle swarm optimization technique represents a heuristic population-based optimization method inspired by the natural motion of bird flocks. Each individual (or particle) that composes the swarm corresponds to a solution of the problem and is associated with a position and a velocity vector. The formula for velocity updating is the core of the method and is composed of three terms with stochastic weights. As a result, the population migrates toward different regions of the search space taking advantage of the mechanism of information sharing that affects the overall swarm dynamics. At the end of the process the best particle is selected and corresponds to the optimal solution to the problem of interest. In this work the three-dimensional trajectory of the multistage rocket is assumed to be composed of four arcs: (i) first stage propulsion, (ii) second stage propulsion, (iii) coast arc (after release of the second stage), and (iv) third stage propulsion. The Euler-Lagrange equations and the Pontryagin minimum principle, in conjunction with the Weierstrass-Erdmann corner conditions, are employed to express the thrust angles as functions of the adjoint variables conjugate to the dynamics equations. The use of these analytical conditions coming from the calculus of variations leads to obtaining the overall rocket dynamics as a function of seven parameters only, namely the unknown values of the initial state

  14. 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

  15. 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.

  16. A new hybrid model optimized by an intelligent optimization algorithm for wind speed forecasting

    International Nuclear Information System (INIS)

    Su, Zhongyue; Wang, Jianzhou; Lu, Haiyan; Zhao, Ge

    2014-01-01

    Highlights: • A new hybrid model is developed for wind speed forecasting. • The model is based on the Kalman filter and the ARIMA. • An intelligent optimization method is employed in the hybrid model. • The new hybrid model has good performance in western China. - Abstract: Forecasting the wind speed is indispensable in wind-related engineering studies and is important in the management of wind farms. As a technique essential for the future of clean energy systems, reducing the forecasting errors related to wind speed has always been an important research subject. In this paper, an optimized hybrid method based on the Autoregressive Integrated Moving Average (ARIMA) and Kalman filter is proposed to forecast the daily mean wind speed in western China. This approach employs Particle Swarm Optimization (PSO) as an intelligent optimization algorithm to optimize the parameters of the ARIMA model, which develops a hybrid model that is best adapted to the data set, increasing the fitting accuracy and avoiding over-fitting. The proposed method is subsequently examined on the wind farms of western China, where the proposed hybrid model is shown to perform effectively and steadily

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

    Directory of Open Access Journals (Sweden)

    Jianlei Zhang

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

  18. 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 ...

  19. 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.

  20. Artificial intelligence techniques used in respiratory sound analysis--a systematic review.

    Science.gov (United States)

    Palaniappan, Rajkumar; Sundaraj, Kenneth; Sundaraj, Sebastian

    2014-02-01

    Artificial intelligence (AI) has recently been established as an alternative method to many conventional methods. The implementation of AI techniques for respiratory sound analysis can assist medical professionals in the diagnosis of lung pathologies. This article highlights the importance of AI techniques in the implementation of computer-based respiratory sound analysis. Articles on computer-based respiratory sound analysis using AI techniques were identified by searches conducted on various electronic resources, such as the IEEE, Springer, Elsevier, PubMed, and ACM digital library databases. Brief descriptions of the types of respiratory sounds and their respective characteristics are provided. We then analyzed each of the previous studies to determine the specific respiratory sounds/pathology analyzed, the number of subjects, the signal processing method used, the AI techniques used, and the performance of the AI technique used in the analysis of respiratory sounds. A detailed description of each of these studies is provided. In conclusion, this article provides recommendations for further advancements in respiratory sound analysis.

  1. 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.

  2. Optimal Piezoelectric Actuators and Sensors Configuration for Vibration Suppression of Aircraft Framework Using Particle Swarm Algorithm

    Directory of Open Access Journals (Sweden)

    Quanzhen Huang

    2017-01-01

    Full Text Available Numbers and locations of sensors and actuators play an important role in cost and control performance for active vibration control system of piezoelectric smart structure. This may lead to a diverse control system if sensors and actuators were not configured properly. An optimal location method of piezoelectric actuators and sensors is proposed in this paper based on particle swarm algorithm (PSA. Due to the complexity of the frame structure, it can be taken as a combination of many piezoelectric intelligent beams and L-type structures. Firstly, an optimal criterion of sensors and actuators is proposed with an optimal objective function. Secondly, each order natural frequency and modal strain are calculated and substituted into the optimal objective function. Preliminary optimal allocation is done using the particle swarm algorithm, based on the similar optimization method and the combination of the vibration stress and strain distribution at the lower modal frequency. Finally, the optimal location is given. An experimental platform was established and the experimental results indirectly verified the feasibility and effectiveness of the proposed method.

  3. 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

  4. 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)

  5. 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...

  6. Design of Wire Antennas by Using an Evolved Particle Swarm Optimization Algorithm

    NARCIS (Netherlands)

    Lepelaars, E.S.A.M.; Zwamborn, A.P.M.; Rogovic, A.; Marasini, C.; Monorchio, A.

    2007-01-01

    A Particle Swarm Optimization (PSO) algorithm has been used in conjunction with a full-wave numerical code based on the Method of Moments (MoM) to design and optimize wire antennas. The PSO is a robust stochastic evolutionary numerical technique that is very effective in optimizing multidimensional

  7. 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.

  8. Discrete PID Tuning Using Artificial Intelligence Techniques

    Directory of Open Access Journals (Sweden)

    Petr DOLEŽEL

    2009-06-01

    Full Text Available PID controllers are widely used in industry these days due to their useful properties such as simple tuning or robustness. While they are applicable to many control problems, they can perform poorly in some applications. Highly nonlinear system control with constrained manipulated variable can be mentioned as an example. The point of the paper is to string together convenient qualities of conventional PID control and progressive techniques based on Artificial Intelligence. Proposed control method should deal with even highly nonlinear systems. To be more specific, there is described new method of discrete PID controller tuning in this paper. This method tunes discrete PID controller parameters online through the use of genetic algorithm and neural model of controlled system in order to control successfully even highly nonlinear systems. After method description and some discussion, there is performed control simulation and comparison to one chosen conventional control method.

  9. Slot Parameter Optimization for Multiband Antenna Performance Improvement Using Intelligent Systems

    Directory of Open Access Journals (Sweden)

    Erdem Demircioglu

    2015-01-01

    Full Text Available This paper discusses bandwidth enhancement for multiband microstrip patch antennas (MMPAs using symmetrical rectangular/square slots etched on the patch and the substrate properties. The slot parameters on MMPA are modeled using soft computing technique of artificial neural networks (ANN. To achieve the best ANN performance, Particle Swarm Optimization (PSO and Differential Evolution (DE are applied with ANN’s conventional training algorithm in optimization of the modeling performance. In this study, the slot parameters are assumed as slot distance to the radiating patch edge, slot width, and length. Bandwidth enhancement is applied to a formerly designed MMPA fed by a microstrip transmission line attached to the center pin of 50 ohm SMA connecter. The simulated antennas are fabricated and measured. Measurement results are utilized for training the artificial intelligence models. The ANN provides 98% model accuracy for rectangular slots and 97% for square slots; however, ANFIS offer 90% accuracy with lack of resonance frequency tracking.

  10. Particle swarm optimization with scale-free interactions.

    Directory of Open Access Journals (Sweden)

    Chen Liu

    Full Text Available The particle swarm optimization (PSO algorithm, in which individuals collaborate with their interacted neighbors like bird flocking to search for the optima, has been successfully applied in a wide range of fields pertaining to searching and convergence. Here we employ the scale-free network to represent the inter-individual interactions in the population, named SF-PSO. In contrast to the traditional PSO with fully-connected topology or regular topology, the scale-free topology used in SF-PSO incorporates the diversity of individuals in searching and information dissemination ability, leading to a quite different optimization process. Systematic results with respect to several standard test functions demonstrate that SF-PSO gives rise to a better balance between the convergence speed and the optimum quality, accounting for its much better performance than that of the traditional PSO algorithms. We further explore the dynamical searching process microscopically, finding that the cooperation of hub nodes and non-hub nodes play a crucial role in optimizing the convergence process. Our work may have implications in computational intelligence and complex networks.

  11. Multiple-Swarm Ensembles: Improving the Predictive Power and Robustness of Predictive Models and Its Use in Computational Biology.

    Science.gov (United States)

    Alves, Pedro; Liu, Shuang; Wang, Daifeng; Gerstein, Mark

    2018-01-01

    Machine learning is an integral part of computational biology, and has already shown its use in various applications, such as prognostic tests. In the last few years in the non-biological machine learning community, ensembling techniques have shown their power in data mining competitions such as the Netflix challenge; however, such methods have not found wide use in computational biology. In this work, we endeavor to show how ensembling techniques can be applied to practical problems, including problems in the field of bioinformatics, and how they often outperform other machine learning techniques in both predictive power and robustness. Furthermore, we develop a methodology of ensembling, Multi-Swarm Ensemble (MSWE) by using multiple particle swarm optimizations and demonstrate its ability to further enhance the performance of ensembles.

  12. Increasing the Intelligence of Virtual Sales Assistants through Knowledge Modeling Techniques

    OpenAIRE

    Molina, Martin

    2001-01-01

    Shopping agents are web-based applications that help consumers to find appropriate products in the context of e-commerce. In this paper we argue about the utility of advanced model-based techniques that recently have been proposed in the fields of Artificial Intelligence and Knowledge Engineering, in order to increase the level of support provided by this type of applications. We illustrate this approach with a virtual sales assistant that dynamically configures a product according to the nee...

  13. 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.

  14. Improved quantum-behaved particle swarm optimization with local search strategy

    Directory of Open Access Journals (Sweden)

    Maolong Xi

    2017-03-01

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

  15. 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)

  16. Detection of drugs and explosives using neutron computerized tomography and artificial intelligence techniques

    Energy Technology Data Exchange (ETDEWEB)

    Ferreira, F.J.O. [Instituto de Engenharia Nuclear, Cidade Universitaria, Rio de Janeiro, CEP 21945-970, Caixa Postal 68550 (Brazil)], E-mail: fferreira@ien.gov.br; Crispim, V.R.; Silva, A.X. [DNC/Poli, PEN COPPE CT, UFRJ Universidade Federal do Rio de Janeiro, CEP 21941-972, Caixa Postal 68509, Rio de Janeiro (Brazil)

    2010-06-15

    In this study the development of a methodology to detect illicit drugs and plastic explosives is described with the objective of being applied in the realm of public security. For this end, non-destructive assay with neutrons was used and the technique applied was the real time neutron radiography together with computerized tomography. The system is endowed with automatic responses based upon the application of an artificial intelligence technique. In previous tests using real samples, the system proved capable of identifying 97% of the inspected materials.

  17. Detection of drugs and explosives using neutron computerized tomography and artificial intelligence techniques

    International Nuclear Information System (INIS)

    Ferreira, F.J.O.; Crispim, V.R.; Silva, A.X.

    2010-01-01

    In this study the development of a methodology to detect illicit drugs and plastic explosives is described with the objective of being applied in the realm of public security. For this end, non-destructive assay with neutrons was used and the technique applied was the real time neutron radiography together with computerized tomography. The system is endowed with automatic responses based upon the application of an artificial intelligence technique. In previous tests using real samples, the system proved capable of identifying 97% of the inspected materials.

  18. 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.)

  19. 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....

  20. 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

  1. Anesthesiology Nurse Scheduling using Particle Swarm Optimization

    Directory of Open Access Journals (Sweden)

    Leopoldo Altamirano

    2012-02-01

    Full Text Available In this article we present an approach designed to solve a real world problem: the Anesthesiology Nurse Scheduling Problem (ANSP at a public French hospital. The anesthesiology nurses are one of the most shared resources in the hospital and we attempt to find a fair/balanced schedule for them, taking into account a set of constraints and the nursesarsquo; stated preferences, concerning the different shifts. We propose a particle swarm optimization algorithm to solve the ANSP. Finally, we compare our technique with previous results obtained using integer programming.

  2. 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.

  3. 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.

  4. Basic investigation of particle swarm optimization performance in a reduced scale PWR passive safety system design

    International Nuclear Information System (INIS)

    Cunha, Joao J. da; Lapa, Celso Marcelo F.; Alvim, Antonio Carlos M.; Lima, Carlos A. Souza; Pereira, Claudio Marcio do N.A.

    2010-01-01

    This work presents a methodology to investigate the viability of using particle swarm optimization technique to obtain the best combination of physical and operational parameters that lead to the best adjusted dimensionless groups, calculated by similarity laws, that are able to simulate the most relevant physical phenomena in single-phase flow under natural circulation and to offer an appropriate alternative reduced scale design for reactor primary loops with this flow characteristics. A PWR reactor core, under natural circulation, based on LOFT test facility, was used as the case study. The particle swarm optimization technique was applied to a problem with these thermo-hydraulics conditions and results demonstrated the viability and adequacy of the method to design similar systems with these characteristics.

  5. Basic investigation of particle swarm optimization performance in a reduced scale PWR passive safety system design

    Energy Technology Data Exchange (ETDEWEB)

    Cunha, Joao J. da [Eletronuclear Eletrobras Termonuclear, Gerencia de Analise de Seguranca Nuclear, Rua da Candelaria, 65, 7o andar. Centro, Rio de Janeiro 20091-906 (Brazil); Lapa, Celso Marcelo F., E-mail: lapa@ien.gov.b [Instituto de Engenharia Nuclear, Divisao de Reatores/PPGIEN, P.O. Box 68550, Rua Helio de Almeida 75 Cidade Universitaria, Ilha do Fundao, Rio de Janeiro 21941-972 (Brazil); Instituto Nacional de Ciencia e Tecnologia de Reatores Nucleares Inovadores (Brazil); Alvim, Antonio Carlos M. [Universidade Federal do Rio de Janeiro, COPPE/Nuclear, P.O. Box 68509, Cidade Universitaria, Ilha do Fundao s/n, Rio de Janeiro 21945-970 (Brazil); Instituto Nacional de Ciencia e Tecnologia de Reatores Nucleares Inovadores (Brazil); Lima, Carlos A. Souza [Instituto de Engenharia Nuclear, Divisao de Reatores/PPGIEN, P.O. Box 68550, Rua Helio de Almeida 75 Cidade Universitaria, Ilha do Fundao, Rio de Janeiro 21941-972 (Brazil); Instituto Politecnico, Universidade do Estado do Rio de Janeiro, Pos-Graduacao em Modelagem Computacional, Rua Alberto Rangel, s/n, Vila Nova, Nova Friburgo 28630-050 (Brazil); Pereira, Claudio Marcio do N.A. [Instituto de Engenharia Nuclear, Divisao de Reatores/PPGIEN, P.O. Box 68550, Rua Helio de Almeida 75 Cidade Universitaria, Ilha do Fundao, Rio de Janeiro 21941-972 (Brazil); Instituto Nacional de Ciencia e Tecnologia de Reatores Nucleares Inovadores (Brazil)

    2010-03-15

    This work presents a methodology to investigate the viability of using particle swarm optimization technique to obtain the best combination of physical and operational parameters that lead to the best adjusted dimensionless groups, calculated by similarity laws, that are able to simulate the most relevant physical phenomena in single-phase flow under natural circulation and to offer an appropriate alternative reduced scale design for reactor primary loops with this flow characteristics. A PWR reactor core, under natural circulation, based on LOFT test facility, was used as the case study. The particle swarm optimization technique was applied to a problem with these thermo-hydraulics conditions and results demonstrated the viability and adequacy of the method to design similar systems with these characteristics.

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

    Directory of Open Access Journals (Sweden)

    Nur Faziera Napis

    2018-05-01

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

  7. 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.

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

    OpenAIRE

    Khulood A. Dagher; Ahmed S. Al-Araji

    2013-01-01

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

  9. Electrical Resistance Imaging of Two-Phase Flow With a Mesh Grouping Technique Based On Particle Swarm Optimization

    International Nuclear Information System (INIS)

    Lee, Bo An; Kim, Bong Seok; Ko, Min Seok; Kim, Kyung Young; Kim, Sin

    2014-01-01

    An electrical resistance tomography (ERT) technique combining the particle swarm optimization (PSO) algorithm with the Gauss-Newton method is applied to the visualization of two-phase flows. In the ERT, the electrical conductivity distribution, namely the conductivity values of pixels (numerical meshes) comprising the domain in the context of a numerical image reconstruction algorithm, is estimated with the known injected currents through the electrodes attached on the domain boundary and the measured potentials on those electrodes. In spite of many favorable characteristics of ERT such as no radiation, low cost, and high temporal resolution compared to other tomography techniques, one of the major drawbacks of ERT is low spatial resolution due to the inherent ill-posedness of conventional image reconstruction algorithms. In fact, the number of known data is much less than that of the unknowns (meshes). Recalling that binary mixtures like two-phase flows consist of only two substances with distinct electrical conductivities, this work adopts the PSO algorithm for mesh grouping to reduce the number of unknowns. In order to verify the enhanced performance of the proposed method, several numerical tests are performed. The comparison between the proposed algorithm and conventional Gauss-Newton method shows significant improvements in the quality of reconstructed images

  10. ELECTRICAL RESISTANCE IMAGING OF TWO-PHASE FLOW WITH A MESH GROUPING TECHNIQUE BASED ON PARTICLE SWARM OPTIMIZATION

    Directory of Open Access Journals (Sweden)

    BO AN LEE

    2014-02-01

    Full Text Available An electrical resistance tomography (ERT technique combining the particle swarm optimization (PSO algorithm with the Gauss-Newton method is applied to the visualization of two-phase flows. In the ERT, the electrical conductivity distribution, namely the conductivity values of pixels (numerical meshes comprising the domain in the context of a numerical image reconstruction algorithm, is estimated with the known injected currents through the electrodes attached on the domain boundary and the measured potentials on those electrodes. In spite of many favorable characteristics of ERT such as no radiation, low cost, and high temporal resolution compared to other tomography techniques, one of the major drawbacks of ERT is low spatial resolution due to the inherent ill-posedness of conventional image reconstruction algorithms. In fact, the number of known data is much less than that of the unknowns (meshes. Recalling that binary mixtures like two-phase flows consist of only two substances with distinct electrical conductivities, this work adopts the PSO algorithm for mesh grouping to reduce the number of unknowns. In order to verify the enhanced performance of the proposed method, several numerical tests are performed. The comparison between the proposed algorithm and conventional Gauss-Newton method shows significant improvements in the quality of reconstructed images.

  11. Electrical Resistance Imaging of Two-Phase Flow With a Mesh Grouping Technique Based On Particle Swarm Optimization

    Energy Technology Data Exchange (ETDEWEB)

    Lee, Bo An; Kim, Bong Seok; Ko, Min Seok; Kim, Kyung Young; Kim, Sin [Jeju National Univ., Jeju (Korea, Republic of)

    2014-02-15

    An electrical resistance tomography (ERT) technique combining the particle swarm optimization (PSO) algorithm with the Gauss-Newton method is applied to the visualization of two-phase flows. In the ERT, the electrical conductivity distribution, namely the conductivity values of pixels (numerical meshes) comprising the domain in the context of a numerical image reconstruction algorithm, is estimated with the known injected currents through the electrodes attached on the domain boundary and the measured potentials on those electrodes. In spite of many favorable characteristics of ERT such as no radiation, low cost, and high temporal resolution compared to other tomography techniques, one of the major drawbacks of ERT is low spatial resolution due to the inherent ill-posedness of conventional image reconstruction algorithms. In fact, the number of known data is much less than that of the unknowns (meshes). Recalling that binary mixtures like two-phase flows consist of only two substances with distinct electrical conductivities, this work adopts the PSO algorithm for mesh grouping to reduce the number of unknowns. In order to verify the enhanced performance of the proposed method, several numerical tests are performed. The comparison between the proposed algorithm and conventional Gauss-Newton method shows significant improvements in the quality of reconstructed images.

  12. Artificial Intelligence based technique for BTS placement

    Science.gov (United States)

    Alenoghena, C. O.; Emagbetere, J. O.; Aibinu, A. M.

    2013-12-01

    The increase of the base transceiver station (BTS) in most urban areas can be traced to the drive by network providers to meet demand for coverage and capacity. In traditional network planning, the final decision of BTS placement is taken by a team of radio planners, this decision is not fool proof against regulatory requirements. In this paper, an intelligent based algorithm for optimal BTS site placement has been proposed. The proposed technique takes into consideration neighbour and regulation considerations objectively while determining cell site. The application will lead to a quantitatively unbiased evaluated decision making process in BTS placement. An experimental data of a 2km by 3km territory was simulated for testing the new algorithm, results obtained show a 100% performance of the neighbour constrained algorithm in BTS placement optimization. Results on the application of GA with neighbourhood constraint indicate that the choices of location can be unbiased and optimization of facility placement for network design can be carried out.

  13. Artificial Intelligence based technique for BTS placement

    International Nuclear Information System (INIS)

    Alenoghena, C O; Emagbetere, J O; 1 Minna (Nigeria))" data-affiliation=" (Department of Telecommunications Engineering, Federal University of Techn.1 Minna (Nigeria))" >Aibinu, A M

    2013-01-01

    The increase of the base transceiver station (BTS) in most urban areas can be traced to the drive by network providers to meet demand for coverage and capacity. In traditional network planning, the final decision of BTS placement is taken by a team of radio planners, this decision is not fool proof against regulatory requirements. In this paper, an intelligent based algorithm for optimal BTS site placement has been proposed. The proposed technique takes into consideration neighbour and regulation considerations objectively while determining cell site. The application will lead to a quantitatively unbiased evaluated decision making process in BTS placement. An experimental data of a 2km by 3km territory was simulated for testing the new algorithm, results obtained show a 100% performance of the neighbour constrained algorithm in BTS placement optimization. Results on the application of GA with neighbourhood constraint indicate that the choices of location can be unbiased and optimization of facility placement for network design can be carried out

  14. Optimization of a Fuzzy-Logic-Control-Based MPPT Algorithm Using the Particle Swarm Optimization Technique

    Directory of Open Access Journals (Sweden)

    Po-Chen Cheng

    2015-06-01

    Full Text Available In this paper, an asymmetrical fuzzy-logic-control (FLC-based maximum power point tracking (MPPT algorithm for photovoltaic (PV systems is presented. Two membership function (MF design methodologies that can improve the effectiveness of the proposed asymmetrical FLC-based MPPT methods are then proposed. The first method can quickly determine the input MF setting values via the power–voltage (P–V curve of solar cells under standard test conditions (STC. The second method uses the particle swarm optimization (PSO technique to optimize the input MF setting values. Because the PSO approach must target and optimize a cost function, a cost function design methodology that meets the performance requirements of practical photovoltaic generation systems (PGSs is also proposed. According to the simulated and experimental results, the proposed asymmetrical FLC-based MPPT method has the highest fitness value, therefore, it can successfully address the tracking speed/tracking accuracy dilemma compared with the traditional perturb and observe (P&O and symmetrical FLC-based MPPT algorithms. Compared to the conventional FLC-based MPPT method, the obtained optimal asymmetrical FLC-based MPPT can improve the transient time and the MPPT tracking accuracy by 25.8% and 0.98% under STC, respectively.

  15. 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.

  16. 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)

  17. 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...

  18. BWR shutdown analyzer using artificial intelligence (AI) techniques

    International Nuclear Information System (INIS)

    Cain, D.G.

    1986-01-01

    A prototype alarm system for detecting abnormal reactor shutdowns based on artificial intelligence technology is described. The system incorporates knowledge about Boiling Water Reactor (BWR) plant design and component behavior, as well as knowledge required to distinguish normal, abnormal, and ATWS accident conditions. The system was developed using a software tool environment for creating knowledge-based applications on a LISP machine. To facilitate prototype implementation and evaluation, a casual simulation of BWR shutdown sequences was developed and interfaced with the alarm system. An intelligent graphics interface for execution and control is described. System performance considerations and general observations relating to artificial intelligence application to nuclear power plant problems are provided

  19. A Binary Cat Swarm Optimization Algorithm for the Non-Unicost Set Covering Problem

    Directory of Open Access Journals (Sweden)

    Broderick Crawford

    2015-01-01

    Full Text Available The Set Covering Problem consists in finding a subset of columns in a zero-one matrix such that they cover all the rows of the matrix at a minimum cost. To solve the Set Covering Problem we use a metaheuristic called Binary Cat Swarm Optimization. This metaheuristic is a recent swarm metaheuristic technique based on the cat behavior. Domestic cats show the ability to hunt and are curious about moving objects. Based on this, the cats have two modes of behavior: seeking mode and tracing mode. We are the first ones to use this metaheuristic to solve this problem; our algorithm solves a set of 65 Set Covering Problem instances from OR-Library.

  20. A Novel Flexible Inertia Weight Particle Swarm Optimization Algorithm.

    Science.gov (United States)

    Amoshahy, Mohammad Javad; Shamsi, Mousa; Sedaaghi, Mohammad Hossein

    2016-01-01

    Particle swarm optimization (PSO) is an evolutionary computing method based on intelligent collective behavior of some animals. It is easy to implement and there are few parameters to adjust. The performance of PSO algorithm depends greatly on the appropriate parameter selection strategies for fine tuning its parameters. Inertia weight (IW) is one of PSO's parameters used to bring about a balance between the exploration and exploitation characteristics of PSO. This paper proposes a new nonlinear strategy for selecting inertia weight which is named Flexible Exponential Inertia Weight (FEIW) strategy because according to each problem we can construct an increasing or decreasing inertia weight strategy with suitable parameters selection. The efficacy and efficiency of PSO algorithm with FEIW strategy (FEPSO) is validated on a suite of benchmark problems with different dimensions. Also FEIW is compared with best time-varying, adaptive, constant and random inertia weights. Experimental results and statistical analysis prove that FEIW improves the search performance in terms of solution quality as well as convergence rate.

  1. Applying Artificial Intelligence and Internet Techniques in Rural Tourism Domain

    OpenAIRE

    Turcu, Cristina; Turcu, Cornel

    2017-01-01

    Society has become more dependent on automated intelligent systems, at the same time, these systems have become more and more complicated. Society's expectation regarding the capabilities and intelligence of such systems has also grown. We have become a more complicated society with more complicated problems. As the expectation of intelligent systems rises, we discover many more applications for artificial intelligence. Additionally, as the difficulty level and computational requirements of s...

  2. Artificial intelligence techniques for photovoltaic applications: A review

    Energy Technology Data Exchange (ETDEWEB)

    Mellit, Adel [Department of Electronics, Faculty of Sciences Engineering, LAMEL Laboratory, Jijel University, Oulad-aissa, P.O. Box 98, Jijel 18000 (Algeria); Kalogirou, Soteris A. [Department of Mechanical Engineering and Materials Science and Engineering, Cyprus University of Technology, P.O. Box 50329, Limassol 3603 (Cyprus)

    2008-10-15

    Artificial intelligence (AI) techniques are becoming useful as alternate approaches to conventional techniques or as components of integrated systems. They have been used to solve complicated practical problems in various areas and are becoming more popular nowadays. They can learn from examples, are fault tolerant in the sense that they are able to handle noisy and incomplete data, are able to deal with nonlinear problems and once trained can perform prediction and generalization at high speed. AI-based systems are being developed and deployed worldwide in a wide variety of applications, mainly because of their symbolic reasoning, flexibility and explanation capabilities. AI has been used in different sectors, such as engineering, economics, medicine, military, marine, etc. They have also been applied for modeling, identification, optimization, prediction, forecasting and control of complex systems. The paper outlines an understanding of how AI systems operate by way of presenting a number of problems in photovoltaic systems application. Problems presented include three areas: forecasting and modeling of meteorological data, sizing of photovoltaic systems and modeling, simulation and control of photovoltaic systems. Published literature presented in this paper show the potential of AI as design tool in photovoltaic systems. (author)

  3. 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.

  4. Techniques for Intelligence Analysis of Networks

    National Research Council Canada - National Science Library

    Cares, Jeffrey R

    2005-01-01

    ...) there are significant intelligence analysis manifestations of these properties; and (4) a more satisfying theory of Networked Competition than currently exists for NCW/NCO is emerging from this research...

  5. Hacking web intelligence open source intelligence and web reconnaissance concepts and techniques

    CERN Document Server

    Chauhan, Sudhanshu

    2015-01-01

    Open source intelligence (OSINT) and web reconnaissance are rich topics for infosec professionals looking for the best ways to sift through the abundance of information widely available online. In many cases, the first stage of any security assessment-that is, reconnaissance-is not given enough attention by security professionals, hackers, and penetration testers. Often, the information openly present is as critical as the confidential data. Hacking Web Intelligence shows you how to dig into the Web and uncover the information many don't even know exists. The book takes a holistic approach

  6. Virtual Power Plant and Microgrids controller for Energy Management based on optimization techniques

    Directory of Open Access Journals (Sweden)

    Maher G. M. Abdolrasol

    2017-06-01

    Full Text Available This paper discuss virtual power plant (VPP and Microgrid controller for energy management system (EMS based on optimization techniques by using two optimization techniques namely Backtracking search algorithm (BSA and particle swarm optimization algorithm (PSO. The research proposes use of multi Microgrid in the distribution networks to aggregate the power form distribution generation and form it into single Microgrid and let these Microgrid deal directly with the central organizer called virtual power plant. VPP duties are price forecast, demand forecast, weather forecast, production forecast, shedding loads, make intelligent decision and for aggregate & optimizes the data. This huge system has been tested and simulated by using Matlab simulink. These paper shows optimizations of two methods were really significant in the results. But BSA is better than PSO to search for better parameters which could make more power saving as in the results and the discussion.

  7. 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.

  8. Influence of excited molecules on electron swarm transport coefficients and gas discharge kinetics

    International Nuclear Information System (INIS)

    Petrovic, Z.L.; Jovanovic, J.V.; Raspopovic, Z.M.; Bzenic, S.A.; Vrhovac, S.B.

    1997-01-01

    In this paper we study different effects of excited molecules on swarm parameters, electron energy distribution functions and gas discharge modeling. First we discuss a possible experiment in parahydrogen to resolve the discrepancy in hydrogen vibrational excitation cross section data. Negative differential conductivity (NDC) is a kinetic phenomenon which manifests itself in a particular dependence of the drift velocity on E=N and it is affected by superelastic collisions with excited states. A complete kinetic scheme for argon required to model excited state densities in gas discharges is also described. These results are used to explain experiments in capacitively and inductively coupled RF plasmas used for processing. The paper illustrates the application of atomic and molecular collision data, swarm data and the theoretical techniques in modeling of gas discharges with large abundances of excited molecules. It is pointed out that swarm experiments with excited molecules are lacking and that there is a shortage of reliable data, while the numerical procedures are sufficiently developed to include all the important effects. (authors). 59 refs., 12 figs

  9. A New Approach for Flexible Molecular Docking Based on Swarm Intelligence

    Directory of Open Access Journals (Sweden)

    Yi Fu

    2015-01-01

    Full Text Available Molecular docking methods play an important role in the field of computer-aided drug design. In the work, on the basis of the molecular docking program AutoDock, we present QLDock as a tool for flexible molecular docking. For the energy evaluation, the algorithm uses the binding free energy function that is provided by the AutoDock 4.2 tool. The new search algorithm combines the features of a quantum-behaved particle swarm optimization (QPSO algorithm and local search method of Solis and Wets for solving the highly flexible protein-ligand docking problem. We compute the interaction of 23 protein-ligand complexes and compare the results with those of the QDock and AutoDock programs. The experimental results show that our approach leads to substantially lower docking energy and higher docking precision in comparison to Lamarckian genetic algorithm and QPSO algorithm alone. QPSO-ls algorithm was able to identify the correct binding mode of 74% of the complexes. In comparison, the accuracy of QPSO and LGA is 52% and 61%, respectively. This difference in performance rises with increasing complexity of the ligand. Thus, the novel algorithm QPSO-ls may be used to dock ligand with many rotatable bonds with high accuracy.

  10. Artificial intelligence techniques for sizing photovoltaic systems. A review

    Energy Technology Data Exchange (ETDEWEB)

    Mellit, A. [Department of Electronics, Faculty of Science Engineering, LAMEL Laboratory, Jijel University, P.O. Box 98, Oulad Aissa, Jijel 18000 (Algeria); Kalogirou, S.A. [Department of Mechanical Engineering and Materials Science and Engineering, Cyprus University of Technology, P.O. Box 50329, Limassol 3603 (Cyprus); Hontoria, L. [Grupo Investigacion y Desarrollo en Energia Solar y Automatica, Dpto. de Electronica, E.P.S. Jaen, Universidad de Jaen, Avda., Madrid, 35, 23071 Jaen (Spain); Shaari, S. [Faculty of Applied Sciences, Universiti Teknologi MARA 40450 Shah Alam, Selangor (Malaysia)

    2009-02-15

    Artificial intelligence (AI) techniques are becoming useful as alternate approaches to conventional techniques or as components of integrated systems. They have been used to solve complicated practical problems in various areas and are becoming more and more popular nowadays. AI-techniques have the following features: can learn from examples; are fault tolerant in the sense that they are able to handle noisy and incomplete data; are able to deal with non-linear problems; and once trained can perform prediction and generalization at high speed. AI-based systems are being developed and deployed worldwide in a myriad of applications, mainly because of their symbolic reasoning, flexibility and explanation capabilities. AI have been used and applied in different sectors, such as engineering, economics, medicine, military, marine, etc. They have also been applied for modeling, identification, optimization, prediction, forecasting, and control of complex systems. The main objective of this paper is to present an overview of the AI-techniques for sizing photovoltaic (PV) systems: stand-alone PVs, grid-connected PV systems, PV-wind hybrid systems, etc. Published literature presented in this paper show the potential of AI as a design tool for the optimal sizing of PV systems. Additionally, the advantage of using an AI-based sizing of PV systems is that it provides good optimization, especially in isolated areas, where the weather data are not always available. (author)

  11. 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)

  12. 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.

  13. 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

  14. Advanced intelligent systems

    CERN Document Server

    Ryoo, Young; Jang, Moon-soo; Bae, Young-Chul

    2014-01-01

    Intelligent systems have been initiated with the attempt to imitate the human brain. People wish to let machines perform intelligent works. Many techniques of intelligent systems are based on artificial intelligence. According to changing and novel requirements, the advanced intelligent systems cover a wide spectrum: big data processing, intelligent control, advanced robotics, artificial intelligence and machine learning. This book focuses on coordinating intelligent systems with highly integrated and foundationally functional components. The book consists of 19 contributions that features social network-based recommender systems, application of fuzzy enforcement, energy visualization, ultrasonic muscular thickness measurement, regional analysis and predictive modeling, analysis of 3D polygon data, blood pressure estimation system, fuzzy human model, fuzzy ultrasonic imaging method, ultrasonic mobile smart technology, pseudo-normal image synthesis, subspace classifier, mobile object tracking, standing-up moti...

  15. Role of Artificial Intelligence Techniques (Automatic Classifiers) in Molecular Imaging Modalities in Neurodegenerative Diseases.

    Science.gov (United States)

    Cascianelli, Silvia; Scialpi, Michele; Amici, Serena; Forini, Nevio; Minestrini, Matteo; Fravolini, Mario Luca; Sinzinger, Helmut; Schillaci, Orazio; Palumbo, Barbara

    2017-01-01

    Artificial Intelligence (AI) is a very active Computer Science research field aiming to develop systems that mimic human intelligence and is helpful in many human activities, including Medicine. In this review we presented some examples of the exploiting of AI techniques, in particular automatic classifiers such as Artificial Neural Network (ANN), Support Vector Machine (SVM), Classification Tree (ClT) and ensemble methods like Random Forest (RF), able to analyze findings obtained by positron emission tomography (PET) or single-photon emission tomography (SPECT) scans of patients with Neurodegenerative Diseases, in particular Alzheimer's Disease. We also focused our attention on techniques applied in order to preprocess data and reduce their dimensionality via feature selection or projection in a more representative domain (Principal Component Analysis - PCA - or Partial Least Squares - PLS - are examples of such methods); this is a crucial step while dealing with medical data, since it is necessary to compress patient information and retain only the most useful in order to discriminate subjects into normal and pathological classes. Main literature papers on the application of these techniques to classify patients with neurodegenerative disease extracting data from molecular imaging modalities are reported, showing that the increasing development of computer aided diagnosis systems is very promising to contribute to the diagnostic process.

  16. 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...

  17. The 2017 Maple Creek Seismic Swarm in Yellowstone National Park

    Science.gov (United States)

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

    2017-12-01

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

  18. 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.

  19. 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.

  20. 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.

  1. 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.

  2. 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...

  3. 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....

  4. 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)

  5. An Energy-Aware Runtime Management of Multi-Core Sensory Swarms

    Directory of Open Access Journals (Sweden)

    Sungchan Kim

    2017-08-01

    Full Text Available In sensory swarms, minimizing energy consumption under performance constraint is one of the key objectives. One possible approach to this problem is to monitor application workload that is subject to change at runtime, and to adjust system configuration adaptively to satisfy the performance goal. As today’s sensory swarms are usually implemented using multi-core processors with adjustable clock frequency, we propose to monitor the CPU workload periodically and adjust the task-to-core allocation or clock frequency in an energy-efficient way in response to the workload variations. In doing so, we present an online heuristic that determines the most energy-efficient adjustment that satisfies the performance requirement. The proposed method is based on a simple yet effective energy model that is built upon performance prediction using IPC (instructions per cycle measured online and power equation derived empirically. The use of IPC accounts for memory intensities of a given workload, enabling the accurate prediction of execution time. Hence, the model allows us to rapidly and accurately estimate the effect of the two control knobs, clock frequency adjustment and core allocation. The experiments show that the proposed technique delivers considerable energy saving of up to 45%compared to the state-of-the-art multi-core energy management technique.

  6. 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

  7. 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.

  8. 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.

  9. Computational intelligence for technology enhanced learning

    Energy Technology Data Exchange (ETDEWEB)

    Xhafa, Fatos [Polytechnic Univ. of Catalonia, Barcelona (Spain). Dept. of Languages and Informatics Systems; Caballe, Santi; Daradoumis, Thanasis [Open Univ. of Catalonia, Barcelona (Spain). Dept. of Computer Sciences Multimedia and Telecommunications; Abraham, Ajith [Machine Intelligence Research Labs (MIR Labs), Auburn, WA (United States). Scientific Network for Innovation and Research Excellence; Juan Perez, Angel Alejandro (eds.) [Open Univ. of Catalonia, Barcelona (Spain). Dept. of Information Sciences

    2010-07-01

    E-Learning has become one of the most wide spread ways of distance teaching and learning. Technologies such as Web, Grid, and Mobile and Wireless networks are pushing teaching and learning communities to find new and intelligent ways of using these technologies to enhance teaching and learning activities. Indeed, these new technologies can play an important role in increasing the support to teachers and learners, to shorten the time to learning and teaching; yet, it is necessary to use intelligent techniques to take advantage of these new technologies to achieve the desired support to teachers and learners and enhance learners' performance in distributed learning environments. The chapters of this volume bring advances in using intelligent techniques for technology enhanced learning as well as development of e-Learning applications based on such techniques and supported by technology. Such intelligent techniques include clustering and classification for personalization of learning, intelligent context-aware techniques, adaptive learning, data mining techniques and ontologies in e-Learning systems, among others. Academics, scientists, software developers, teachers and tutors and students interested in e-Learning will find this book useful for their academic, research and practice activity. (orig.)

  10. Multi-Objective Sustainable Operation of the Three Gorges Cascaded Hydropower System Using Multi-Swarm Comprehensive Learning Particle Swarm Optimization

    Directory of Open Access Journals (Sweden)

    Xiang Yu

    2016-06-01

    Full Text Available Optimal operation of hydropower reservoir systems often needs to optimize multiple conflicting objectives simultaneously. The conflicting objectives result in a Pareto front, which is a set of non-dominated solutions. Non-dominated solutions cannot outperform each other on all the objectives. An optimization framework based on the multi-swarm comprehensive learning particle swarm optimization algorithm is proposed to solve the multi-objective operation of hydropower reservoir systems. Through adopting search techniques such as decomposition, mutation and differential evolution, the algorithm tries to derive multiple non-dominated solutions reasonably distributed over the true Pareto front in one single run, thereby facilitating determining the final tradeoff. The long-term sustainable planning of the Three Gorges cascaded hydropower system consisting of the Three Gorges Dam and Gezhouba Dam located on the Yangtze River in China is studied. Two conflicting objectives, i.e., maximizing hydropower generation and minimizing deviation from the outflow lower target to realize the system’s economic, environmental and social benefits during the drought season, are optimized simultaneously. Experimental results demonstrate that the optimization framework helps to robustly derive multiple feasible non-dominated solutions with satisfactory convergence, diversity and extremity in one single run for the case studied.

  11. Using Artificial Intelligence Techniques to Implement a Multifactor Authentication System

    Directory of Open Access Journals (Sweden)

    Jackson Phiri

    2011-08-01

    Full Text Available The recent years have seen a rise in the number of cases of cyber-crime committed through identity theft and fraud. To address this problem, this paper uses adaptive neural-fuzzy inference system, fuzzy logic and artificial neural network to implement a multifactor authentication system through a technique of information fusion. To begin with, the identity attributes are mined using the three corpora from three major sources namely the social networks, a set of questionnaires and application forms from the various services offered both in the real and cyberspace. The statistical information generated by the corpora is then used to compose an identity attribute metric model. The composed identity attributes metrics values classified as biometrics, device metrics and pseudo metrics are then fused at the score level through a technique of information fusion in a multifactor authentication system by using each of the above artificial intelligence technologies and the results compared.

  12. Modeling and simulation of dynamic ant colony's labor division for task allocation of UAV swarm

    Science.gov (United States)

    Wu, Husheng; Li, Hao; Xiao, Renbin; Liu, Jie

    2018-02-01

    The problem of unmanned aerial vehicle (UAV) task allocation not only has the intrinsic attribute of complexity, such as highly nonlinear, dynamic, highly adversarial and multi-modal, but also has a better practicability in various multi-agent systems, which makes it more and more attractive recently. In this paper, based on the classic fixed response threshold model (FRTM), under the idea of "problem centered + evolutionary solution" and by a bottom-up way, the new dynamic environmental stimulus, response threshold and transition probability are designed, and a dynamic ant colony's labor division (DACLD) model is proposed. DACLD allows a swarm of agents with a relatively low-level of intelligence to perform complex tasks, and has the characteristic of distributed framework, multi-tasks with execution order, multi-state, adaptive response threshold and multi-individual response. With the proposed model, numerical simulations are performed to illustrate the effectiveness of the distributed task allocation scheme in two situations of UAV swarm combat (dynamic task allocation with a certain number of enemy targets and task re-allocation due to unexpected threats). Results show that our model can get both the heterogeneous UAVs' real-time positions and states at the same time, and has high degree of self-organization, flexibility and real-time response to dynamic environments.

  13. 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.

  14. Recurrent neural network-based modeling of gene regulatory network using elephant swarm water search algorithm.

    Science.gov (United States)

    Mandal, Sudip; Saha, Goutam; Pal, Rajat Kumar

    2017-08-01

    Correct inference of genetic regulations inside a cell from the biological database like time series microarray data is one of the greatest challenges in post genomic era for biologists and researchers. Recurrent Neural Network (RNN) is one of the most popular and simple approach to model the dynamics as well as to infer correct dependencies among genes. Inspired by the behavior of social elephants, we propose a new metaheuristic namely Elephant Swarm Water Search Algorithm (ESWSA) to infer Gene Regulatory Network (GRN). This algorithm is mainly based on the water search strategy of intelligent and social elephants during drought, utilizing the different types of communication techniques. Initially, the algorithm is tested against benchmark small and medium scale artificial genetic networks without and with presence of different noise levels and the efficiency was observed in term of parametric error, minimum fitness value, execution time, accuracy of prediction of true regulation, etc. Next, the proposed algorithm is tested against the real time gene expression data of Escherichia Coli SOS Network and results were also compared with others state of the art optimization methods. The experimental results suggest that ESWSA is very efficient for GRN inference problem and performs better than other methods in many ways.

  15. 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.

  16. 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.

  17. 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.

  18. Designing with computational intelligence

    CERN Document Server

    Lopes, Heitor; Mourelle, Luiza

    2017-01-01

    This book discusses a number of real-world applications of computational intelligence approaches. Using various examples, it demonstrates that computational intelligence has become a consolidated methodology for automatically creating new competitive solutions to complex real-world problems. It also presents a concise and efficient synthesis of different systems using computationally intelligent techniques.

  19. 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)

  20. Data Mining for Business Intelligence Concepts, Techniques, and Applications in Microsoft Office Excel(r) with XLMiner(r)

    CERN Document Server

    Shmueli, Galit; Bruce, Peter C

    2011-01-01

    Data Mining for Business Intelligence, Second Edition uses real data and actual cases to illustrate the applicability of data mining (DM) intelligence in the development of successful business models. Featuring complimentary access to XLMiner, the Microsoft Office Excel add-in, this book allows readers to follow along and implement algorithms at their own speed, with a minimal learning curve. In addition, students and practitioners of DM techniques are presented with hands-on, business-oriented applications. An abundant amount of exercises and examples, now doubled in number in the second edit

  1. 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...

  2. Metaheuristic analysis in reverse logistics of waste

    Energy Technology Data Exchange (ETDEWEB)

    Serrano Elena, A.

    2016-07-01

    This paper focuses in the use of search metaheuristic techniques on a dynamic and deterministic model to analyze and solve cost optimization problems and location in reverse logistics, within the field of municipal waste management of Málaga (Spain). In this work we have selected two metaheuristic techniques having relevance in present research, to test the validity of the proposed approach: an important technique for its international presence as is the Genetic Algorithm (GA) and another interesting technique that works with swarm intelligence as is the Particles Swarm Optimization (PSO). These metaheuristic techniques will be used to solve cost optimization problems and location of MSW recovery facilities (transfer centers and treatment plants). (Author)

  3. Particle Swarm Optimization for Structural Design Problems

    Directory of Open Access Journals (Sweden)

    Hamit SARUHAN

    2010-02-01

    Full Text Available The aim of this paper is to employ the Particle Swarm Optimization (PSO technique to a mechanical engineering design problem which is minimizing the volume of a cantilevered beam subject to bending strength constraints. Mechanical engineering design problems are complex activities which are computing capability are more and more required. The most of these problems are solved by conventional mathematical programming techniques that require gradient information. These techniques have several drawbacks from which the main one is becoming trapped in local optima. As an alternative to gradient-based techniques, the PSO does not require the evaluation of gradients of the objective function. The PSO algorithm employs the generation of guided random positions when they search for the global optimum point. The PSO which is a nature inspired heuristics search technique imitates the social behavior of bird flocking. The results obtained by the PSO are compared with Mathematical Programming (MP. It is demonstrated that the PSO performed and obtained better convergence reliability on the global optimum point than the MP. Using the MP, the volume of 2961000 mm3 was obtained while the beam volume of 2945345 mm3 was obtained by the PSO.

  4. 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

  5. A Comparison between Metaheuristics as Strategies for Minimizing Cyclic Instability in Ambient Intelligence

    Directory of Open Access Journals (Sweden)

    Vic Callaghan

    2012-08-01

    Full Text Available In this paper we present a comparison between six novel approaches to the fundamental problem of cyclic instability in Ambient Intelligence. These approaches are based on different optimization algorithms, Particle Swarm Optimization (PSO, Bee Swarm Optimization (BSO, micro Particle Swarm Optimization (μ-PSO, Artificial Immune System (AIS, Genetic Algorithm (GA and Mutual Information Maximization for Input Clustering (MIMIC. In order to be able to use these algorithms, we introduced the concept of Average Cumulative Oscillation (ACO, which enabled us to measure the average behavior of the system. This approach has the advantage that it does not need to analyze the topological properties of the system, in particular the loops, which can be computationally expensive. In order to test these algorithms we used the well-known discrete system called the Game of Life for 9, 25, 49 and 289 agents. It was found that PSO and μ-PSO have the best performance in terms of the number of agents locked. These results were confirmed using the Wilcoxon Signed Rank Test. This novel and successful approach is very promising and can be used to remove instabilities in real scenarios with a large number of agents (including nomadic agents and complex interactions and dependencies among them.

  6. 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.

  7. The use of artificial intelligence techniques to improve the multiple payload integration process

    Science.gov (United States)

    Cutts, Dannie E.; Widgren, Brian K.

    1992-01-01

    A maximum return of science and products with a minimum expenditure of time and resources is a major goal of mission payload integration. A critical component then, in successful mission payload integration is the acquisition and analysis of experiment requirements from the principal investigator and payload element developer teams. One effort to use artificial intelligence techniques to improve the acquisition and analysis of experiment requirements within the payload integration process is described.

  8. 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

  9. Different types of maximum power point tracking techniques for renewable energy systems: A survey

    Science.gov (United States)

    Khan, Mohammad Junaid; Shukla, Praveen; Mustafa, Rashid; Chatterji, S.; Mathew, Lini

    2016-03-01

    Global demand for electricity is increasing while production of energy from fossil fuels is declining and therefore the obvious choice of the clean energy source that is abundant and could provide security for development future is energy from the sun. In this paper, the characteristic of the supply voltage of the photovoltaic generator is nonlinear and exhibits multiple peaks, including many local peaks and a global peak in non-uniform irradiance. To keep global peak, MPPT is the important component of photovoltaic systems. Although many review articles discussed conventional techniques such as P & O, incremental conductance, the correlation ripple control and very few attempts have been made with intelligent MPPT techniques. This document also discusses different algorithms based on fuzzy logic, Ant Colony Optimization, Genetic Algorithm, artificial neural networks, Particle Swarm Optimization Algorithm Firefly, Extremum seeking control method and hybrid methods applied to the monitoring of maximum value of power at point in systems of photovoltaic under changing conditions of irradiance.

  10. A Novel Flexible Inertia Weight Particle Swarm Optimization Algorithm

    Science.gov (United States)

    Shamsi, Mousa; Sedaaghi, Mohammad Hossein

    2016-01-01

    Particle swarm optimization (PSO) is an evolutionary computing method based on intelligent collective behavior of some animals. It is easy to implement and there are few parameters to adjust. The performance of PSO algorithm depends greatly on the appropriate parameter selection strategies for fine tuning its parameters. Inertia weight (IW) is one of PSO’s parameters used to bring about a balance between the exploration and exploitation characteristics of PSO. This paper proposes a new nonlinear strategy for selecting inertia weight which is named Flexible Exponential Inertia Weight (FEIW) strategy because according to each problem we can construct an increasing or decreasing inertia weight strategy with suitable parameters selection. The efficacy and efficiency of PSO algorithm with FEIW strategy (FEPSO) is validated on a suite of benchmark problems with different dimensions. Also FEIW is compared with best time-varying, adaptive, constant and random inertia weights. Experimental results and statistical analysis prove that FEIW improves the search performance in terms of solution quality as well as convergence rate. PMID:27560945

  11. 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.

  12. Discrete ternary particle swarm optimization for area optimization of MPRM circuits

    International Nuclear Information System (INIS)

    Yu Haizhen; Wang Pengjun; Wang Disheng; Zhang Huihong

    2013-01-01

    Having the advantage of simplicity, robustness and low computational costs, the particle swarm optimization (PSO) algorithm is a powerful evolutionary computation tool for synthesis and optimization of Reed-Muller logic based circuits. Exploring discrete PSO and probabilistic transition rules, the discrete ternary particle swarm optimization (DTPSO) is proposed for mixed polarity Reed-Muller (MPRM) circuits. According to the characteristics of mixed polarity OR/XNOR expression, a tabular technique is improved, and it is applied in the polarity conversion of MPRM functions. DTPSO is introduced to search the best polarity for an area of MPRM circuits by building parameter mapping relationships between particles and polarities. The computational results show that the proposed DTPSO outperforms the reported method using maxterm conversion starting from POS Boolean functions. The average saving in the number of terms is about 11.5%; the algorithm is quite efficient in terms of CPU time and achieves 12.2% improvement on average. (semiconductor integrated circuits)

  13. Feature Selection and Parameter Optimization of Support Vector Machines Based on Modified Artificial Fish Swarm Algorithms

    Directory of Open Access Journals (Sweden)

    Kuan-Cheng Lin

    2015-01-01

    Full Text Available Rapid advances in information and communication technology have made ubiquitous computing and the Internet of Things popular and practicable. These applications create enormous volumes of data, which are available for analysis and classification as an aid to decision-making. Among the classification methods used to deal with big data, feature selection has proven particularly effective. One common approach involves searching through a subset of the features that are the most relevant to the topic or represent the most accurate description of the dataset. Unfortunately, searching through this kind of subset is a combinatorial problem that can be very time consuming. Meaheuristic algorithms are commonly used to facilitate the selection of features. The artificial fish swarm algorithm (AFSA employs the intelligence underlying fish swarming behavior as a means to overcome optimization of combinatorial problems. AFSA has proven highly successful in a diversity of applications; however, there remain shortcomings, such as the likelihood of falling into a local optimum and a lack of multiplicity. This study proposes a modified AFSA (MAFSA to improve feature selection and parameter optimization for support vector machine classifiers. Experiment results demonstrate the superiority of MAFSA in classification accuracy using subsets with fewer features for given UCI datasets, compared to the original FASA.

  14. Recent Advances on Hybrid Intelligent Systems

    CERN Document Server

    Melin, Patricia; Kacprzyk, Janusz

    2013-01-01

    This book presents recent advances on hybrid intelligent systems using soft computing techniques for intelligent control and robotics, pattern recognition, time series prediction and optimization of complex problems. Soft Computing (SC) consists of several intelligent computing paradigms, including fuzzy logic, neural networks, and bio-inspired optimization algorithms, which can be used to produce powerful hybrid intelligent systems. The book is organized in five main parts, which contain groups of papers around a similar subject. The first part consists of papers with the main theme of hybrid intelligent systems for control and robotics, which are basically state of the art papers that propose new models and concepts, which can be the basis for achieving intelligent control and mobile robotics. The second part contains papers with the main theme of hybrid intelligent systems for pattern recognition and time series prediction, which are basically papers using nature-inspired techniques, like evolutionary algo...

  15. Intelligent control systems 1990

    International Nuclear Information System (INIS)

    Shoureshi, R.

    1991-01-01

    The field of artificial intelligence (Al) has generated many useful ideas and techniques that can be integrated into the design of control systems. It is believed and, for special cases, has been demonstrated, that integration of Al into control systems would provide the necessary tools for solving many of the complex problems that present control techniques and Al algorithms are unable to do, individually. However, this integration requires the development of basic understanding and new fundamentals to provide scientific bases for achievement of its potential. This book presents an overview of some of the latest research studies in the area of intelligent control systems. These papers present techniques for formulation of intelligent control, and development of the rule-based control systems. Papers present applications of control systems in nuclear power plants and HVAC systems

  16. Swarm intelligence techniques for optimization and management tasks insensor networks

    OpenAIRE

    Hernández Pibernat, Hugo

    2012-01-01

    Premi extraordinari doctorat curs 2011-2012, àmbit Enginyeria de les TIC The main contributions of this thesis are located in the domain of wireless sensor netorks. More in detail, we introduce energyaware algorithms and protocols in the context of the following topics: self-synchronized duty-cycling in networks with energy harvesting capabilities, distributed graph coloring and minimum energy broadcasting with realistic antennas. In the following, we review the research conducted...

  17. A Constructive Data Classification Version of the Particle Swarm Optimization Algorithm

    Directory of Open Access Journals (Sweden)

    Alexandre Szabo

    2013-01-01

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

  18. Using Game Theory Techniques and Concepts to Develop Proprietary Models for Use in Intelligent Games

    Science.gov (United States)

    Christopher, Timothy Van

    2011-01-01

    This work is about analyzing games as models of systems. The goal is to understand the techniques that have been used by game designers in the past, and to compare them to the study of mathematical game theory. Through the study of a system or concept a model often emerges that can effectively educate students about making intelligent decisions…

  19. 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...

  20. 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).

  1. 8th International Symposium on Intelligent Distributed Computing & Workshop on Cyber Security and Resilience of Large-Scale Systems & 6th International Workshop on Multi-Agent Systems Technology and Semantics

    CERN Document Server

    Braubach, Lars; Venticinque, Salvatore; Badica, Costin

    2015-01-01

    This book represents the combined peer-reviewed proceedings of the Eight International Symposium on Intelligent Distributed Computing - IDC'2014, of the Workshop on Cyber Security and Resilience of Large-Scale Systems - WSRL-2014, and of the Sixth International Workshop on Multi-Agent Systems Technology and Semantics- MASTS-2014. All the events were held in Madrid, Spain, during September 3-5, 2014. The 47 contributions published in this book address several topics related to theory and applications of the intelligent distributed computing and multi-agent systems, including: agent-based data processing, ambient intelligence, collaborative systems, cryptography and security, distributed algorithms, grid and cloud computing, information extraction, knowledge management, big data and ontologies, social networks, swarm intelligence or videogames amongst others.

  2. Computational intelligence-based optimization of maximally stable extremal region segmentation for object detection

    Science.gov (United States)

    Davis, Jeremy E.; Bednar, Amy E.; Goodin, Christopher T.; Durst, Phillip J.; Anderson, Derek T.; Bethel, Cindy L.

    2017-05-01

    Particle swarm optimization (PSO) and genetic algorithms (GAs) are two optimization techniques from the field of computational intelligence (CI) for search problems where a direct solution can not easily be obtained. One such problem is finding an optimal set of parameters for the maximally stable extremal region (MSER) algorithm to detect areas of interest in imagery. Specifically, this paper describes the design of a GA and PSO for optimizing MSER parameters to detect stop signs in imagery produced via simulation for use in an autonomous vehicle navigation system. Several additions to the GA and PSO are required to successfully detect stop signs in simulated images. These additions are a primary focus of this paper and include: the identification of an appropriate fitness function, the creation of a variable mutation operator for the GA, an anytime algorithm modification to allow the GA to compute a solution quickly, the addition of an exponential velocity decay function to the PSO, the addition of an "execution best" omnipresent particle to the PSO, and the addition of an attractive force component to the PSO velocity update equation. Experimentation was performed with the GA using various combinations of selection, crossover, and mutation operators and experimentation was also performed with the PSO using various combinations of neighborhood topologies, swarm sizes, cognitive influence scalars, and social influence scalars. The results of both the GA and PSO optimized parameter sets are presented. This paper details the benefits and drawbacks of each algorithm in terms of detection accuracy, execution speed, and additions required to generate successful problem specific parameter sets.

  3. Algorithms in ambient intelligence

    NARCIS (Netherlands)

    Aarts, E.H.L.; Korst, J.H.M.; Verhaegh, W.F.J.; Verhaegh, W.F.J.; Aarts, E.H.L.; Korst, J.H.M.

    2004-01-01

    In this chapter, we discuss the new paradigm for user-centered computing known as ambient intelligence and its relation with methods and techniques from the field of computational intelligence, including problem solving, machine learning, and expert systems.

  4. REVIEW OF HEART DISEASE PREDICTION SYSTEM USING DATA MINING AND HYBRID INTELLIGENT TECHNIQUES

    Directory of Open Access Journals (Sweden)

    R. Chitra

    2013-07-01

    Full Text Available The Healthcare industry generally clinical diagnosis is done mostly by doctor’s expertise and experience. Computer Aided Decision Support System plays a major role in medical field. With the growing research on heart disease predicting system, it has become important to categories the research outcomes and provides readers with an overview of the existing heart disease prediction techniques in each category. Neural Networks are one of many data mining analytical tools that can be utilized to make predictions for medical data. From the study it is observed that Hybrid Intelligent Algorithm improves the accuracy of the heart disease prediction system. The commonly used techniques for Heart Disease Prediction and their complexities are summarized in this paper.

  5. Development of an Accident Diagnostic Scheme Using Artificial Intelligence Techniques (I)

    Energy Technology Data Exchange (ETDEWEB)

    Na, M. G.; Lee, S. H.; Kim, D. S.; No, Y. G.; Lee, S. W. [Chosun University, Gwangju (Korea, Republic of); Ahn, K. I. [Korea Atomic Energy Research Institute, Daejeon (Korea, Republic of)

    2010-06-15

    As a means to effectively manage the severe nuclear accidents, it is important to identify and diagnose the accident initiating events during an initial short time interval after the accidents by observing the major controlling parameters. Main objective of this study is to develop the diagnostic approach for the accurate prediction of accident initiating events using artificial intelligence techniques. For this, first, a variety of artificial intelligence techniques such as Finn, Gmbh, and Sm were examined through this study. Among them, Sc and Gmbh model were assessed as a useful approach to predict the break location and the break size of Local. In order to verify the proposed algorithm, the 111 accident simulation data (based on Map) were applied to train the Sc and Gmbh models, and the test data was used to independently verify whether or not the SVC and GMDH models work well. The analysis of the maximum errors and RMS errors, and the performance of the GMDH according to the existence of measurement errors and SIS actuation showed that the proposed SVC and GMDH models can accurately classify the break locations and accurately predict the break size. As the time-integrated signals were used for inputs into the GMDH model within a period of 60 second after a reactor scram, the actuation of the safety systems such as safety injection system (SIS), auxiliary feed water system, and containment spray system, were not considered in this study. It is because the initial 60 second time-integrated signals were used and the safety systems usually start to actuate after a more than 60 second time delay after the reactor scram

  6. Development of an Accident Diagnostic Scheme Using Artificial Intelligence Techniques (I)

    International Nuclear Information System (INIS)

    Na, M. G.; Lee, S. H.; Kim, D. S.; No, Y. G.; Lee, S. W.; Ahn, K. I.

    2010-06-01

    As a means to effectively manage the severe nuclear accidents, it is important to identify and diagnose the accident initiating events during an initial short time interval after the accidents by observing the major controlling parameters. Main objective of this study is to develop the diagnostic approach for the accurate prediction of accident initiating events using artificial intelligence techniques. For this, first, a variety of artificial intelligence techniques such as Finn, Gmbh, and Sm were examined through this study. Among them, Sc and Gmbh model were assessed as a useful approach to predict the break location and the break size of Local. In order to verify the proposed algorithm, the 111 accident simulation data (based on Map) were applied to train the Sc and Gmbh models, and the test data was used to independently verify whether or not the SVC and GMDH models work well. The analysis of the maximum errors and RMS errors, and the performance of the GMDH according to the existence of measurement errors and SIS actuation showed that the proposed SVC and GMDH models can accurately classify the break locations and accurately predict the break size. As the time-integrated signals were used for inputs into the GMDH model within a period of 60 second after a reactor scram, the actuation of the safety systems such as safety injection system (SIS), auxiliary feed water system, and containment spray system, were not considered in this study. It is because the initial 60 second time-integrated signals were used and the safety systems usually start to actuate after a more than 60 second time delay after the reactor scram

  7. Application of PSO (particle swarm optimization) and GA (genetic algorithm) techniques on demand estimation of oil in Iran

    International Nuclear Information System (INIS)

    Assareh, E.; Behrang, M.A.; Assari, M.R.; Ghanbarzadeh, A.

    2010-01-01

    This paper presents application of PSO (Particle Swarm Optimization) and GA (Genetic Algorithm) techniques to estimate oil demand in Iran, based on socio-economic indicators. The models are developed in two forms (exponential and linear) and applied to forecast oil demand in Iran. PSO-DEM and GA-DEM (PSO and GA demand estimation models) are developed to estimate the future oil demand values based on population, GDP (gross domestic product), import and export data. Oil consumption in Iran from 1981 to 2005 is considered as the case of this study. The available data is partly used for finding the optimal, or near optimal values of the weighting parameters (1981-1999) and partly for testing the models (2000-2005). For the best results of GA, the average relative errors on testing data were 2.83% and 1.72% for GA-DEM exponential and GA-DEM linear , respectively. The corresponding values for PSO were 1.40% and 1.36% for PSO-DEM exponential and PSO-DEM linear , respectively. Oil demand in Iran is forecasted up to year 2030. (author)

  8. 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.

  9. 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)

  10. Formation control of robotic swarm using bounded artificial forces.

    Science.gov (United States)

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

    2013-01-01

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

  11. 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.

  12. 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.

  13. Strategic bidding in electricity markets using particle swarm optimization

    International Nuclear Information System (INIS)

    Yucekaya, Ahmet D.; Valenzuela, Jorge; Dozier, Gerry

    2009-01-01

    Profit maximization for power companies is highly related to the bidding strategies used. In order to sell electricity at high prices and maximize profit, power companies need suitable bidding models that consider power operating constraints and price uncertainty within the market. In this paper, we present two particle swarm optimization (PSO) algorithms to determine bid prices and quantities under the rules of a competitive power market. The first method uses a conventional PSO technique to find solutions. The second method uses a decomposition technique in conjunction with the PSO approach. This new decomposition-based PSO dramatically outperforms the conventional form of PSO. We show that for nonlinear cost functions PSO solutions provide higher expected profits than marginal cost-based bidding. (author)

  14. Optimization of Mangala Hydropower Station, Pakistan, using Optimization Techniques

    Directory of Open Access Journals (Sweden)

    Zaman Muhammad

    2017-01-01

    Full Text Available Hydropower generation is one of the key element in the economy of a country. The present study focusses on the optimal electricity generation from the Mangla reservoir in Pakistan. A mathematical model has been developed for the Mangla hydropower station and particle swarm and genetic algorithm optimization techniques were applied at this model for optimal electricity generation. Results revealed that electricity production increases with the application of optimization techniques at the proposed mathematical model. Genetic Algorithm can produce maximum electricity than Particle swarm optimization but the time of execution of particle swarm optimization is much lesser than the Genetic algorithm. Mangla hydropower station can produce up to 59*109 kWh electricity by using the flows optimally than 47*108 kWh production from traditional methods.

  15. Integrating Symbolic and Statistical Methods for Testing Intelligent Systems Applications to Machine Learning and Computer Vision

    Energy Technology Data Exchange (ETDEWEB)

    Jha, Sumit Kumar [University of Central Florida, Orlando; Pullum, Laura L [ORNL; Ramanathan, Arvind [ORNL

    2016-01-01

    Embedded intelligent systems ranging from tiny im- plantable biomedical devices to large swarms of autonomous un- manned aerial systems are becoming pervasive in our daily lives. While we depend on the flawless functioning of such intelligent systems, and often take their behavioral correctness and safety for granted, it is notoriously difficult to generate test cases that expose subtle errors in the implementations of machine learning algorithms. Hence, the validation of intelligent systems is usually achieved by studying their behavior on representative data sets, using methods such as cross-validation and bootstrapping.In this paper, we present a new testing methodology for studying the correctness of intelligent systems. Our approach uses symbolic decision procedures coupled with statistical hypothesis testing to. We also use our algorithm to analyze the robustness of a human detection algorithm built using the OpenCV open-source computer vision library. We show that the human detection implementation can fail to detect humans in perturbed video frames even when the perturbations are so small that the corresponding frames look identical to the naked eye.

  16. Strength Pareto particle swarm optimization and hybrid EA-PSO for multi-objective optimization.

    Science.gov (United States)

    Elhossini, Ahmed; Areibi, Shawki; Dony, Robert

    2010-01-01

    This paper proposes an efficient particle swarm optimization (PSO) technique that can handle multi-objective optimization problems. It is based on the strength Pareto approach originally used in evolutionary algorithms (EA). The proposed modified particle swarm algorithm is used to build three hybrid EA-PSO algorithms to solve different multi-objective optimization problems. This algorithm and its hybrid forms are tested using seven benchmarks from the literature and the results are compared to the strength Pareto evolutionary algorithm (SPEA2) and a competitive multi-objective PSO using several metrics. The proposed algorithm shows a slower convergence, compared to the other algorithms, but requires less CPU time. Combining PSO and evolutionary algorithms leads to superior hybrid algorithms that outperform SPEA2, the competitive multi-objective PSO (MO-PSO), and the proposed strength Pareto PSO based on different metrics.

  17. 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.

  18. 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

  19. 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.

  20. 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.