Naeem, Huma; Hussain, Mukhtar; Khan, Shoab A
2009-01-01
Surveillance control and reporting (SCR) system for air threats play an important role in the defense of a country. SCR system corresponds to air and ground situation management/processing along with information fusion, communication, coordination, simulation and other critical defense oriented tasks. Threat Evaluation and Weapon Assignment (TEWA) sits at the core of SCR system. In such a system, maximal or near maximal utilization of constrained resources is of extreme importance. Manual TEWA systems cannot provide optimality because of different limitations e.g.surface to air missile (SAM) can fire from a distance of 5Km, but manual TEWA systems are constrained by human vision range and other constraints. Current TEWA systems usually work on target-by-target basis using some type of greedy algorithm thus affecting the optimality of the solution and failing in multi-target scenario. his paper relates to a novel two-staged flexible dynamic decision support based optimal threat evaluation and weapon assignment...
Symplectic algebraic dynamics algorithm
2007-01-01
Based on the algebraic dynamics solution of ordinary differential equations andintegration of ,the symplectic algebraic dynamics algorithm sn is designed,which preserves the local symplectic geometric structure of a Hamiltonian systemand possesses the same precision of the na ve algebraic dynamics algorithm n.Computer experiments for the 4th order algorithms are made for five test modelsand the numerical results are compared with the conventional symplectic geometric algorithm,indicating that sn has higher precision,the algorithm-inducedphase shift of the conventional symplectic geometric algorithm can be reduced,and the dynamical fidelity can be improved by one order of magnitude.
Evolutionary Algorithms and Dynamic Programming
Doerr, Benjamin; Eremeev, Anton; Neumann, Frank; Theile, Madeleine; Thyssen, Christian
2013-01-01
Recently, it has been proven that evolutionary algorithms produce good results for a wide range of combinatorial optimization problems. Some of the considered problems are tackled by evolutionary algorithms that use a representation which enables them to construct solutions in a dynamic programming fashion. We take a general approach and relate the construction of such algorithms to the development of algorithms using dynamic programming techniques. Thereby, we give general guidelines on how ...
Inventory Management with Asset-Based Financing
John A. Buzacott; Rachel Q. Zhang
2004-01-01
Most of the traditional models in production and inventory control ignore the financial states of an organization and can lead to infeasible practices in real systems. This paper is the first attempt to incorporate asset-based financing into production decisions. Instead of setting a known, exogenously determined budgetary constraint as most existing models suggest, we model the available cash in each period as a function of assets and liabilities that may be updated periodically according to...
2007-01-01
Based on the exact analytical solution of ordinary differential equations, a truncation of the Taylor series of the exact solution to the Nth order leads to the Nth order algebraic dynamics algorithm. A detailed numerical comparison is presented with Runge-Kutta algorithm and symplectic geometric algorithm for 12 test models. The results show that the algebraic dynamics algorithm can better preserve both geometrical and dynamical fidelity of a dynamical system at a controllable precision, and it can solve the problem of algorithm-induced dissipation for the Runge-Kutta algorithm and the problem of algorithm-induced phase shift for the symplectic geometric algorithm.
WANG ShunJin; ZHANG Hua
2007-01-01
Based on the exact analytical solution of ordinary differential equations,a truncation of the Taylor series of the exact solution to the Nth order leads to the Nth order algebraic dynamics algorithm.A detailed numerical comparison is presented with Runge-Kutta algorithm and symplectic geometric algorithm for 12 test models.The results show that the algebraic dynamics algorithm can better preserve both geometrical and dynamical fidelity of a dynamical system at a controllable precision,and it can solve the problem of algorithm-induced dissipation for the Runge-Kutta algorithm and the problem of algorithm-induced phase shift for the symplectic geometric algorithm.
Control algorithms for dynamic attenuators
Hsieh, Scott S., E-mail: sshsieh@stanford.edu [Department of Radiology, Stanford University, Stanford, California 94305 and Department of Electrical Engineering, Stanford University, Stanford, California 94305 (United States); Pelc, Norbert J. [Department of Radiology, Stanford University, Stanford California 94305 and Department of Bioengineering, Stanford University, Stanford, California 94305 (United States)
2014-06-15
Purpose: The authors describe algorithms to control dynamic attenuators in CT and compare their performance using simulated scans. Dynamic attenuators are prepatient beam shaping filters that modulate the distribution of x-ray fluence incident on the patient on a view-by-view basis. These attenuators can reduce dose while improving key image quality metrics such as peak or mean variance. In each view, the attenuator presents several degrees of freedom which may be individually adjusted. The total number of degrees of freedom across all views is very large, making many optimization techniques impractical. The authors develop a theory for optimally controlling these attenuators. Special attention is paid to a theoretically perfect attenuator which controls the fluence for each ray individually, but the authors also investigate and compare three other, practical attenuator designs which have been previously proposed: the piecewise-linear attenuator, the translating attenuator, and the double wedge attenuator. Methods: The authors pose and solve the optimization problems of minimizing the mean and peak variance subject to a fixed dose limit. For a perfect attenuator and mean variance minimization, this problem can be solved in simple, closed form. For other attenuator designs, the problem can be decomposed into separate problems for each view to greatly reduce the computational complexity. Peak variance minimization can be approximately solved using iterated, weighted mean variance (WMV) minimization. Also, the authors develop heuristics for the perfect and piecewise-linear attenuators which do not requirea priori knowledge of the patient anatomy. The authors compare these control algorithms on different types of dynamic attenuators using simulated raw data from forward projected DICOM files of a thorax and an abdomen. Results: The translating and double wedge attenuators reduce dose by an average of 30% relative to current techniques (bowtie filter with tube current
Efficient Algorithms for Langevin and DPD Dynamics
Goga, N.; Rzepiela, A. J.; de Vries, A. H.; Marrink, S. J.; Berendsen, H. J. C.
2012-01-01
In this article, we present several algorithms for stochastic dynamics, including Langevin dynamics and different variants of Dissipative Particle Dynamics (DPD), applicable to systems with or without constraints. The algorithms are based on the impulsive application of friction and noise, thus avoi
A Unifying Multibody Dynamics Algorithm Development Workbench
Ziegler, John L.
2005-01-01
The development of new and efficient algorithms for multibody dynamics has been an important research area. These algorithms are used for modeling, simulation, and control of systems such as spacecraft, robotic systems, automotive applications, the human body, manufacturing operations, and micro-electromechanical systems (MEMS). At JPL's Dynamics and Real Time Simulation (DARTS) Laboratory we have developed software that serves as a computational workbench for these algorithms. This software utilizes the mathematical perspective of the spatial operator algebra, which allows the development of dynamics algorithms and new insights into multibody dynamics.
Algebraic dynamics solution and algebraic dynamics algorithm of Burgers equations
2008-01-01
Algebraic dynamics solution and algebraic dynamics algorithm of nonlinear partial differential evolution equations in the functional space are applied to Burgers equation. The results indicate that the approach is effective for analytical solutions to Burgers equation, and the algorithm for numerical solutions of Burgers equation is more stable, with higher precision than other existing finite difference algo-rithms.
Dynamic Programming Algorithms in Speech Recognition
Titus Felix FURTUNA
2008-01-01
Full Text Available In a system of speech recognition containing words, the recognition requires the comparison between the entry signal of the word and the various words of the dictionary. The problem can be solved efficiently by a dynamic comparison algorithm whose goal is to put in optimal correspondence the temporal scales of the two words. An algorithm of this type is Dynamic Time Warping. This paper presents two alternatives for implementation of the algorithm designed for recognition of the isolated words.
Dynamic Streaming Algorithms for Epsilon-Kernels
Chan, Timothy M.
2016-01-01
Introduced by Agarwal, Har-Peled, and Varadarajan [J. ACM, 2004], an epsilon-kernel of a point set is a coreset that can be used to approximate the width, minimum enclosing cylinder, minimum bounding box, and solve various related geometric optimization problems. Such coresets form one of the most important tools in the design of linear-time approximation algorithms in computational geometry, as well as efficient insertion-only streaming algorithms and dynamic (non-streaming) data structures...
Algorithms for Lattice QCD with Dynamical Fermions
We consider recent progress in algorithms for generating gauge field configurations that include the dynamical effects of light fermions. We survey what has been achieved in recent state-of-the-art computations, and examine the trade-offs between performance and control of systematic errors. We briefly review the use of polynomial and rational approximations in Hybrid Monte Carlo algorithms, and some of the theory of on-shell chiral fermions on the lattice. This provides a theoretical framework within which we compare algorithmic alternatives for their implementation; and again we examine the trade-offs between speed and error control
DYNAMIC BANDWIDTH ALLOCATION ALGORITHM UTILIZING FULL BAND
Han Guodong; Wen Jianhua; Wu Jiangxing
2006-01-01
A kind of Dynamic Full Bandwidth Utilized (DFBU) allocation algorithm is introduced. This algorithm allows a single link to use bandwidth far beyond its fair share bandwidth in a multi-service packet transporting system. Three important parameters as the bound on maximum and minimum bandwidth, the maximum packet delay and the minimum band width utilization are discussed and analyzed. Results of experiments show that the DFBU-algorithm is capable of making a single link in the system to use all the spare bandwidth (up to full-bandwidth) while the performance of fairness and QoS requirement is still guaranteed.
Cell list algorithms for nonequilibrium molecular dynamics
Dobson, Matthew; Fox, Ian; Saracino, Alexandra
2016-06-01
We present two modifications of the standard cell list algorithm that handle molecular dynamics simulations with deforming periodic geometry. Such geometry naturally arises in the simulation of homogeneous, linear nonequilibrium flow modeled with periodic boundary conditions, and recent progress has been made developing boundary conditions suitable for general 3D flows of this type. Previous works focused on the planar flows handled by Lees-Edwards or Kraynik-Reinelt boundary conditions, while the new versions of the cell list algorithm presented here are formulated to handle the general 3D deforming simulation geometry. As in the case of equilibrium, for short-ranged pairwise interactions, the cell list algorithm reduces the computational complexity of the force computation from O(N2) to O(N), where N is the total number of particles in the simulation box. We include a comparison of the complexity and efficiency of the two proposed modifications of the standard algorithm.
Algorithm for dynamic Speckle pattern processing
Cariñe, J.; Guzmán, R.; Torres-Ruiz, F. A.
2016-07-01
In this paper we present a new algorithm for determining surface activity by processing speckle pattern images recorded with a CCD camera. Surface activity can be produced by motility or small displacements among other causes, and is manifested as a change in the pattern recorded in the camera with reference to a static background pattern. This intensity variation is considered to be a small perturbation compared with the mean intensity. Based on a perturbative method we obtain an equation with which we can infer information about the dynamic behavior of the surface that generates the speckle pattern. We define an activity index based on our algorithm that can be easily compared with the outcomes from other algorithms. It is shown experimentally that this index evolves in time in the same way as the Inertia Moment method, however our algorithm is based on direct processing of speckle patterns without the need for other kinds of post-processes (like THSP and co-occurrence matrix), making it a viable real-time method. We also show how this algorithm compares with several other algorithms when applied to calibration experiments. From these results we conclude that our algorithm offer qualitative and quantitative advantages over current methods.
Local minimization algorithms for dynamic programming equations
Kalise, Dante; Kröner, Axel; Kunisch, Karl
2015-01-01
The numerical realization of the dynamic programming principle for continuous-time optimal control leads to nonlinear Hamilton-Jacobi-Bellman equations which require the minimization of a nonlinear mapping over the set of admissible controls. This minimization is often performed by comparison over a finite number of elements of the control set. In this paper we demonstrate the importance of an accurate realization of these minimization problems and propose algorithms by which this can be achi...
Benchmarking dynamic Bayesian network structure learning algorithms
Trabelsi, Ghada; Leray, Philippe; Ben Ayed, Mounir; Alimi, Adel
2012-01-01
Dynamic Bayesian Networks (DBNs) are probabilistic graphical models dedicated to modeling multivariate time series. Two-time slice BNs (2-TBNs) are the most current type of these models. Static BN structure learning is a well-studied domain. Many approaches have been proposed and the quality of these algorithms has been studied over a range of di erent standard networks and methods of evaluation. To the best of our knowledge, all studies about DBN structure learning use their own benchmarks a...
An Improved Dynamic Bandwidth Allocation Algorithm for Ethernet PON
无
2003-01-01
This paper proposes an improved Dynamic Bandwidth Allocation (DBA) algorithm for EPON, which combines static and traditional dynamic allocation schemes. Simulation result shows that the proposed algorithm may effectively improve the performance of packet delay.
Fundamental algorithms in computational fluid dynamics
Pulliam, Thomas H
2014-01-01
Intended as a textbook for courses in computational fluid dynamics at the senior undergraduate or graduate level, this book is a follow-up to the book Fundamentals of Computational Fluid Dynamics by the same authors, which was published in the series Scientific Computation in 2001. Whereas the earlier book concentrated on the analysis of numerical methods applied to model equations, this new book concentrates on algorithms for the numerical solution of the Euler and Navier-Stokes equations. It focuses on some classical algorithms as well as the underlying ideas based on the latest methods. A key feature of the book is the inclusion of programming exercises at the end of each chapter based on the numerical solution of the quasi-one-dimensional Euler equations and the shock-tube problem. These exercises can be included in the context of a typical course, and sample solutions are provided in each chapter, so readers can confirm that they have coded the algorithms correctly.
Enhanced Dynamic Algorithm of Genome Sequence Alignments
Arabi E. keshk
2014-05-01
Full Text Available The merging of biology and computer science has created a new field called computational biology that explore the capacities of computers to gain knowledge from biological data, bioinformatics. Computational biology is rooted in life sciences as well as computers, information sciences, and technologies. The main problem in computational biology is sequence alignment that is a way of arranging the sequences of DNA, RNA or protein to identify the region of similarity and relationship between sequences. This paper introduces an enhancement of dynamic algorithm of genome sequence alignment, which called EDAGSA. It is filling the three main diagonals without filling the entire matrix by the unused data. It gets the optimal solution with decreasing the execution time and therefore the performance is increased. To illustrate the effectiveness of optimizing the performance of the proposed algorithm, it is compared with the traditional methods such as Needleman-Wunsch, Smith-Waterman and longest common subsequence algorithms. Also, database is implemented for using the algorithm in multi-sequence alignments for searching the optimal sequence that matches the given sequence.
Dynamic hierarchical algorithm for accelerated microfossil identification
Wong, Cindy M.; Joseph, Dileepan
2015-02-01
Marine microfossils provide a useful record of the Earth's resources and prehistory via biostratigraphy. To study Hydrocarbon reservoirs and prehistoric climate, geoscientists visually identify the species of microfossils found in core samples. Because microfossil identification is labour intensive, automation has been investigated since the 1980s. With the initial rule-based systems, users still had to examine each specimen under a microscope. While artificial neural network systems showed more promise for reducing expert labour, they also did not displace manual identification for a variety of reasons, which we aim to overcome. In our human-based computation approach, the most difficult step, namely taxon identification is outsourced via a frontend website to human volunteers. A backend algorithm, called dynamic hierarchical identification, uses unsupervised, supervised, and dynamic learning to accelerate microfossil identification. Unsupervised learning clusters specimens so that volunteers need not identify every specimen during supervised learning. Dynamic learning means interim computation outputs prioritize subsequent human inputs. Using a dataset of microfossils identified by an expert, we evaluated correct and incorrect genus and species rates versus simulated time, where each specimen identification defines a moment. The proposed algorithm accelerated microfossil identification effectively, especially compared to benchmark results obtained using a k-nearest neighbour method.
An Improved Artificial Immune Algorithm with a Dynamic Threshold
Zhang Qiao; Xu Xu; Liang Yan-chun
2006-01-01
An improved artificial immune algorithm with a dynamic threshold is presented. The calculation for the affinity function in the real-valued coding artificial immune algorithm is modified through considering the antibody's fitness and setting the dynamic threshold value. Numerical experiments show that compared with the genetic algorithm and the originally real-valued coding artificial immune algorithm, the improved algorithm possesses high speed of convergence and good performance for preventing premature convergence.
Dynamic Traffic Light Sequence Algorithm Using RFID
Khalid A.S. Al-Khateeb
2008-01-01
Full Text Available Problem statement: Traffic congestion and tidal flow management were recognized as major problems in modern urban areas, which have caused much frustration and loss of man hours. Approach: In order to solve the problem an intelligent RFID traffic control has been developed. It has circumvented or avoided the problems that usually arise with systems such as those, which use image processing and beam interruption techniques. RFID technology with appropriate algorithm and data base were applied to a multi vehicle, multi lane and multi road junction area to provide an efficient time management scheme. A dynamic time schedule was worked out for the passage of each column. Results: The simulation has shown that, the dynamic sequence algorithm has the ability to intelligently adjust itself even with the presence of some extreme cases. The real time operation of the system emulated the judgment of a traffic policeman on duty, by considering the number of vehicles in each column and the routing proprieties. Conclusions/Recommendations: RFID together with Internet and GSM technologies are anticipated to create a revolution in traffic management and control systems. The data base contains online statistical information, which can be used by operators and planners to develop better models in the future.
Optimized Dynamical Decoupling via Genetic Algorithms
Quiroz, Gregory
2013-01-01
We utilize genetic algorithms to find optimal dynamical decoupling (DD) sequences for a single-qubit system subjected to a general decoherence model under a variety of control pulse conditions. We focus on the case of sequences with equal pulse-intervals and perform the optimization with respect to pulse type and order. In this manner we obtain robust DD sequences, first in the limit of ideal pulses, then when including pulse imperfections such as finite pulse duration and qubit rotation (flip-angle) errors. Although our optimization is numerical, we identify a deterministic structure underlies the top-performing sequences. We use this structure to devise DD sequences which outperform previously designed concatenated DD (CDD) and quadratic DD (QDD) sequences in the presence of pulse errors. We explain our findings using time-dependent perturbation theory and provide a detailed scaling analysis of the optimal sequences.
Tanti Octavia
2003-01-01
Full Text Available A Modified Giffler and Thompson algorithm combined with dynamic slack time is used to allocate machines resources in dynamic nature. It was compared with a Real Time Order Promising (RTP algorithm. The performance of modified Giffler and Thompson and RTP algorithms are measured by mean tardiness. The result shows that modified Giffler and Thompson algorithm combined with dynamic slack time provides significantly better result compared with RTP algorithm in terms of mean tardiness.
Asset-Based Assessment in Educational Psychology: Capturing Perceptions during a Paradigm Shift
Lubbe, Carien; Eloff, Irma
2004-01-01
Several trends are compelling educational psychologists towards a philosophy of assessment that is asset-based and strength focused. This article shares the results from a study that explored perceptions about asset-based assessment in Educational Psychology in South Africa. Three focus groups were held and four main themes emerged from the…
Parallel-Processing Algorithms For Dynamics Of Manipulators
Fijany, Amir; Bejczy, Antal K.
1991-01-01
Class of parallel and parallel/pipeline algorithms presented for more efficient computation of manipulator inertia matrix. Essential for implementing advanced dynamic control schemes as well as dynamic simulation of manipulator motion.
Efficient Intelligent Optimized Algorithm for Dynamic Vehicle Routing Problem
Jiangqing Wang
2011-11-01
Full Text Available In order to solve the dynamic vehicle routing problem (DVRP containing both dynamic network environment and real-time customer requests, an efficient intelligent optimized algorithm called IOA is proposed in this paper, which takes advantages of both global searching ability of evolutionary algorithms and local searching capability of ant colony algorithm. The proposed IOA incorporates ant colony algorithm for exploration and evolutionary algorithm for exploitation, and uses real-time information during the optimization process. In order to discuss the performance of the proposed algorithm, a mixed integral programming model for DVRP is formulated, and benchmark functions are constructed. Detailed simulation results and comparisons with the existed work show that the proposed IOA algorithm can achieve a higher performance gain, and is well suited to problems containing dynamic network environment and real-time customer requests.
On an algorithm for dynamic reconstruction of the input
Blizorukova, M. S.; V.I. Maksimov
2013-01-01
We consider the problem of dynamic reconstruction of the input in a system described by a vector differential equation and nonlinear in the state variable. We indicate an algorithm that is stable under information noises and computational errors and is aimed at infinite system operation time. The algorithm is based on the dynamic regularization method. © 2013 Pleiades Publishing, Ltd.
Algebraic dynamics solution to and algebraic dynamics algorithm for nonlinear advection equation
2008-01-01
Algebraic dynamics approach and algebraic dynamics algorithm for the solution of nonlinear partial differential equations are applied to the nonlinear advection equa-tion. The results show that the approach is effective for the exact analytical solu-tion and the algorithm has higher precision than other existing algorithms in nu-merical computation for the nonlinear advection equation.
Intersecting Asset-Based Service, Strengths, and Mentoring for Socially Responsible Leadership.
Hastings, Lindsay
2016-06-01
Grounded in a youth leadership and mentoring program, this chapter discusses the value of asset-based community development from the service-learning literature and the concept of generativity from the leadership development literature. PMID:27150907
Dynamic Uniform Scaling for Multiobjective Genetic Algorithms
Pedersen, Gerulf; Goldberg, David E.
2004-01-01
Before Multiobjective Evolutionary Algorithms (MOEAs) can be used as a widespread tool for solving arbitrary real world problems there are some salient issues which require further investigation. One of these issues is how a uniform distribution of solutions along the Pareto non-dominated front can......, the issue of obtaining a diverse set of solutions for badly scaled objective functions will be investigated and proposed solutions will be implemented using the NSGA-II algorithm....
Dynamic Obfuscation Algorithm based on Demand-Driven Symbolic Execution
Yubo Yang
2014-06-01
Full Text Available Dynamic code obfuscation technique increases the difficulty of dynamically reverse by the runtime confusion. Path explosion directly affects the efficiency and accuracy of dynamic symbolic analysis. Because of the defect, this paper presents a novel algorithm DDD (Demand-Driven Dynamic Obfuscation Algorithm by using the demand-driven theory of symbolic analysis. First, create a large number of invalid paths to mislead the result of symbolic analysis. Second, according to the demand-driven theory, create a specific execution path to protect the security of software. The design and implementation of the algorithm is based on the current popular and mature SMT (satisfiability model theory, and the experimental effects are tested by Z3 - the SMT solver and Pex - the symbolic execution test tools. The experimental results prove that the algorithm enhance the security of the program.
New MPPT algorithm based on hybrid dynamical theory
Elmetennani, Shahrazed
2014-11-01
This paper presents a new maximum power point tracking algorithm based on the hybrid dynamical theory. A multiceli converter has been considered as an adaptation stage for the photovoltaic chain. The proposed algorithm is a hybrid automata switching between eight different operating modes, which has been validated by simulation tests under different working conditions. © 2014 IEEE.
WANG Shundin; ZHANG Hua
2008-01-01
Using functional derivative technique In quantum field theory,the algebraic dy-namics approach for solution of ordinary differential evolution equations was gen-eralized to treat partial differential evolution equations.The partial differential evo-lution equations were lifted to the corresponding functional partial differential equations in functional space by Introducing the time translation operator.The functional partial differential evolution equations were solved by algebraic dynam-ics.The algebraic dynamics solutions are analytical In Taylor series In terms of both initial functions and time.Based on the exact analytical solutions,a new nu-merical algorithm-algebraic dynamics algorithm was proposed for partial differ-ential evolution equations.The difficulty of and the way out for the algorithm were discussed.The application of the approach to and computer numerical experi-ments on the nonlinear Burgers equation and meteorological advection equation indicate that the algebraic dynamics approach and algebraic dynamics algorithm are effective to the solution of nonlinear partial differential evolution equations both analytically and numerically.
Dynamic multi DAG scheduling algorithm for optical grid environment
Zhu, Liying; Sun, Zhenyu; Guo, Wei; Jin, Yaohui; Sun, Weiqiang; Hu, Weisheng
2007-11-01
Facing the evolvement of the Optical Grid technology, dynamic task scheduling can largely improve the efficiency of the Grid environment under the real circumstances. We propose a Serve On Time (SOT) algorithm - based on the idea of combining all the dynamic multi tasks so that all the tasks will obtain the rights to be served as soon as possible. We then introduce the basic First Come First Serve (FCFS) algorithm. A simulation will show the advantage of SOT.
Multi-Particle Collision Dynamics Algorithm for Nematic Fluids
Shendruk, Tyler N.; Yeomans, Julia M.
2015-01-01
Research on transport, self-assembly and defect dynamics within confined, flowing liquid crystals requires versatile and computationally efficient mesoscopic algorithms to account for fluctuating nematohydrodynamic interactions. We present a multi-particle collision dynamics (MPCD) based algorithm to simulate liquid-crystal hydrodynamic and director fields in two and three dimensions. The nematic-MPCD method is shown to successfully reproduce the features of a nematic liquid crystal, includin...
Dynamic Skyline Computation with the Skyline Breaker Algorithm
Köppl, Dominik
2014-01-01
Given a sequential data input, we tackle parallel dynamic skyline computation of the read data by means of a spatial tree structure for indexing fine-grained feature vectors. For this purpose, we modified the Skyline Breaker algorithm that solves skyline computation with multiple local split decision trees concurrently. With this approach, we propose an algorithm for dynamic skyline computation that inherits the robustness against the dimension curse and different data distributions.
Using Genetic Algorithms for Navigation Planning in Dynamic Environments
Ferhat Uçan
2012-01-01
Full Text Available Navigation planning can be considered as a combination of searching and executing the most convenient flight path from an initial waypoint to a destination waypoint. Generally the aim is to follow the flight path, which provides minimum fuel consumption for the air vehicle. For dynamic environments, constraints change dynamically during flight. This is a special case of dynamic path planning. As the main concern of this paper is flight planning, the conditions and objectives that are most probable to be used in navigation problem are considered. In this paper, the genetic algorithm solution of the dynamic flight planning problem is explained. The evolutionary dynamic navigation planning algorithm is developed for compensating the existing deficiencies of the other approaches. The existing fully dynamic algorithms process unit changes to topology one modification at a time, but when there are several such operations occurring in the environment simultaneously, the algorithms are quite inefficient. The proposed algorithm may respond to the concurrent constraint updates in a shorter time for dynamic environment. The most secure navigation of the air vehicle is planned and executed so that the fuel consumption is minimum.
2008-01-01
Using functional derivative technique in quantum field theory, the algebraic dy-namics approach for solution of ordinary differential evolution equations was gen-eralized to treat partial differential evolution equations. The partial differential evo-lution equations were lifted to the corresponding functional partial differential equations in functional space by introducing the time translation operator. The functional partial differential evolution equations were solved by algebraic dynam-ics. The algebraic dynamics solutions are analytical in Taylor series in terms of both initial functions and time. Based on the exact analytical solutions, a new nu-merical algorithm—algebraic dynamics algorithm was proposed for partial differ-ential evolution equations. The difficulty of and the way out for the algorithm were discussed. The application of the approach to and computer numerical experi-ments on the nonlinear Burgers equation and meteorological advection equation indicate that the algebraic dynamics approach and algebraic dynamics algorithm are effective to the solution of nonlinear partial differential evolution equations both analytically and numerically.
Dynamic Uniform Scaling for Multiobjective Genetic Algorithms
Pedersen, Gerulf; Goldberg, D.E.
2004-01-01
Before Multiobjective Evolutionary Algorithms (MOEAs) can be used as a widespread tool for solving arbitrary real world problems there are some salient issues which require further investigation. One of these issues is how a uniform distribution of solutions along the Pareto non-dominated front c...
Optimized Bayesian dynamic advising theory and algorithms
Karny, Miroslav
2006-01-01
Written by one of the world''s leading groups in the area of Bayesian identification, control, and decision making, this book provides the theoretical and algorithmic basis of optimized probabilistic advising. It is accompanied by a CD that contains a specialized Matlab-based Mixtools toolbox, and examples illustrating the important areas.
Dynamic gate algorithm for multimode fiber Bragg grating sensor systems.
Ganziy, D; Jespersen, O; Woyessa, G; Rose, B; Bang, O
2015-06-20
We propose a novel dynamic gate algorithm (DGA) for precise and accurate peak detection. The algorithm uses a threshold-determined detection window and center of gravity algorithm with bias compensation. We analyze the wavelength fit resolution of the DGA for different values of the signal-to-noise ratio and different peak shapes. Our simulations and experiments demonstrate that the DGA method is fast and robust with better stability and accuracy than conventional algorithms. This makes it very attractive for future implementation in sensing systems, especially based on multimode fiber Bragg gratings. PMID:26193010
Synthesizing Dynamic Programming Algorithms from Linear Temporal Logic Formulae
Rosu, Grigore; Havelund, Klaus
2001-01-01
The problem of testing a linear temporal logic (LTL) formula on a finite execution trace of events, generated by an executing program, occurs naturally in runtime analysis of software. We present an algorithm which takes an LTL formula and generates an efficient dynamic programming algorithm. The generated algorithm tests whether the LTL formula is satisfied by a finite trace of events given as input. The generated algorithm runs in linear time, its constant depending on the size of the LTL formula. The memory needed is constant, also depending on the size of the formula.
A dynamic global and local combined particle swarm optimization algorithm
Particle swarm optimization (PSO) algorithm has been developing rapidly and many results have been reported. PSO algorithm has shown some important advantages by providing high speed of convergence in specific problems, but it has a tendency to get stuck in a near optimal solution and one may find it difficult to improve solution accuracy by fine tuning. This paper presents a dynamic global and local combined particle swarm optimization (DGLCPSO) algorithm to improve the performance of original PSO, in which all particles dynamically share the best information of the local particle, global particle and group particles. It is tested with a set of eight benchmark functions with different dimensions and compared with original PSO. Experimental results indicate that the DGLCPSO algorithm improves the search performance on the benchmark functions significantly, and shows the effectiveness of the algorithm to solve optimization problems.
Effective multicasting algorithm for dynamic membership with delay constraint
CHEN Lin; XU Zheng-quan
2006-01-01
This paper proposes an effective heuristic algorithm for dynamic multicast routing with delay-constrained DDMR.The tree constructed by DDMR has the following characteristics: (1) multicast tree changes with the dynamic memberships; (2)the cost of the tree is as small as possible at each node addition/removal event; (3) all of the path delay meet a fixed delay constraint;(4) minimal perturbation to an existing tree. The proposed algorithm is based on "damage" and "usefulness" concepts proposed in previous work, and has a new parameter bf(Balancing Factor) for judging whether or not to rearrange a tree region when membership changes. Mutation operation in Genetic Algorithm (GA) is also employed to find an attached node for a new adding node.Simulation showed that our algorithm performs well and is better than static heuristic algorithms, in term of cost especially.
Parallel Algorithm and Dynamic Exponent for Diffusion-limited Aggregation
Moriarty, K J M; Greenlaw, R
1997-01-01
A parallel algorithm for ``diffusion-limited aggregation'' (DLA) is described and analyzed from the perspective of computational complexity. The dynamic exponent z of the algorithm is defined with respect to the probabilistic parallel random-access machine (PRAM) model of parallel computation according to $T \\sim L^{z}$, where L is the cluster size, T is the running time, and the algorithm uses a number of processors polynomial in D_2 is the second generalized dimension. Simulations of DLA are carried out to measure D_2 and to test scaling assumptions employed in the complexity analysis of the parallel algorithm. It is plausible that the parallel algorithm attains the minimum possible value of the dynamic exponent in which case z characterizes the intrinsic history dependence of DLA.
Parallel algorithms and architecture for computation of manipulator forward dynamics
Fijany, Amir; Bejczy, Antal K.
1989-01-01
Parallel computation of manipulator forward dynamics is investigated. Considering three classes of algorithms for the solution of the problem, that is, the O(n), the O(n exp 2), and the O(n exp 3) algorithms, parallelism in the problem is analyzed. It is shown that the problem belongs to the class of NC and that the time and processors bounds are of O(log2/2n) and O(n exp 4), respectively. However, the fastest stable parallel algorithms achieve the computation time of O(n) and can be derived by parallelization of the O(n exp 3) serial algorithms. Parallel computation of the O(n exp 3) algorithms requires the development of parallel algorithms for a set of fundamentally different problems, that is, the Newton-Euler formulation, the computation of the inertia matrix, decomposition of the symmetric, positive definite matrix, and the solution of triangular systems. Parallel algorithms for this set of problems are developed which can be efficiently implemented on a unique architecture, a triangular array of n(n+2)/2 processors with a simple nearest-neighbor interconnection. This architecture is particularly suitable for VLSI and WSI implementations. The developed parallel algorithm, compared to the best serial O(n) algorithm, achieves an asymptotic speedup of more than two orders-of-magnitude in the computation the forward dynamics.
Multi-particle collision dynamics algorithm for nematic fluids.
Shendruk, Tyler N; Yeomans, Julia M
2015-07-01
Research on transport, self-assembly and defect dynamics within confined, flowing liquid crystals requires versatile and computationally efficient mesoscopic algorithms to account for fluctuating nematohydrodynamic interactions. We present a multi-particle collision dynamics (MPCD) based algorithm to simulate liquid-crystal hydrodynamic and director fields in two and three dimensions. The nematic-MPCD method is shown to successfully reproduce the features of a nematic liquid crystal, including a nematic-isotropic phase transition with hysteresis in 3D, defect dynamics, isotropic Frank elastic coefficients, tumbling and shear alignment regimes and boundary condition-dependent order parameter fields. PMID:26035731
A Hybrid Immigrants Scheme for Genetic Algorithms in Dynamic Environments
Shengxiang Yang; Renato Tinós
2007-01-01
Dynamic optimization problems are a kind of optimization problems that involve changes over time. They pose a serious challenge to traditional optimization methods as well as conventional genetic algorithms since the goal is no longer to search for the optimal solution(s) of a fixed problem but to track the moving optimum over time. Dynamic optimization problems have attracted a growing interest from the genetic algorithm community in recent years. Several approaches have been developed to enhance the performance of genetic algorithms in dynamic environments. One approach is to maintain the diversity of the population via random immigrants. This paper proposes a hybrid immigrants scheme that combines the concepts of elitism, dualism and random immigrants for genetic algorithms to address dynamic optimization problems. In this hybrid scheme, the best individual, i.e., the elite, from the previous generation and its dual individual are retrieved as the bases to create immigrants via traditional mutation scheme. These elitism-based and dualism-based immigrants together with some random immigrants are substituted into the current population, replacing the worst individuals in the population. These three kinds of immigrants aim to address environmental changes of slight, medium and significant degrees respectively and hence efficiently adapt genetic algorithms to dynamic environments that are subject to different severities of changes. Based on a series of systematically constructed dynamic test problems, experiments are carried out to investigate the performance of genetic algorithms with the hybrid immigrants scheme and traditional random immigrants scheme. Experimental results validate the efficiency of the proposed hybrid immigrants scheme for improving the performance of genetic algorithms in dynamic environments.
Quantum Dynamical Entropies and Gács Algorithmic Entropy
Fabio Benatti
2012-07-01
Full Text Available Several quantum dynamical entropies have been proposed that extend the classical Kolmogorov–Sinai (dynamical entropy. The same scenario appears in relation to the extension of algorithmic complexity theory to the quantum realm. A theorem of Brudno establishes that the complexity per unit time step along typical trajectories of a classical ergodic system equals the KS-entropy. In the following, we establish a similar relation between the Connes–Narnhofer–Thirring quantum dynamical entropy for the shift on quantum spin chains and the Gács algorithmic entropy. We further provide, for the same system, a weaker linkage between the latter algorithmic complexity and a different quantum dynamical entropy proposed by Alicki and Fannes.
Heuristic Scheduling Algorithm Oriented Dynamic Tasks for Imaging Satellites
Maocai Wang
2014-01-01
Full Text Available Imaging satellite scheduling is an NP-hard problem with many complex constraints. This paper researches the scheduling problem for dynamic tasks oriented to some emergency cases. After the dynamic properties of satellite scheduling were analyzed, the optimization model is proposed in this paper. Based on the model, two heuristic algorithms are proposed to solve the problem. The first heuristic algorithm arranges new tasks by inserting or deleting them, then inserting them repeatedly according to the priority from low to high, which is named IDI algorithm. The second one called ISDR adopts four steps: insert directly, insert by shifting, insert by deleting, and reinsert the tasks deleted. Moreover, two heuristic factors, congestion degree of a time window and the overlapping degree of a task, are employed to improve the algorithm’s performance. Finally, a case is given to test the algorithms. The results show that the IDI algorithm is better than ISDR from the running time point of view while ISDR algorithm with heuristic factors is more effective with regard to algorithm performance. Moreover, the results also show that our method has good performance for the larger size of the dynamic tasks in comparison with the other two methods.
WANG; Shunjin; ZHANG; Hua
2006-01-01
The problem of preserving fidelity in numerical computation of nonlinear ordinary differential equations is studied in terms of preserving local differential structure and approximating global integration structure of the dynamical system.The ordinary differential equations are lifted to the corresponding partial differential equations in the framework of algebraic dynamics,and a new algorithm-algebraic dynamics algorithm is proposed based on the exact analytical solutions of the ordinary differential equations by the algebraic dynamics method.In the new algorithm,the time evolution of the ordinary differential system is described locally by the time translation operator and globally by the time evolution operator.The exact analytical piece-like solution of the ordinary differential equations is expressd in terms of Taylor series with a local convergent radius,and its finite order truncation leads to the new numerical algorithm with a controllable precision better than Runge Kutta Algorithm and Symplectic Geometric Algorithm.
Functional clustering algorithm for the analysis of dynamic network data
Feldt, S.; Waddell, J; Hetrick, V. L.; Berke, J. D.; Żochowski, M
2009-01-01
We formulate a technique for the detection of functional clusters in discrete event data. The advantage of this algorithm is that no prior knowledge of the number of functional groups is needed, as our procedure progressively combines data traces and derives the optimal clustering cutoff in a simple and intuitive manner through the use of surrogate data sets. In order to demonstrate the power of this algorithm to detect changes in network dynamics and connectivity, we apply it to both simulat...
An Improved Adaptive Dynamic Particle Swarm Optimization Algorithm
Hongbo Zhao; Lina Feng
2014-01-01
In order to overcome the weakness that particle swarm optimization algorithm is likely to fall into local minimum when the complex optimization problems are solved, a new adaptive dynamic particle swarm optimization algorithm is proposed. The paper introduces the evaluation index of particle swarm premature convergence to judge the state of particle swarm in the population space, for the sake of investigates the timing of taking effect of influence function. The influence function is adaptive...
A Dynamic Elimination-Combining Stack Algorithm
Bar-Nissan, Gal; Suissa, Adi
2011-01-01
Two key synchronization paradigms for the construction of scalable concurrent data-structures are software combining and elimination. Elimination-based concurrent data-structures allow operations with reverse semantics (such as push and pop stack operations) to "collide" and exchange values without having to access a central location. Software combining, on the other hand, is effective when colliding operations have identical semantics: when a pair of threads performing operations with identical semantics collide, the task of performing the combined set of operations is delegated to one of the threads and the other thread waits for its operation(s) to be performed. Applying this mechanism iteratively can reduce memory contention and increase throughput. The most highly scalable prior concurrent stack algorithm is the elimination-backoff stack. The elimination-backoff stack provides high parallelism for symmetric workloads in which the numbers of push and pop operations are roughly equal, but its performance d...
An Algorithm of Sensor Management Based on Dynamic Target Detection
LIUXianxing; ZHOULin; JINYong
2005-01-01
The probability density of stationary target is only evolved at measurement update, but the probability density of dynamic target is evolved not only at measurement update but also during measurements, this paper researches an algorithm of dynamic targets detection. Firstly, it presents the evolution of probability density at measurement update by Bayes' rule and the evolution of probability density during measurements by Fokker-Planck differential equations, respectively. Secondly, the method of obtaining information entropy by the probability density is given and sensor resources are distributed based on the evolution of information entropy viz. the maximization of information gain. Simulation results show that compared with the algorithm of serial search, this algorithm is feasible and effective when it is used to detect dynamic target.
Crime Busting Model Based on Dynamic Ranking Algorithms
Yang Cao
2013-01-01
Full Text Available This paper proposed a crime busting model with two dynamic ranking algorithms to detect the likelihood of a suspect and the possibility of a leader in a complex social network. Signally, in order to obtain the priority list of suspects, an advanced network mining approach with a dynamic cumulative nominating algorithm is adopted to rapidly reduce computational expensiveness than most other topology-based approaches. Our method can also greatly increase the accuracy of solution with the enhancement of semantic learning filtering at the same time. Moreover, another dynamic algorithm of node contraction is also presented to help identify the leader among conspirators. Test results are given to verify the theoretical results, which show the great performance for either small or large datasets.
Left ventricular border recognition using a dynamic search algorithm
Initial results obtained with a simple, fully automated algorithm for detection of left ventricular boundaries are presented. The strength of this approach is the use of dynamic programming search techniques, which allow determination of local border points to be influenced by the entire global border location. The relative contributions of mask mode subtraction and the dynamic search technique are evaluated with respect to accurate border definition. These computer-determined ventricular borders are compared with hand-traced borders on subtracted and unsubtracted images. The modular dynamic search algorithm is shown to perform better than previously described algorithms, which generally require operator interaction. It is also shown that for both manual and automated techniques, ventricular borders derived from subtracted images may be significantly different from borders derived from nonsubtracted images
Novel algorithm for distributed replicas management based on dynamic programming
Wang Tao; Lu Xianliang; Hou Mengshu
2006-01-01
Replicas can improve the data reliability in distributed system. However, the traditional algorithms for replica management are based on the assumption that all replicas have the uniform reliability, which is inaccurate in some actual systems. To address such problem, a novel algorithm is proposed based on dynamic programming to manage the number and distribution of replicas in different nodes. By using Markov model, replicas management is organized as a multi-phase process, and the recursion equations are provided. In this algorithm, the heterogeneity of nodes, the expense for maintaining replicas and the engaged space have been considered. Under these restricted conditions, this algorithm realizes high data reliability in a distributed system. The results of case analysis prove the feasibility of the algorithm.
PACE: A dynamic programming algorithm for hardware/software partitioning
Knudsen, Peter Voigt; Madsen, Jan
1996-01-01
This paper presents the PACE partitioning algorithm which is used in the LYCOS co-synthesis system for partitioning control/dataflow graphs into hardware and software parts. The algorithm is a dynamic programming algorithm which solves both the problem of minimizing system execution time with a...... hardware area constraint and the problem of minimizing hardware area with a system execution time constraint. The target architecture consists of a single microprocessor and a single hardware chip (ASIC, FPGA, etc.) which are connected by a communication channel. The algorithm incorporates a realistic...... communication model and thus attempts to minimize communication overhead. The time-complexity of the algorithm is O(n2·𝒜) and the space-complexity is O(n·𝒜) where 𝒜 is the total area of the hardware chip and n the number of code fragments which may be placed in either hardware or software...
Applying Genetic Algorithm to Dynamic Layout Problem
K.V.Chandratre; K. N. Nandurkar
2011-01-01
In today’s economy, manufacturing plants must be able to operate efficiently and respond quickly to changes in the product mix and demand.[1] Layout design has a significant impact on manufacturing efficiency. Initially, it was treated as a static decision but due to improvements in technology, it is possible to rearrange the manufacturing facilities in different scenarios. The Plant layout affects on the total cost in the industry. Nowadays Dynamic layout is becoming an important issue. Dyna...
New heuristic algorithm for dynamic traffic in WDM optical networks
A. Rodríguez García
2015-12-01
Full Text Available This paper presents the results from the simulation of Snake One, a new heuristic algorithm, and the comparison made between three heuristic algorithms: Genetic Algorithms, Simulated Annealing, and Tabu Search, using blocking probability and network utilization as standard indicators. The simulation exercise was conducted on WDM NSFNET under dynamic traffic conditions. The results show a substantial decrease of blocking. However, this causes a relative network utilization growth. There are also load intervals which lead to performance improvement, decreasing the number of blocked requests.
A Dynamic Hashing Algorithm Suitable for Embedded System
Li Jianwei
2013-06-01
Full Text Available With the increasing of the data numbers, the linear hashing will be a lot of overflow blocks result from Data skew and the index size of extendible hash will surge so as to waste too much memory. This lead to the above two Typical Dynamic hashing algorithm don’t suitable for embedded system that need certain real-time requirements and memory resources are very scarce. To solve this problem, this paper was proposed a dynamic hashing algorithm suitable for embedded system combining with the characteristic of extendible hashing and linear hashing.it is no overflow buckets and the index size is proportional to the adjustment number.
Dynamics of Quantum Adiabatic Evolution Algorithm for Number Partitioning
Smelyanskiy, Vadius; vonToussaint, Udo V.; Timucin, Dogan A.; Clancy, Daniel (Technical Monitor)
2002-01-01
We have developed a general technique to study the dynamics of the quantum adiabatic evolution algorithm applied to random combinatorial optimization problems in the asymptotic limit of large problem size n. We use as an example the NP-complete Number Partitioning problem and map the algorithm dynamics to that of an auxiliary quantum spin glass system with the slowly varying Hamiltonian. We use a Green function method to obtain the adiabatic eigenstates and the minimum exitation gap, gmin = O(n2(sup -n/2)), corresponding to the exponential complexity of the algorithm for Number Partitioning. The key element of the analysis is the conditional energy distribution computed for the set of all spin configurations generated from a given (ancestor) configuration by simultaneous flipping of a fixed number of spins. For the problem in question this distribution is shown to depend on the ancestor spin configuration only via a certain parameter related to the energy of the configuration. As the result, the algorithm dynamics can be described in terms of one-dimensional quantum diffusion in the energy space. This effect provides a general limitation of a quantum adiabatic computation in random optimization problems. Analytical results are in agreement with the numerical simulation of the algorithm.
Node-Dependence-Based Dynamic Incentive Algorithm in Opportunistic Networks
Ruiyun Yu
2014-01-01
Full Text Available Opportunistic networks lack end-to-end paths between source nodes and destination nodes, so the communications are mainly carried out by the “store-carry-forward” strategy. Selfish behaviors of rejecting packet relay requests will severely worsen the network performance. Incentive is an efficient way to reduce selfish behaviors and hence improves the reliability and robustness of the networks. In this paper, we propose the node-dependence-based dynamic gaming incentive (NDI algorithm, which exploits the dynamic repeated gaming to motivate nodes relaying packets for other nodes. The NDI algorithm presents a mechanism of tolerating selfish behaviors of nodes. Reward and punishment methods are also designed based on the node dependence degree. Simulation results show that the NDI algorithm is effective in increasing the delivery ratio and decreasing average latency when there are a lot of selfish nodes in the opportunistic networks.
Decentralized Control of Dynamic Routing with a Neural Network Algorithm
无
2000-01-01
A state-dependent routing algorithm based on the neural network model, which takes advantage of other dynamic routing algorithm for circuit-switched network, is given in [1]. But, the Algorithm in [1] is a centralized control model with complex O (N7), therefore, is difficult to realize by hardware. A simplified algorithm is put forward in this paper, in which routing can be controlled decentralizedly, and its complexity is reduced to O (10N3). Computer simulations are made in a fully connected test network with eight nodes. The results show that the centralized control model has very effective performance that can match RTNR, and the centralized control model is not as good as the centralized one but better than DAR-1.
Parallel conjugate gradient algorithms for manipulator dynamic simulation
Fijany, Amir; Scheld, Robert E.
1989-01-01
Parallel conjugate gradient algorithms for the computation of multibody dynamics are developed for the specialized case of a robot manipulator. For an n-dimensional positive-definite linear system, the Classical Conjugate Gradient (CCG) algorithms are guaranteed to converge in n iterations, each with a computation cost of O(n); this leads to a total computational cost of O(n sq) on a serial processor. A conjugate gradient algorithms is presented that provide greater efficiency using a preconditioner, which reduces the number of iterations required, and by exploiting parallelism, which reduces the cost of each iteration. Two Preconditioned Conjugate Gradient (PCG) algorithms are proposed which respectively use a diagonal and a tridiagonal matrix, composed of the diagonal and tridiagonal elements of the mass matrix, as preconditioners. Parallel algorithms are developed to compute the preconditioners and their inversions in O(log sub 2 n) steps using n processors. A parallel algorithm is also presented which, on the same architecture, achieves the computational time of O(log sub 2 n) for each iteration. Simulation results for a seven degree-of-freedom manipulator are presented. Variants of the proposed algorithms are also developed which can be efficiently implemented on the Robot Mathematics Processor (RMP).
von Maltzahn, Robyn; Durrheim, Kevin
2008-01-01
This paper contests the major emphasis placed on the multidimensional nature of poverty measurement. Instead, it argues that poverty pictures created by different measures and at different units of analysis tend to converge. This argument is derived from a comparison of poverty pictures created using income and asset-based measures at the national…
Dynamic evolutionary community detection algorithms based on the modularity matrix
Motivated by the relationship of the dynamic behaviors and network structure, in this paper, we present two efficient dynamic community detection algorithms. The phases of the nodes in the network can evolve according to our proposed differential equations. In each iteration, the phases of the nodes are controlled by several parameters. It is found that the phases of the nodes are ultimately clustered into several communities after a short period of evolution. They can be adopted to detect the communities successfully. The second differential equation can dynamically adjust several parameters, so it can obtain satisfactory detection results. Simulations on some test networks have verified the efficiency of the presented algorithms. (interdisciplinary physics and related areas of science and technology)
A Dynamic Scheduling Algorithm for Divisible Loads in Grid Environments
Said Elnaffar
2007-06-01
Full Text Available
Divisible loads are those workloads that can be partitioned by a scheduler into any arbitrary chunks. The problem of scheduling divisible loads has been defined for a long time, however, a handful of solutions have been proposed. Furthermore, almost all proposed approaches attempt to perform scheduling in dedicated environments such as LANs, whereas scheduling in non-dedicated environments such as Grids remains an open problem. In Grids, the incessant variation of a worker's computing power is a chief difficulty of splitting and distributing workloads to Grid workers efficiently. In this paper, we first introduce a computation model that explains the impact of local (internal tasks and Grid (external tasks that arrive at a given worker. This model helps estimate the available computing power of a worker under the fluctuation of the number of local and Grid applications. Based on this model, we propose the CPU power prediction strategy. Additionally, we build a new dynamic scheduling algorithm by incorporating the prediction strategy into a static scheduling algorithm. Lastly we demonstrate that the proposed dynamic algorithm is superior to the existing dynamic and static algorithms by a comprehensive set of simulations.
An algorithm for the solution of dynamic linear programs
Psiaki, Mark L.
1989-01-01
The algorithm's objective is to efficiently solve Dynamic Linear Programs (DLP) by taking advantage of their special staircase structure. This algorithm constitutes a stepping stone to an improved algorithm for solving Dynamic Quadratic Programs, which, in turn, would make the nonlinear programming method of Successive Quadratic Programs more practical for solving trajectory optimization problems. The ultimate goal is to being trajectory optimization solution speeds into the realm of real-time control. The algorithm exploits the staircase nature of the large constraint matrix of the equality-constrained DLPs encountered when solving inequality-constrained DLPs by an active set approach. A numerically-stable, staircase QL factorization of the staircase constraint matrix is carried out starting from its last rows and columns. The resulting recursion is like the time-varying Riccati equation from multi-stage LQR theory. The resulting factorization increases the efficiency of all of the typical LP solution operations over that of a dense matrix LP code. At the same time numerical stability is ensured. The algorithm also takes advantage of dynamic programming ideas about the cost-to-go by relaxing active pseudo constraints in a backwards sweeping process. This further decreases the cost per update of the LP rank-1 updating procedure, although it may result in more changes of the active set that if pseudo constraints were relaxed in a non-stagewise fashion. The usual stability of closed-loop Linear/Quadratic optimally-controlled systems, if it carries over to strictly linear cost functions, implies that the saving due to reduced factor update effort may outweigh the cost of an increased number of updates. An aerospace example is presented in which a ground-to-ground rocket's distance is maximized. This example demonstrates the applicability of this class of algorithms to aerospace guidance. It also sheds light on the efficacy of the proposed pseudo constraint relaxation
A dynamic inertia weight particle swarm optimization algorithm
Particle swarm optimization (PSO) algorithm has been developing rapidly and has been applied widely since it was introduced, as it is easily understood and realized. This paper presents an improved particle swarm optimization algorithm (IPSO) to improve the performance of standard PSO, which uses the dynamic inertia weight that decreases according to iterative generation increasing. It is tested with a set of 6 benchmark functions with 30, 50 and 150 different dimensions and compared with standard PSO. Experimental results indicate that the IPSO improves the search performance on the benchmark functions significantly
A Dynamic Approach to Weighted Suffix Tree Construction Algorithm
Binay Kumar Pandey
2011-02-01
Full Text Available In present time weighted suffix tree is consider as a one of the most important existing data structure used for analyzing molecular weighted sequence. Although a static partitioning based parallel algorithm existed for the construction of weighted suffix tree, but for very long weighted DNA sequences it takes significant amount of time. However, in our implementation of dynamic partition based parallel weighted suffix tree construction algorithm on cluster computing makes it possible to significantly accelerate the construction of weighted suffix tree.
Dynamic bandwidth allocation algorithm for full-band utilization
Han Guodong; Wang Hui; Wu Jiangxing
2006-01-01
To improve and optimize the bandwidth utilization for multi-service packet transporting system, a kind of Dynamic Full Bandwidth Utilized (DFBU) allocation algorithm allowing a single link to use far beyond its fair share bandwidth is presented. Three important parameters as the bound on max and minimum bandwidth, the maximum packet delay and the minimum bandwidth utilization are discussed and analyzed. Results of experiments show that the DFBU-algorithm is capable of making a single link in the system use all the spare bandwidth (up to full-bandwidth) while the performance of fairness and QoS requirement is still guaranteed.
Group implicit concurrent algorithms in nonlinear structural dynamics
Ortiz, M.; Sotelino, E. D.
1989-01-01
During the 70's and 80's, considerable effort was devoted to developing efficient and reliable time stepping procedures for transient structural analysis. Mathematically, the equations governing this type of problems are generally stiff, i.e., they exhibit a wide spectrum in the linear range. The algorithms best suited to this type of applications are those which accurately integrate the low frequency content of the response without necessitating the resolution of the high frequency modes. This means that the algorithms must be unconditionally stable, which in turn rules out explicit integration. The most exciting possibility in the algorithms development area in recent years has been the advent of parallel computers with multiprocessing capabilities. So, this work is mainly concerned with the development of parallel algorithms in the area of structural dynamics. A primary objective is to devise unconditionally stable and accurate time stepping procedures which lend themselves to an efficient implementation in concurrent machines. Some features of the new computer architecture are summarized. A brief survey of current efforts in the area is presented. A new class of concurrent procedures, or Group Implicit algorithms is introduced and analyzed. The numerical simulation shows that GI algorithms hold considerable promise for application in coarse grain as well as medium grain parallel computers.
Parameter optimization in molecular dynamics simulations using a genetic algorithm
In this work, we introduce a genetic algorithm for the parameterization of the reactive force field developed by Kieffer . This potential includes directional covalent bonds and dispersion terms. Important features of this force field for simulating systems that undergo significant structural reorganization are (i) the ability to account for the redistribution of electron density upon ionization, formation, or breaking of bonds, through a charge transfer term, and (ii) the fact that the angular constraints dynamically adjust when a change in the coordination number of an atom occurs. In this paper, we present the implementation of the genetic algorithm into the existing code as well as the algorithm efficiency and preliminary results on Si-Si force field optimization. The parameters obtained by this method will be compared to existing parameter sets obtained by a trial-and-error process.
Dynamic Routing and Resource Assignment Algorithm In sloted optical Networks
Bisheng Quan
2013-04-01
Full Text Available All-optical wavelength division multiplexing networks are the most promising candidate for the next generation wideband backbone networks. To improve the utilization of wavelength, time division multiplexing technology is introduced. The routing, wavelength and time-slots assignment problem was studied in such time-space switched networks. Two new dynamic algorithms were proposed which distribute slots of the session request on multiple different wavelengths of single fiber separately based on fixed alternate routing and adaptive routing policy. Especially in LLR-MWLB algorithm, network link weights adjust adaptively with the available time slots of each link and load balancing strategy is adopted. The effectiveness of the proposed algorithms is demonstrated by simulations and results show the better performance.
Iterative Dynamic Diversity Evolutionary Algorithm for Constrained Optimization
GAO Wei-Shang; SHAO Cheng
2014-01-01
Evolutionary algorithms (EAs) were shown to be effective for complex constrained optimization problems. However, inflexible exploration in general EAs would lead to losing the global optimum nearby the ill-convergence regions. In this paper, we propose an iterative dynamic diversity evolutionary algorithm (IDDEA) with contractive subregions guiding exploitation through local extrema to the global optimum in suitable steps. In IDDEA, a novel optimum estimation strategy with multi-agents evolving diversely is suggested to eﬃciently compute dominance trend and establish a subregion. In addition, a subregion converging iteration is designed to redistrict a smaller subregion in current subregion for next iteration, which is based on a special dominance estimation scheme. Meanwhile, an infimum penalty function is embedded into IDDEA to judge agents and penalize adaptively the unfeasible agents with the lowest fitness of feasible agents. Furthermore, several engineering design optimization problems taken from the specialized literature are successfully solved by the present algorithm with high reliable solutions.
Multiscale Reaction-Diffusion Algorithms: PDE-Assisted Brownian Dynamics
Franz, Benjamin
2013-06-19
Two algorithms that combine Brownian dynami cs (BD) simulations with mean-field partial differential equations (PDEs) are presented. This PDE-assisted Brownian dynamics (PBD) methodology provides exact particle tracking data in parts of the domain, whilst making use of a mean-field reaction-diffusion PDE description elsewhere. The first PBD algorithm couples BD simulations with PDEs by randomly creating new particles close to the interface, which partitions the domain, and by reincorporating particles into the continuum PDE-description when they cross the interface. The second PBD algorithm introduces an overlap region, where both descriptions exist in parallel. It is shown that the overlap region is required to accurately compute variances using PBD simulations. Advantages of both PBD approaches are discussed and illustrative numerical examples are presented. © 2013 Society for Industrial and Applied Mathematics.
Dynamical linked cluster expansions: Algorithmic aspects and applications
Dynamical linked cluster expansions are linked cluster expansions with hopping parameter terms endowed with their own dynamics. They amount to a generalization of series expansions from 2-point to point-link-point interactions. We outline an associated multiple-line graph theory involving extended notions of connectivity and indicate an algorithmic implementation of graphs. Fields of applications are SU(N) gauge Higgs systems within variational estimates, spin glasses and partially annealed neural networks. We present results for the critical line in an SU(2) gauge Higgs model for the electroweak phase transition. The results agree well with corresponding high precision Monte Carlo results
A Bayesian Algorithm for Functional Mapping of Dynamic Complex Traits
Rongling Wu; Tian Liu
2009-01-01
Functional mapping of dynamic traits measured in a longitudinal study was originally derived within the maximum likelihood (ML) context and implemented with the EM algorithm. Although ML-based functional mapping possesses many favorable statistical properties in parameter estimation, it may be computationally intractable for analyzing longitudinal data with high dimensions and high measurement errors. In this article, we derive a general functional mapping framework for quantitative trait loc...
Computational Fluid Dynamics. [numerical methods and algorithm development
1992-01-01
This collection of papers was presented at the Computational Fluid Dynamics (CFD) Conference held at Ames Research Center in California on March 12 through 14, 1991. It is an overview of CFD activities at NASA Lewis Research Center. The main thrust of computational work at Lewis is aimed at propulsion systems. Specific issues related to propulsion CFD and associated modeling will also be presented. Examples of results obtained with the most recent algorithm development will also be presented.
A SYMPLECTIC ALGORITHM FOR DYNAMICS OF RIGID BODY
LU Ying-jie; REN Ge-xue
2006-01-01
For the dynamics of a rigid body with a fixed point based on the quaternion and the corresponding generalized momenta, a displacement-based symplectic integration scheme for differential-algebraic equations is proposed and applied to the Lagrange's equations based on dependent generalized momenta. Numerical experiments show that the algorithm possesses such characters as high precision and preserving system invariants.More importantly, the generalized momenta based Lagrange's equations show unique advantages over the traditional Lagrange's equations in symplectic integrations.
The δf algorithm for beam dynamics
An algorithm is developed to study particle dynamics of beams including collective interaction with high accuracy and low noise. Particle dynamics with collective interactions is treated through particle simulation, where the main or average distribution f0 and the deviation away from it δf are separately followed. The main distribution f0 is handled by an analytic equilibrium solution and the perturbation away from it δf is followed by the method of characteristics. We call this the δf algorithm. We specifically model a synchrotron collider which includes the collision section where collective effects of collisions are simulated by this δf algorithm and the rest of the collider where single particle dynamics are treated by simple harmonic transport. The most important target of this simulation is to understand and predict the long-time behavior of the beam luminosity and lifetime. The δf method allows the study the effect of small perturbations over long timescales on beam lifetime by eliminating the numerical noise problem inherent in Particle-in-Cell techniques. In the δf code using the reference parameters of the SSC (Superconducting Super Collider), beam blow-up near resonances and oscillations in the tune shift, Δν, far from resonances are observed. In studying long timescale particle diffusion in the phase space of the beams away from resonances, the δf code performance is compared with a tracking code which does not incorporate collective interaction
Dynamic hybrid algorithms for MAP inference in discrete MRFs.
Alahari, Karteek; Kohli, Pushmeet; Torr, Philip H S
2010-10-01
In this paper, we present novel techniques that improve the computational and memory efficiency of algorithms for solving multilabel energy functions arising from discrete mrfs or crfs. These methods are motivated by the observations that the performance of minimization algorithms depends on: 1) the initialization used for the primal and dual variables and 2) the number of primal variables involved in the energy function. Our first method (dynamic alpha-expansion) works by "recycling" results from previous problem instances. The second method simplifies the energy function by "reducing" the number of unknown variables present in the problem. Further, we show that it can also be used to generate a good initialization for the dynamic alpha-expansion algorithm by "reusing" dual variables. We test the performance of our methods on energy functions encountered in the problems of stereo matching and color and object-based segmentation. Experimental results show that our methods achieve a substantial improvement in the performance of alpha-expansion, as well as other popular algorithms such as sequential tree-reweighted message passing and max-product belief propagation. We also demonstrate the applicability of our schemes for certain higher order energy functions, such as the one described in [1], for interactive texture-based image and video segmentation. In most cases, we achieve a 10-15 times speed-up in the computation time. Our modified alpha-expansion algorithm provides similar performance to Fast-PD, but is conceptually much simpler. Both alpha-expansion and Fast-PD can be made orders of magnitude faster when used in conjunction with the "reduce" scheme proposed in this paper. PMID:20724761
A Dynamic Job Scheduling Algorithm for Parallel System
张建; 陆鑫达; 加力
2003-01-01
One of the fundamental problems in parallel and distributed systems is deciding how to allocate jobs toprocessors. The goals of job scheduling in a parallel environment are to minimize the parallel execution time of ajob and try to balance the user's desire with the system's desire. The users always want their jobs be completed asquickly as possible, while the system wants to service as many jobs as possible. In this paper, a dynamic job-scheduling algorithm was introduced. This algorithm tries to utilize the information of a practical system to allo-cate the jobs more evenly. The communication time between the processor and scheduler is overlapped with thecomputation time of the processor. So the communication overhead can be little. The principle of scheduling thejob is based on the desirability of each processor. The scheduler would not allocate a new job to a processor that isalready fully utilized. The execution efficiency of the system will be increased. This algorithm also can be reused inother complex algorithms.
Multiagent Flight Control in Dynamic Environments with Cooperative Coevolutionary Algorithms
Knudson, Matthew D.; Colby, Mitchell; Tumer, Kagan
2014-01-01
Dynamic flight environments in which objectives and environmental features change with respect to time pose a difficult problem with regards to planning optimal flight paths. Path planning methods are typically computationally expensive, and are often difficult to implement in real time if system objectives are changed. This computational problem is compounded when multiple agents are present in the system, as the state and action space grows exponentially. In this work, we use cooperative coevolutionary algorithms in order to develop policies which control agent motion in a dynamic multiagent unmanned aerial system environment such that goals and perceptions change, while ensuring safety constraints are not violated. Rather than replanning new paths when the environment changes, we develop a policy which can map the new environmental features to a trajectory for the agent while ensuring safe and reliable operation, while providing 92% of the theoretically optimal performance
Algorithms for computational fluid dynamics n parallel processors
A study of parallel algorithms for the numerical solution of partial differential equations arising in computational fluid dynamics is presented. The actual implementation on parallel processors of shared and nonshared memory design is discussed. The performance of these algorithms is analyzed in terms of machine efficiency, communication time, bottlenecks and software development costs. For elliptic equations, a parallel preconditioned conjugate gradient method is described, which has been used to solve pressure equations discretized with high order finite elements on irregular grids. A parallel full multigrid method and a parallel fast Poisson solver are also presented. Hyperbolic conservation laws were discretized with parallel versions of finite difference methods like the Lax-Wendroff scheme and with the Random Choice method. Techniques are developed for comparing the behavior of an algorithm on different architectures as a function of problem size and local computational effort. Effective use of these advanced architecture machines requires the use of machine dependent programming. It is shown that the portability problems can be minimized by introducing high level operations on vectors and matrices structured into program libraries
Dynamic behavior of shortest path routing algorithms for communication networks
Bertsekas, D. P.
1980-06-01
Several proposed routing algorithms for store and forward communication networks, including one currently in operation in the ARPANET, route messages along shortest paths computed by using some set of link lengths. When these lengths depend on current traffic conditions as they must in an adaptive algorithm, dynamic behavior questions such as stability convergence, and speed of convergence are of interest. This paper is the first attempt to analyze systematically these issues. It is shown that minimum queuing delay path algorithms tend to exhibit violent oscillatory behavior in the absence of a damping mechanism. The oscillations can be damped by means of several types of schemes, two of which are analyzed in this paper. In the first scheme a constant bias is added to the queuing delay thereby providing a preference towards paths with a small number of links. In the second scheme the effects of several past routings are averaged as, for example, when the link lengths are computed and communicated asynchronously throughout the network.
Dynamic airspace configuration algorithms for next generation air transportation system
Wei, Jian
The National Airspace System (NAS) is under great pressure to safely and efficiently handle the record-high air traffic volume nowadays, and will face even greater challenge to keep pace with the steady increase of future air travel demand, since the air travel demand is projected to increase to two to three times the current level by 2025. The inefficiency of traffic flow management initiatives causes severe airspace congestion and frequent flight delays, which cost billions of economic losses every year. To address the increasingly severe airspace congestion and delays, the Next Generation Air Transportation System (NextGen) is proposed to transform the current static and rigid radar based system to a dynamic and flexible satellite based system. New operational concepts such as Dynamic Airspace Configuration (DAC) have been under development to allow more flexibility required to mitigate the demand-capacity imbalances in order to increase the throughput of the entire NAS. In this dissertation, we address the DAC problem in the en route and terminal airspace under the framework of NextGen. We develop a series of algorithms to facilitate the implementation of innovative concepts relevant with DAC in both the en route and terminal airspace. We also develop a performance evaluation framework for comprehensive benefit analyses on different aspects of future sector design algorithms. First, we complete a graph based sectorization algorithm for DAC in the en route airspace, which models the underlying air route network with a weighted graph, converts the sectorization problem into the graph partition problem, partitions the weighted graph with an iterative spectral bipartition method, and constructs the sectors from the partitioned graph. The algorithm uses a graph model to accurately capture the complex traffic patterns of the real flights, and generates sectors with high efficiency while evenly distributing the workload among the generated sectors. We further improve
Dynamics of the evolution of learning algorithms by selection
We study the evolution of artificial learning systems by means of selection. Genetic programming is used to generate populations of programs that implement algorithms used by neural network classifiers to learn a rule in a supervised learning scenario. In contrast to concentrating on final results, which would be the natural aim while designing good learning algorithms, we study the evolution process. Phenotypic and genotypic entropies, which describe the distribution of fitness and of symbols, respectively, are used to monitor the dynamics. We identify significant functional structures responsible for the improvements in the learning process. In particular, some combinations of variables and operators are useful in assessing performance in rule extraction and can thus implement annealing of the learning schedule. We also find combinations that can signal surprise, measured on a single example, by the difference between predicted and correct classification. When such favorable structures appear, they are disseminated on very short time scales throughout the population. Due to such abruptness they can be thought of as dynamical transitions. But foremost, we find a strict temporal order of such discoveries. Structures that measure performance are never useful before those for measuring surprise. Invasions of the population by such structures in the reverse order were never observed. Asymptotically, the generalization ability approaches Bayesian results
Integrated dynamic shared protection algorithm for GMPLS networks
无
2008-01-01
The path protection approach is widely investigated as a survivability solution for GMPLS networks, which has the advantage of efficient capacity utilization. However, there is a problem of the path protection approach that searching a disjoint backup path for a primary path is often unsuccessful. In order to resolve this problem, an integrated dynamic shared protection (IDSP) algorithm is proposed. The main idea of the proposed algorithm is that the path protection approach is first used to establish a backup path for the primary path; if the establishment is unsuccessful, then the primary path is dynamically divided into segments whose hop count are not fixed but not more than the limitation calculated by the equations introduced. In this proposal, backup bandwidth sharing is allowed to improve the capacity utilization ratio, which makes the link cost function quite different from previous ones. Simulation experiments are presented to demonstrate the efficiency of the proposed method compared with previous methods. Numerical results show that IDSP can not only achieve low protection failure probability but can also gain a better tradeoff between the protection overbuild and the average recovery time.
Bidirectional Dynamic Diversity Evolutionary Algorithm for Constrained Optimization
Weishang Gao
2013-01-01
Full Text Available Evolutionary algorithms (EAs were shown to be effective for complex constrained optimization problems. However, inflexible exploration-exploitation and improper penalty in EAs with penalty function would lead to losing the global optimum nearby or on the constrained boundary. To determine an appropriate penalty coefficient is also difficult in most studies. In this paper, we propose a bidirectional dynamic diversity evolutionary algorithm (Bi-DDEA with multiagents guiding exploration-exploitation through local extrema to the global optimum in suitable steps. In Bi-DDEA potential advantage is detected by three kinds of agents. The scale and the density of agents will change dynamically according to the emerging of potential optimal area, which play an important role of flexible exploration-exploitation. Meanwhile, a novel double optimum estimation strategy with objective fitness and penalty fitness is suggested to compute, respectively, the dominance trend of agents in feasible region and forbidden region. This bidirectional evolving with multiagents can not only effectively avoid the problem of determining penalty coefficient but also quickly converge to the global optimum nearby or on the constrained boundary. By examining the rapidity and veracity of Bi-DDEA across benchmark functions, the proposed method is shown to be effective.
Huang, Xiaobiao, E-mail: xiahuang@slac.stanford.edu; Safranek, James
2014-09-01
Nonlinear dynamics optimization is carried out for a low emittance upgrade lattice of SPEAR3 in order to improve its dynamic aperture and Touschek lifetime. Two multi-objective optimization algorithms, a genetic algorithm and a particle swarm algorithm, are used for this study. The performance of the two algorithms are compared. The result shows that the particle swarm algorithm converges significantly faster to similar or better solutions than the genetic algorithm and it does not require seeding of good solutions in the initial population. These advantages of the particle swarm algorithm may make it more suitable for many accelerator optimization applications.
Nonlinear dynamics optimization is carried out for a low emittance upgrade lattice of SPEAR3 in order to improve its dynamic aperture and Touschek lifetime. Two multi-objective optimization algorithms, a genetic algorithm and a particle swarm algorithm, are used for this study. The performance of the two algorithms are compared. The result shows that the particle swarm algorithm converges significantly faster to similar or better solutions than the genetic algorithm and it does not require seeding of good solutions in the initial population. These advantages of the particle swarm algorithm may make it more suitable for many accelerator optimization applications
Dynamic characterization of oil fields, complex stratigraphically using genetic algorithms
A novel methodology is presented in this paper for the characterization of highly heterogeneous oil fields by integration of the oil fields dynamic information to the static updated model. The objective of the oil field's characterization process is to build an oil field model, as realistic as possible, through the incorporation of all the available information. The classical approach consists in producing a model based in the oil field's static information, having as the process final stage the validation model with the dynamic information available. It is important to clarify that the term validation implies a punctual process by nature, generally intended to secure the required coherence between productive zones and petrophysical properties. The objective of the proposed methodology is to enhance the prediction capacity of the oil field's model by previously integrating, parameters inherent to the oil field's fluid dynamics by a process of dynamic data inversion through an optimization procedure based on evolutionary computation. The proposed methodology relies on the construction of the oil field's high-resolution static model, escalated by means of hybrid techniques while aiming to preserve the oil field's heterogeneity. Afterwards, using an analytic simulator as reference, the scaled model is methodically modified by means of an optimization process that uses genetic algorithms and production data as conditional information. The process's final product is a model that observes the static and dynamic conditions of the oil field with the capacity to minimize the economic impact that generates production historical adjustments to the simulation tasks. This final model features some petrophysical properties (porosity, permeability and water saturation), as modified to achieve a better adjustment of the simulated production's history versus the real one history matching. Additionally, the process involves a slight modification of relative permeability, which has
A local algorithm for detecting community structures in dynamic networks
Massaro, Emanuele; Guazzini, Andrea; Passarella, Andrea; Bagnoli, Franco
2013-01-01
The emergence and the global adaptation of mobile devices has influenced human interactions at the individual, community, and social levels leading to the so called Cyber-Physical World (CPW) convergence scenario [1]. One of the most important features of CPW is the possibility of exploiting information about the structure of social communities of users, that manifest through joint movement patterns and frequency of physical co-location: mobile devices of users that belong to the same social community are likely to "see" each other (and thus be able to communicate through ad hoc networking techniques) more frequently and regularly than devices outside of the community. In mobile opportunistic networks, this fact can be exploited, for example, to optimize networking operations such as forwarding and dissemination of messages. In this paper we present a novel local cognitive-inspired algorithm for revealing the structure of these dynamic social networks by exploiting information about physical encounters, logge...
RETRACTED ARTICLE: Dynamic voltage restorer controller using grade algorithm
S. Deepa
2015-12-01
Full Text Available This paper deals with the terminology and various issues about power quality problems. This problem occurs owing to voltage sag, swell, harmonics, and surges. The sustained overvoltage and undervoltage originated from power system may often damage/or disrupt computerized process. Voltage sags and harmonics disturb the power quality and this can be overcome by custom power device called dynamic voltage restorer (DVR. The DVR is normally installed between the source voltage and critical or sensitive load. The vital role of DVR depends on the efficiency of the control technique involved in switching circuit of the inverter. In this paper, Combination of improved grade algorithm with fuzzy membership function is used to decide the Proportional-Integral coefficients. The DVR works well both in balanced and unbalanced conditions of voltages. The simulation results show the efficiency of the proposed method.
On the Feasibility of Maintenance Algorithms in Dynamic Graphs
Casteigts, Arnaud; Mathieson, Luke
2011-01-01
Near ubiquitous mobile computing has led to intense interest in dynamic graph theory. This provides a new and challenging setting for algorithmics and complexity theory. For any graph-based problem, the rapid evolution of a (possibly disconnected) graph over time naturally leads to the important complexity question: is it better to calculate a new solution from scratch or to adapt the known solution on the prior graph to quickly provide a solution of guaranteed quality for the changed graph? In this paper, we demonstrate that the former is the best approach in some cases, but that there are cases where the latter is feasible. We prove that, under certain conditions, hard problems cannot even be approximated in any reasonable complexity bound --- i.e., even with a large amount of time, having a solution to a very similar graph does not help in computing a solution to the current graph. To achieve this, we formalize the idea as a maintenance algorithm. Using Dominating Set as the primary example we show that W[...
A Dynamic Multistage Hybrid Swarm Intelligence Optimization Algorithm for Function Optimization
Daqing Wu; Jianguo Zheng
2012-01-01
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 c...
A Multi-Swarm Cellular PSO based on Clonal Selection Algorithm in Dynamic Environments
Nabizadeh, Somayeh; Rezvanian, Alireza; Meybodi, Mohammd Reza
2013-01-01
Many real-world problems are dynamic optimization problems. In this case, the optima in the environment change dynamically. Therefore, traditional optimization algorithms disable to track and find optima. In this paper, a new multi-swarm cellular particle swarm optimization based on clonal selection algorithm (CPSOC) is proposed for dynamic environments. In the proposed algorithm, the search space is partitioned into cells by a cellular automaton. Clustered particles in each cell, which make ...
A Multiagent Dynamic Assessment Approach for Water Quality Based on Improved Q-Learning Algorithm
Jianjun Ni; Li Ren; Minghua Liu; Daqi Zhu
2013-01-01
The dynamic water quality assessment is a challenging and critical issue in water resource management systems. To deal with this complex problem, a dynamic water assessment model based on multiagent technology is proposed, and an improved Q-learning algorithm is used in this paper. In the proposed Q-learning algorithm, a fuzzy membership function and a punishment mechanism are introduced to improve the learning speed of Q-learning algorithm. The dynamic water quality assessment for different ...
AN ADVANCED DYNAMIC FEEDBACK AND RANDOM DISPATCHING LOAD-BALANCING ALGORITHM FOR GMLC IN 3G
Liao Jianxin; Zhang Hao; Zhu Xiaomin
2006-01-01
Based on the system architecture and software structure of GMLC (Gateway Mobile Location Center) in 3G (third generation), a new dynamic load-balancing algorithm is proposed. It bases on dynamic feedback and imports the increment for admitting new request into the load forecast. It dynamically adjusts the dispatching probability according to the remainder process capability of each node. Experiments on the performance of algorithm have been carried out in GMLC and the algorithm is compared with Pick-KX algorithm and DFB (Dynamic FeedBack) algorithm in average throughput and average response time. Experiments results show that the average throughput of the proposed algorithm is about five percents higher than that of the other two algorithms and the average response time is four percents higher under high system loading condition.
A Dynamic Web Service Composition Algorithm Based on TOPSIS
Longchang Zhang
2011-08-01
Full Text Available Multi-period QoS evaluations have to be considered for obtaining a reliable decision in the service selection process. Besides, open and dynamic Internet environment increased the uncertainty of decision-making. To solve the above difficulties, this paper presents a novel hybrid data type (including real numbers, interval numbers, triangular fuzzy numbers and intuitionistic fuzzy numbers QoS model, multi-period hybrid QoS aggregating operator and a strategy for aggregating composition service QoS firstly. Furthermore, a dynamic Web service composition algorithm based on TOPSIS (DWSCA_TOPSIS is presented to evaluate multi-period hybrid QoS data. DWSCA_TOPSIS includes four main steps: converting hybrid QoS into intervals, calculating weighted normalized decision-matrix, determining the positive-ideal and negative-ideal solution, calculating the close-degrees of candidates. Finally, some experiments are given using actual QoS data to demonstrate the benefits and effectiveness of our approach.
Ab initio multiple cloning algorithm for quantum nonadiabatic molecular dynamics
We present a new algorithm for ab initio quantum nonadiabatic molecular dynamics that combines the best features of ab initio Multiple Spawning (AIMS) and Multiconfigurational Ehrenfest (MCE) methods. In this new method, ab initio multiple cloning (AIMC), the individual trajectory basis functions (TBFs) follow Ehrenfest equations of motion (as in MCE). However, the basis set is expanded (as in AIMS) when these TBFs become sufficiently mixed, preventing prolonged evolution on an averaged potential energy surface. We refer to the expansion of the basis set as “cloning,” in analogy to the “spawning” procedure in AIMS. This synthesis of AIMS and MCE allows us to leverage the benefits of mean-field evolution during periods of strong nonadiabatic coupling while simultaneously avoiding mean-field artifacts in Ehrenfest dynamics. We explore the use of time-displaced basis sets, “trains,” as a means of expanding the basis set for little cost. We also introduce a new bra-ket averaged Taylor expansion (BAT) to approximate the necessary potential energy and nonadiabatic coupling matrix elements. The BAT approximation avoids the necessity of computing electronic structure information at intermediate points between TBFs, as is usually done in saddle-point approximations used in AIMS. The efficiency of AIMC is demonstrated on the nonradiative decay of the first excited state of ethylene. The AIMC method has been implemented within the AIMS-MOLPRO package, which was extended to include Ehrenfest basis functions
Ab initio multiple cloning algorithm for quantum nonadiabatic molecular dynamics
Makhov, Dmitry V.; Glover, William J.; Martinez, Todd J.; Shalashilin, Dmitrii V.
2014-08-01
We present a new algorithm for ab initio quantum nonadiabatic molecular dynamics that combines the best features of ab initio Multiple Spawning (AIMS) and Multiconfigurational Ehrenfest (MCE) methods. In this new method, ab initio multiple cloning (AIMC), the individual trajectory basis functions (TBFs) follow Ehrenfest equations of motion (as in MCE). However, the basis set is expanded (as in AIMS) when these TBFs become sufficiently mixed, preventing prolonged evolution on an averaged potential energy surface. We refer to the expansion of the basis set as "cloning," in analogy to the "spawning" procedure in AIMS. This synthesis of AIMS and MCE allows us to leverage the benefits of mean-field evolution during periods of strong nonadiabatic coupling while simultaneously avoiding mean-field artifacts in Ehrenfest dynamics. We explore the use of time-displaced basis sets, "trains," as a means of expanding the basis set for little cost. We also introduce a new bra-ket averaged Taylor expansion (BAT) to approximate the necessary potential energy and nonadiabatic coupling matrix elements. The BAT approximation avoids the necessity of computing electronic structure information at intermediate points between TBFs, as is usually done in saddle-point approximations used in AIMS. The efficiency of AIMC is demonstrated on the nonradiative decay of the first excited state of ethylene. The AIMC method has been implemented within the AIMS-MOLPRO package, which was extended to include Ehrenfest basis functions.
Ab initio multiple cloning algorithm for quantum nonadiabatic molecular dynamics
Makhov, Dmitry V.; Shalashilin, Dmitrii V. [Department of Chemistry, University of Leeds, Leeds LS2 9JT (United Kingdom); Glover, William J.; Martinez, Todd J. [Department of Chemistry and The PULSE Institute, Stanford University, Stanford, California 94305, USA and SLAC National Accelerator Laboratory, Menlo Park, California 94025 (United States)
2014-08-07
We present a new algorithm for ab initio quantum nonadiabatic molecular dynamics that combines the best features of ab initio Multiple Spawning (AIMS) and Multiconfigurational Ehrenfest (MCE) methods. In this new method, ab initio multiple cloning (AIMC), the individual trajectory basis functions (TBFs) follow Ehrenfest equations of motion (as in MCE). However, the basis set is expanded (as in AIMS) when these TBFs become sufficiently mixed, preventing prolonged evolution on an averaged potential energy surface. We refer to the expansion of the basis set as “cloning,” in analogy to the “spawning” procedure in AIMS. This synthesis of AIMS and MCE allows us to leverage the benefits of mean-field evolution during periods of strong nonadiabatic coupling while simultaneously avoiding mean-field artifacts in Ehrenfest dynamics. We explore the use of time-displaced basis sets, “trains,” as a means of expanding the basis set for little cost. We also introduce a new bra-ket averaged Taylor expansion (BAT) to approximate the necessary potential energy and nonadiabatic coupling matrix elements. The BAT approximation avoids the necessity of computing electronic structure information at intermediate points between TBFs, as is usually done in saddle-point approximations used in AIMS. The efficiency of AIMC is demonstrated on the nonradiative decay of the first excited state of ethylene. The AIMC method has been implemented within the AIMS-MOLPRO package, which was extended to include Ehrenfest basis functions.
A Bayesian Algorithm for Functional Mapping of Dynamic Complex Traits
Rongling Wu
2009-04-01
Full Text Available Functional mapping of dynamic traits measured in a longitudinal study was originally derived within the maximum likelihood (ML context and implemented with the EM algorithm. Although ML-based functional mapping possesses many favorable statistical properties in parameter estimation, it may be computationally intractable for analyzing longitudinal data with high dimensions and high measurement errors. In this article, we derive a general functional mapping framework for quantitative trait locus mapping of dynamic traits within the Bayesian paradigm. Markov chain Monte Carlo techniques were implemented for functional mapping to estimate biologically and statistically sensible parameters that model the structures of time-dependent genetic effects and covariance matrix. The Bayesian approach is useful to handle difficulties in constructing confidence intervals as well as the identifiability problem, enhancing the statistical inference of functional mapping. We have undertaken simulation studies to investigate the statistical behavior of Bayesian-based functional mapping and used a real example with F2 mice to validate the utilization and usefulness of the model.
Dynamic load balancing algorithm for molecular dynamics based on Voronoi cells domain decompositions
Fattebert, J.-L. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Richards, D.F. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Glosli, J.N. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
2012-12-01
We present a new algorithm for automatic parallel load balancing in classical molecular dynamics. It assumes a spatial domain decomposition of particles into Voronoi cells. It is a gradient method which attempts to minimize a cost function by displacing Voronoi sites associated with each processor/sub-domain along steepest descent directions. Excellent load balance has been obtained for quasi-2D and 3D practical applications, with up to 440·10^{6} particles on 65,536 MPI tasks.
A Density Based Dynamic Data Clustering Algorithm based on Incremental Dataset
K. R.S. Kumar
2012-01-01
Full Text Available Problem statement: Clustering and visualizing high-dimensional dynamic data is a challenging problem. Most of the existing clustering algorithms are based on the static statistical relationship among data. Dynamic clustering is a mechanism to adopt and discover clusters in real time environments. There are many applications such as incremental data mining in data warehousing applications, sensor network, which relies on dynamic data clustering algorithms. Approach: In this work, we present a density based dynamic data clustering algorithm for clustering incremental dataset and compare its performance with full run of normal DBSCAN, Chameleon on the dynamic dataset. Most of the clustering algorithms perform well and will give ideal performance with good accuracy measured with clustering accuracy, which is calculated using the original class labels and the calculated class labels. However, if we measure the performance with a cluster validation metric, then it will give another kind of result. Results: This study addresses the problems of clustering a dynamic dataset in which the data set is increasing in size over time by adding more and more data. So to evaluate the performance of the algorithms, we used Generalized Dunn Index (GDI, Davies-Bouldin index (DB as the cluster validation metric and as well as time taken for clustering. Conclusion: In this study, we have successfully implemented and evaluated the proposed density based dynamic clustering algorithm. The performance of the algorithm was compared with Chameleon and DBSCAN clustering algorithms. The proposed algorithm performed significantly well in terms of clustering accuracy as well as speed.
This paper presents a new optimization technique based on a multiple tabu search algorithm (MTS) to solve the dynamic economic dispatch (ED) problem with generator constraints. In the constrained dynamic ED problem, the load demand and spinning reserve capacity as well as some practical operation constraints of generators, such as ramp rate limits and prohibited operating zone are taken into consideration. The MTS algorithm introduces additional mechanisms such as initialization, adaptive searches, multiple searches, crossover and restarting process. To show its efficiency, the MTS algorithm is applied to solve constrained dynamic ED problems of power systems with 6 and 15 units. The results obtained from the MTS algorithm are compared to those achieved from the conventional approaches, such as simulated annealing (SA), genetic algorithm (GA), tabu search (TS) algorithm and particle swarm optimization (PSO). The experimental results show that the proposed MTS algorithm approaches is able to obtain higher quality solutions efficiently and with less computational time than the conventional approaches
Pothiya, Saravuth; Kongprawechnon, Waree [School of Communication, Instrumentation and Control, Sirindhorn International Institute of Technology, Thammasat University, P.O. Box 22, Pathumthani (Thailand); Ngamroo, Issarachai [Electrical Engineering Department, Faculty of Engineering, King Mongkut' s Institute of Technology Ladkrabang, Bangkok (Thailand)
2008-04-15
This paper presents a new optimization technique based on a multiple tabu search algorithm (MTS) to solve the dynamic economic dispatch (ED) problem with generator constraints. In the constrained dynamic ED problem, the load demand and spinning reserve capacity as well as some practical operation constraints of generators, such as ramp rate limits and prohibited operating zone are taken into consideration. The MTS algorithm introduces additional mechanisms such as initialization, adaptive searches, multiple searches, crossover and restarting process. To show its efficiency, the MTS algorithm is applied to solve constrained dynamic ED problems of power systems with 6 and 15 units. The results obtained from the MTS algorithm are compared to those achieved from the conventional approaches, such as simulated annealing (SA), genetic algorithm (GA), tabu search (TS) algorithm and particle swarm optimization (PSO). The experimental results show that the proposed MTS algorithm approaches is able to obtain higher quality solutions efficiently and with less computational time than the conventional approaches. (author)
Identification of Dynamic Parameters Based on Pseudo-Parallel Ant Colony Optimization Algorithm
ZHAO Feng-yao; MA Zhen-yue; ZHANG Yun-liang
2007-01-01
For the parameter identification of dynamic problems, a pseudo-parallel ant colony optimization (PPACO) algorithm based on graph-based ant system (AS) was introduced. On the platform of ANSYS dynamic analysis, the PPACO algorithm was applied to the identification of dynamic parameters successfully. Using simulated data of forces and displacements, elastic modulus E and damping ratio ξ was identified for a designed 3D finite element model, and the detailed identification step was given. Mathematical example and simulation example show that the proposed method has higher precision, faster convergence speed and stronger antinoise ability compared with the standard genetic algorithm and the ant colony optimization (ACO) algorithms.
A parallel clustered dynamic programming algorithm for discrete time optimal control problems
Optimal control of dynamical systems is a problem that arises in many areas of engineering and physical science. Due to the special structure of optimal control problems, currently there is no parallel algorithm that can solve optimal control problems efficiently on computers with a large number of processors. In this paper, we will introduce a new optimal control algorithm that permits massively parallel processing. The proposed algorithm, called Cluster Dynamic Programming, is a combination of two efficient serial algorithms, differential dynamic programming and a stagewise Newton's method. Parallel numerical results on an Intel iPSC/860 will be presented
Dynamic Fuzzy Logic Control of Genetic Algorithm Probabilities
Huijuan Guo; Yi Feng; Fei Hao; Shengtong Zhong; Shuai Li
2014-01-01
Genetic Algorithms are traditionally used to solve combinatorial optimization problems. The implementation of Genetic Algorithms involves of using genetic operators (crossover, mutation, selection, etc.). Meanwhile, paramters (such as population size, probabilities of crossover and mutation) of Genetic Algorithm need to be chosen or tuned. In this paper, we propose a hybrid Fuzzy-Genetic Algorithm (FLGA) approach to solve the multiprocessor scheduling problem. Based on traditional Genetic Alg...
Dynamic Task Scheduling Algorithm based on Ant Colony Scheme
Kamolov Nizomiddin Baxodirjonovich
2015-08-01
Full Text Available Many scientific applications running in Cloud Computing system are workflow applications that contains large number of tasks and in which tasks are connected by precedence relations. Efficient scheduling the workflow tasks become a challenging issue in Cloud Computing environments because the scheduling decides performance of the applications. Unfortunately, finding the optimal scheduling is known as NP-hard. Ant Colony Optimization algorithm can be applied to design efficient scheduling algorithms. Previous scheduling algorithms that use Ant Colony mechanism lack rapid adaptivity. This paper proposes a task scheduling algorithm that uses a modified Ant Colony Optimization. The modified version uses probability in order for ants to decide target machine. The proposed task scheduling algorithm is implemented in WorkflowSim in order to measure performance. The experimental results show that the proposed scheduling algorithm reduce average makespan to about 6.4% compared to a scheduling algorithm that uses basic Ant Colony Optimization scheme.
Maintenance of Process Control Algorithms based on Dynamic Program Slicing
Hansen, Ole Fink; Andersen, Nils Axel; Ravn, Ole
2010-01-01
behavior of a control algorithm, enables maintenance personnel to focus on only relevant parts of the algorithm and semi-automatically locate the part of the algorithm that is responsible for the reduced performance. The solution is tuning-free and can be applied to installed and running systems without......Today’s industrial control systems gradually lose performance after installation and must be regularly maintained by means of adjusting parameters and modifying the control algorithm, in order to regain high performance. Industrial control algorithms are complex software systems, and it is...... particularly difficult to locate causes of performance loss, while readjusting the algorithm once the cause of performance loss is actually realized and found is relatively simple. In this paper we present a software-engineering approach to the maintenance problem, which provides tools for exploring the...
A Density Based Dynamic Data Clustering Algorithm based on Incremental Dataset
K. R.S. Kumar; S. A.L. Mary
2012-01-01
Problem statement: Clustering and visualizing high-dimensional dynamic data is a challenging problem. Most of the existing clustering algorithms are based on the static statistical relationship among data. Dynamic clustering is a mechanism to adopt and discover clusters in real time environments. There are many applications such as incremental data mining in data warehousing applications, sensor network, which relies on dynamic data clustering algorithms. Approach: In this work, we present a ...
Explicit symplectic algorithms based on generating functions for charged particle dynamics
Zhang, Ruili; Tang, Yifa; Liu, Jian; He, Yang; Xiao, Jianyuan
2016-01-01
Dynamics of a charged particle in the canonical coordinates is a Hamiltonian system, and the well-known symplectic algorithm has been regarded as the de facto method for numerical integration of Hamiltonian systems due to its long-term accuracy and fidelity. For long-term simulations with high efficiency, explicit symplectic algorithms are desirable. However, it is widely accepted that explicit symplectic algorithms are only available for sum-separable Hamiltonians, and that this restriction severely limits the application of explicit symplectic algorithms to charged particle dynamics. To overcome this difficulty, we combine the familiar sum-split method and a generating function method to construct second and third order explicit symplectic algorithms for dynamics of charged particle. The generating function method is designed to generate explicit symplectic algorithms for product-separable Hamiltonian with form of $H(\\mathbf{p},\\mathbf{q})=\\mathbf{p}_{i}f(\\mathbf{q})$ or $H(\\mathbf{p},\\mathbf{q})=\\mathbf{q}...
Dynamic Fuzzy Logic Control of GeneticAlgorithm Probabilities
Feng, Yi
2008-01-01
Genetic algorithms are commonly used to solve combinatorial optimizationproblems. The implementation evolves using genetic operators (crossover, mutation,selection, etc.). Anyway, genetic algorithms like some other methods have parameters(population size, probabilities of crossover and mutation) which need to be tune orchosen.In this paper, our project is based on an existing hybrid genetic algorithmworking on the multiprocessor scheduling problem. We propose a hybrid Fuzzy-Genetic Algorithm ...
Qi, Wei; Zhang, Chi; Fu, Guangtao; Zhou, Huicheng
2016-02-01
It is widely recognized that optimization algorithm parameters have significant impacts on algorithm performance, but quantifying the influence is very complex and difficult due to high computational demands and dynamic nature of search parameters. The overall aim of this paper is to develop a global sensitivity analysis based framework to dynamically quantify the individual and interactive influence of algorithm parameters on algorithm performance. A variance decomposition sensitivity analysis method, Analysis of Variance (ANOVA), is used for sensitivity quantification, because it is capable of handling small samples and more computationally efficient compared with other approaches. The Shuffled Complex Evolution method developed at the University of Arizona algorithm (SCE-UA) is selected as an optimization algorithm for investigation, and two criteria, i.e., convergence speed and success rate, are used to measure the performance of SCE-UA. Results show the proposed framework can effectively reveal the dynamic sensitivity of algorithm parameters in the search processes, including individual influences of parameters and their interactive impacts. Interactions between algorithm parameters have significant impacts on SCE-UA performance, which has not been reported in previous research. The proposed framework provides a means to understand the dynamics of algorithm parameter influence, and highlights the significance of considering interactive parameter influence to improve algorithm performance in the search processes.
A HYBRID GRANULARITY PARALLEL ALGORITHM FOR PRECISE INTEGRATION OF STRUCTURAL DYNAMIC RESPONSES
Yuanyin Li; Xianlong Jin; Genguo Li
2008-01-01
Precise integration methods to solve structural dynamic responses and the corre-sponding time integration formula are composed of two parts: the multiplication of an exponential matrix with a vector and the integration term. The second term can be solved by the series solu-tion. Two hybrid granularity parallel algorithms are designed, that is, the exponential matrix and the first term are computed by the fine-grained parallel algorithm and the second term is com-puted by the coarse-grained parallel algorithm. Numerical examples show that these two hybrid granularity parallel algorithms obtain higher speedup and parallel efficiency than two existing parallel algorithms.
Efficient conjugate gradient algorithms for computation of the manipulator forward dynamics
Fijany, Amir; Scheid, Robert E.
1989-01-01
The applicability of conjugate gradient algorithms for computation of the manipulator forward dynamics is investigated. The redundancies in the previously proposed conjugate gradient algorithm are analyzed. A new version is developed which, by avoiding these redundancies, achieves a significantly greater efficiency. A preconditioned conjugate gradient algorithm is also presented. A diagonal matrix whose elements are the diagonal elements of the inertia matrix is proposed as the preconditioner. In order to increase the computational efficiency, an algorithm is developed which exploits the synergism between the computation of the diagonal elements of the inertia matrix and that required by the conjugate gradient algorithm.
Zhang, Zhihua; Sheng, Zheng; Shi, Hanqing; Fan, Zhiqiang
2016-01-01
Using the RFC technique to estimate refractivity parameters is a complex nonlinear optimization problem. In this paper, an improved cuckoo search (CS) algorithm is proposed to deal with this problem. To enhance the performance of the CS algorithm, a parameter dynamic adaptive operation and crossover operation were integrated into the standard CS (DACS-CO). Rechenberg's 1/5 criteria combined with learning factor were used to control the parameter dynamic adaptive adjusting process. The crossover operation of genetic algorithm was utilized to guarantee the population diversity. The new hybrid algorithm has better local search ability and contributes to superior performance. To verify the ability of the DACS-CO algorithm to estimate atmospheric refractivity parameters, the simulation data and real radar clutter data are both implemented. The numerical experiments demonstrate that the DACS-CO algorithm can provide an effective method for near-real-time estimation of the atmospheric refractivity profile from radar clutter.
Zhihua Zhang
2016-01-01
Full Text Available Using the RFC technique to estimate refractivity parameters is a complex nonlinear optimization problem. In this paper, an improved cuckoo search (CS algorithm is proposed to deal with this problem. To enhance the performance of the CS algorithm, a parameter dynamic adaptive operation and crossover operation were integrated into the standard CS (DACS-CO. Rechenberg’s 1/5 criteria combined with learning factor were used to control the parameter dynamic adaptive adjusting process. The crossover operation of genetic algorithm was utilized to guarantee the population diversity. The new hybrid algorithm has better local search ability and contributes to superior performance. To verify the ability of the DACS-CO algorithm to estimate atmospheric refractivity parameters, the simulation data and real radar clutter data are both implemented. The numerical experiments demonstrate that the DACS-CO algorithm can provide an effective method for near-real-time estimation of the atmospheric refractivity profile from radar clutter.
Zhang, Zhihua; Sheng, Zheng; Shi, Hanqing; Fan, Zhiqiang
2016-01-01
Using the RFC technique to estimate refractivity parameters is a complex nonlinear optimization problem. In this paper, an improved cuckoo search (CS) algorithm is proposed to deal with this problem. To enhance the performance of the CS algorithm, a parameter dynamic adaptive operation and crossover operation were integrated into the standard CS (DACS-CO). Rechenberg's 1/5 criteria combined with learning factor were used to control the parameter dynamic adaptive adjusting process. The crossover operation of genetic algorithm was utilized to guarantee the population diversity. The new hybrid algorithm has better local search ability and contributes to superior performance. To verify the ability of the DACS-CO algorithm to estimate atmospheric refractivity parameters, the simulation data and real radar clutter data are both implemented. The numerical experiments demonstrate that the DACS-CO algorithm can provide an effective method for near-real-time estimation of the atmospheric refractivity profile from radar clutter. PMID:27212938
Algorithms for New Product Development:. An Exercise in Thought Dynamics
Goldenberg, Y.; Horowitz, R.; Mazursky, D.; Solomon, S.
We generate new product ideas using algorithms which manipulate formally conceptual structures. These structures are abstract closed configurations composed of discrete elements which represent parts or properties of the products and of their immediate environment. The algorithms are assembled of a very limited number of fundamental operations acting on the elements. We present some successful field applications of the method carried out in leading international companies as well as an experiment providing quantitative proof of its superiority over usual ideas search.
Dynamic topology multi force particle swarm optimization algorithm and its application
Chen, Dongning; Zhang, Ruixing; Yao, Chengyu; Zhao, Zheyu
2016-01-01
Particle swarm optimization (PSO) algorithm is an effective bio-inspired algorithm but it has shortage of premature convergence. Researchers have made some improvements especially in force rules and population topologies. However, the current algorithms only consider a single kind of force rules and lack consideration of comprehensive improvement in both multi force rules and population topologies. In this paper, a dynamic topology multi force particle swarm optimization (DTMFPSO) algorithm is proposed in order to get better search performance. First of all, the principle of the presented multi force particle swarm optimization (MFPSO) algorithm is that different force rules are used in different search stages, which can balance the ability of global and local search. Secondly, a fitness-driven edge-changing (FE) topology based on the probability selection mechanism of roulette method is designed to cut and add edges between the particles, and the DTMFPSO algorithm is proposed by combining the FE topology with the MFPSO algorithm through concurrent evolution of both algorithm and structure in order to further improve the search accuracy. Thirdly, Benchmark functions are employed to evaluate the performance of the DTMFPSO algorithm, and test results show that the proposed algorithm is better than the well-known PSO algorithms, such as µPSO, MPSO, and EPSO algorithms. Finally, the proposed algorithm is applied to optimize the process parameters for ultrasonic vibration cutting on SiC wafer, and the surface quality of the SiC wafer is improved by 12.8% compared with the PSO algorithm in Ref. [25]. This research proposes a DTMFPSO algorithm with multi force rules and dynamic population topologies evolved simultaneously, and it has better search performance.
DU Mao-Kang; HE Bo; WANG Yong
2011-01-01
Recently, the cryptosystem based on chaos has attracted much attention. Wang and Yu (Commun. Nonlin. Sci. Numer. Simulat. 14(2009)574) proposed a block encryption algorithm based on dynamic sequences of multiple chaotic systems. We analyze the potential Saws in the algorithm. Then, a chosen-plaintext attack is presented. Some remedial measures are suggested to avoid the flaws effectively. Furthermore, an improved encryption algorithm is proposed to resist the attacks and to keep all the merits of the original cryptosystem.
Dynamic platform-independent meta-algorithms for graph-partitioning
Schwartz, Victor Scott
1998-01-01
Approved for public release, distribution is unlimited A dynamic platform-independent solver is developed for use with network and graph algorithms of operations research. This solver allows analysts to solve a large variety of problems without writing code. Algorithms from a library can be integrated into a meta-algorithm which also provides easy monitoring of solution progress. The solver, DORS, is demonstrated by heuristically solving a graph-partitioning problem to minimize the number ...
NONLINEAR FILTER METHOD OF GPS DYNAMIC POSITIONING BASED ON BANCROFT ALGORITHM
ZHANGQin; TAOBen-zao; ZHAOChao-ying; WANGLi
2005-01-01
Because of the ignored items after linearization, the extended Kalman filter (EKF) becomes a form of suboptimal gradient descent algorithm. The emanative tendency exists in GPS solution when the filter equations are ill-posed. The deviation in the estimation cannot be avoided. Furthermore, the true solution may be lost in pseudorange positioning because the linearized pseudorange equations are partial solutions. To solve the above problems in GPS dynamic positioning by using EKF, a closed-form Kalman filter method called the two-stage algorithm is presented for the nonlinear algebraic solution of GPS dynamic positioning based on the global nonlinear least squares closed algorithm--Bancroft numerical algorithm of American. The method separates the spatial parts from temporal parts during processing the GPS filter problems, and solves the nonlinear GPS dynamic positioning, thus getting stable and reliable dynamic positioning solutions.
Force-Based Incremental Algorithm for Mining Community Structure in Dynamic Network
Bo Yang; Da-You Liu
2006-01-01
Community structure is an important property of network. Being able to identify communities can provide invaluable help in exploiting and understanding both social and non-social networks. Several algorithms have been developed up till now. However, all these algorithms can work well only with small or moderate networks with vertexes of order 104.Besides, all the existing algorithms are off-line and cannot work well with highly dynamic networks such as web, in which web pages are updated frequently. When an already clustered network is updated, the entire network including original and incremental parts has to be recalculated, even though only slight changes are involved. To address this problem, an incremental algorithm is proposed, which allows for mining community structure in large-scale and dynamic networks. Based on the community structure detected previously, the algorithm takes little time to reclassify the entire network including both the original and incremental parts. Furthermore, the algorithm is faster than most of the existing algorithms such as Girvan and Newman's algorithm and its improved versions. Also, the algorithm can help to visualize these community structures in network and provide a new approach to research on the evolving process of dynamic networks.
Double Four-Bar Crank-Slider Mechanism Dynamic Balancing by Meta-Heuristic Algorithms
Habib Emdadi; Mahsa Yazdanian; Mir Mohammad Ettefagh; Mohammad-Reza Feizi-Derakhshi
2013-01-01
In this paper, a new method for dynamic balancing of double four-bar crank slider mechanism by meta- heuristic-based optimization algorithms is proposed. For this purpose, a proper objective function which is necessary for balancing of this mechanism and corresponding constraints has been obtained by dynamic modeling of the mechanism. Then PSO, ABC, BGA and HGAPSO algorithms have been applied for minimizing the defined cost function in optimization step. The optimization results have been stu...
Zhihua Zhang; Zheng Sheng; Hanqing Shi; Zhiqiang Fan
2016-01-01
Using the RFC technique to estimate refractivity parameters is a complex nonlinear optimization problem. In this paper, an improved cuckoo search (CS) algorithm is proposed to deal with this problem. To enhance the performance of the CS algorithm, a parameter dynamic adaptive operation and crossover operation were integrated into the standard CS (DACS-CO). Rechenberg’s 1/5 criteria combined with learning factor were used to control the parameter dynamic adaptive adjusting process. The crossov...
Behera, H S; Sahu, Sabyasachi; Bhoi, Sourav Kumar
2011-01-01
CPU being considered a primary computer resource, its scheduling is central to operating-system design. A thorough performance evaluation of various scheduling algorithms manifests that Round Robin Algorithm is considered as optimal in time shared environment because the static time is equally shared among the processes. We have proposed an efficient technique in the process scheduling algorithm by using dynamic time quantum in Round Robin. Our approach is based on the calculation of time quantum twice in single round robin cycle. Taking into consideration the arrival time, we implement the algorithm. Experimental analysis shows better performance of this improved algorithm over the Round Robin algorithm and the Shortest Remaining Burst Round Robin algorithm. It minimizes the overall number of context switches, average waiting time and average turn-around time. Consequently the throughput and CPU utilization is better.
Information dynamics algorithm for detecting communities in networks
Massaro, E; Bagnoli, F; Liò, P
2011-01-01
The problem of community detection is relevant in many scientific disciplines, from social science to statistical physics. Given the impact of community detection in many areas, such as psychology and social sciences, we have addressed the issue of modifying existing well performing algorithms by incorporating elements of the domain application fields, i.e. domain-inspired. We have focused on a psychology and social network - inspired approach which may be useful for further strengthening the link between social network studies and mathematics of community detection. Here we introduce a community-detection algorithm derived from the van Dongen's Markov Cluster algorithm (MCL) method by considering networks' nodes as agents capable to take decisions. In this framework we have introduced a memory factor to mimic a typical human behavior such as the oblivion effect. The method is based on information diffusion and it includes a non-linear processing phase. We test our method on two classical community benchmark ...
Battiste, Vernol; Lawton, George; Lachter, Joel; Brandt, Summer; Koteskey, Robert; Dao, Arik-Quang; Kraut, Josh; Ligda, Sarah; Johnson, Walter W.
2012-01-01
Managing the interval between arrival aircraft is a major part of the en route and TRACON controller s job. In an effort to reduce controller workload and low altitude vectoring, algorithms have been developed to allow pilots to take responsibility for, achieve and maintain proper spacing. Additionally, algorithms have been developed to create dynamic weather-free arrival routes in the presence of convective weather. In a recent study we examined an algorithm to handle dynamic re-routing in the presence of convective weather and two distinct spacing algorithms. The spacing algorithms originated from different core algorithms; both were enhanced with trajectory intent data for the study. These two algorithms were used simultaneously in a human-in-the-loop (HITL) simulation where pilots performed weather-impacted arrival operations into Louisville International Airport while also performing interval management (IM) on some trials. The controllers retained responsibility for separation and for managing the en route airspace and some trials managing IM. The goal was a stress test of dynamic arrival algorithms with ground and airborne spacing concepts. The flight deck spacing algorithms or controller managed spacing not only had to be robust to the dynamic nature of aircraft re-routing around weather but also had to be compatible with two alternative algorithms for achieving the spacing goal. Flight deck interval management spacing in this simulation provided a clear reduction in controller workload relative to when controllers were responsible for spacing the aircraft. At the same time, spacing was much less variable with the flight deck automated spacing. Even though the approaches taken by the two spacing algorithms to achieve the interval management goals were slightly different they seem to be simpatico in achieving the interval management goal of 130 sec by the TRACON boundary.
Optimization in Quaternion Dynamic Systems: Gradient, Hessian, and Learning Algorithms.
Xu, Dongpo; Xia, Yili; Mandic, Danilo P
2016-02-01
The optimization of real scalar functions of quaternion variables, such as the mean square error or array output power, underpins many practical applications. Solutions typically require the calculation of the gradient and Hessian. However, real functions of quaternion variables are essentially nonanalytic, which are prohibitive to the development of quaternion-valued learning systems. To address this issue, we propose new definitions of quaternion gradient and Hessian, based on the novel generalized Hamilton-real (GHR) calculus, thus making a possible efficient derivation of general optimization algorithms directly in the quaternion field, rather than using the isomorphism with the real domain, as is current practice. In addition, unlike the existing quaternion gradients, the GHR calculus allows for the product and chain rule, and for a one-to-one correspondence of the novel quaternion gradient and Hessian with their real counterparts. Properties of the quaternion gradient and Hessian relevant to numerical applications are also introduced, opening a new avenue of research in quaternion optimization and greatly simplified the derivations of learning algorithms. The proposed GHR calculus is shown to yield the same generic algorithm forms as the corresponding real- and complex-valued algorithms. Advantages of the proposed framework are illuminated over illustrative simulations in quaternion signal processing and neural networks. PMID:26087504
Dynamic gate algorithm for multimode fiber Bragg grating sensor systems
Ganziy, Denis; Jespersen, O.; Woyessa, Getinet;
2015-01-01
-to-noise ratio and different peak shapes. Our simulations and experiments demonstrate that the DGA method is fast and robust with better stability and accuracy than conventional algorithms. This makes it very attractive for future implementation in sensing systems, especially based on multimode fiber Bragg...
Arild Helseth
2015-12-01
Full Text Available Stochastic dual dynamic programming (SDDP has become a popular algorithm used in practical long-term scheduling of hydropower systems. The SDDP algorithm is computationally demanding, but can be designed to take advantage of parallel processing. This paper presents a novel parallel scheme for the SDDP algorithm, where the stage-wise synchronization point traditionally used in the backward iteration of the SDDP algorithm is partially relaxed. The proposed scheme was tested on a realistic model of a Norwegian water course, proving that the synchronization point relaxation significantly improves parallel efficiency.
A Dynamic Clustering-Based Routing Algorithm for Wireless Senor Networks
Kai Zhou
2008-01-01
Full Text Available Wireless sensor networks (WSN is an energy limited system, thus this study presents an energy-aware quality of service (QoS routing algorithm for it, which can also run efficiently with best-effort traffic. Furthermore, our work differs from existing algorithms in two ways: (1 We improve the first order energy consumption model with dynamic clustering; (2 We use clustering to build the multi-objectives programming model to support QoS. Simulations and comparisons with some typical route algorithms show that our algorithm is robust and effective.
An Algorithm for the Stochastic Simulation of Gene Expression and Heterogeneous Population Dynamics
Charlebois, Daniel A; Fraser, Dawn; Kaern, Mads
2011-01-01
We present an algorithm for the stochastic simulation of gene expression and heterogeneous population dynamics. The algorithm combines an exact method to simulate molecular-level fluctuations in single cells and a constant-number Monte Carlo method to simulate time-dependent statistical characteristics of growing cell populations. To benchmark performance, we compare simulation results with steadystate and time-dependent analytical solutions for several scenarios, including steadystate and time-dependent gene expression, and the effects on population heterogeneity of cell growth, division, and DNA replication. This comparison demonstrates that the algorithm provides an efficient and accurate approach to simulate how complex biological features influence gene expression. We also use the algorithm to model gene expression dynamics within "bet-hedging" cell populations during their adaption to environmental stress. These simulations indicate that the algorithm provides a framework suitable for simulating and ana...
DARAL: A Dynamic and Adaptive Routing Algorithm for Wireless Sensor Networks.
Estévez, Francisco José; Glösekötter, Peter; González, Jesús
2016-01-01
The evolution of Smart City projects is pushing researchers and companies to develop more efficient embedded hardware and also more efficient communication technologies. These communication technologies are the focus of this work, presenting a new routing algorithm based on dynamically-allocated sub-networks and node roles. Among these features, our algorithm presents a fast set-up time, a reduced overhead and a hierarchical organization, which allows for the application of complex management techniques. This work presents a routing algorithm based on a dynamically-allocated hierarchical clustering, which uses the link quality indicator as a reference parameter, maximizing the network coverage and minimizing the control message overhead and the convergence time. The present work based its test scenario and analysis in the density measure, considered as a node degree. The routing algorithm is compared with some of the most well known routing algorithms for different scenario densities. PMID:27347962
DARAL: A Dynamic and Adaptive Routing Algorithm for Wireless Sensor Networks
Estévez, Francisco José; Glösekötter, Peter; González, Jesús
2016-01-01
The evolution of Smart City projects is pushing researchers and companies to develop more efficient embedded hardware and also more efficient communication technologies. These communication technologies are the focus of this work, presenting a new routing algorithm based on dynamically-allocated sub-networks and node roles. Among these features, our algorithm presents a fast set-up time, a reduced overhead and a hierarchical organization, which allows for the application of complex management techniques. This work presents a routing algorithm based on a dynamically-allocated hierarchical clustering, which uses the link quality indicator as a reference parameter, maximizing the network coverage and minimizing the control message overhead and the convergence time. The present work based its test scenario and analysis in the density measure, considered as a node degree. The routing algorithm is compared with some of the most well known routing algorithms for different scenario densities. PMID:27347962
Resource Matchmaking Algorithm using Dynamic Rough Set in Grid Environment
Ataollahi, Iraj
2009-01-01
Grid environment is a service oriented infrastructure in which many heterogeneous resources participate to provide the high performance computation. One of the bug issues in the grid environment is the vagueness and uncertainty between advertised resources and requested resources. Furthermore, in an environment such as grid dynamicity is considered as a crucial issue which must be dealt with. Classical rough set have been used to deal with the uncertainty and vagueness. But it can just be used on the static systems and can not support dynamicity in a system. In this work we propose a solution, called Dynamic Rough Set Resource Discovery (DRSRD), for dealing with cases of vagueness and uncertainty problems based on Dynamic rough set theory which considers dynamic features in this environment. In this way, requested resource properties have a weight as priority according to which resource matchmaking and ranking process is done. We also report the result of the solution obtained from the simulation in GridSim s...
Igeta, Hideki; Hasegawa, Mikio
Chaotic dynamics have been effectively applied to improve various heuristic algorithms for combinatorial optimization problems in many studies. Currently, the most used chaotic optimization scheme is to drive heuristic solution search algorithms applicable to large-scale problems by chaotic neurodynamics including the tabu effect of the tabu search. Alternatively, meta-heuristic algorithms are used for combinatorial optimization by combining a neighboring solution search algorithm, such as tabu, gradient, or other search method, with a global search algorithm, such as genetic algorithms (GA), ant colony optimization (ACO), or others. In these hybrid approaches, the ACO has effectively optimized the solution of many benchmark problems in the quadratic assignment problem library. In this paper, we propose a novel hybrid method that combines the effective chaotic search algorithm that has better performance than the tabu search and global search algorithms such as ACO and GA. Our results show that the proposed chaotic hybrid algorithm has better performance than the conventional chaotic search and conventional hybrid algorithms. In addition, we show that chaotic search algorithm combined with ACO has better performance than when combined with GA.
Dynamic Deployment of Wireless Sensor Networks by Biogeography Based Optimization Algorithm
Luo Liu
2012-06-01
Full Text Available As the usage and development of wireless sensor networks increases, problems related to these networks are becoming apparent. Dynamic deployment is one of the main topics that directly affects the performance of the wireless sensor networks. In this paper, biogeography-based optimization is applied to the dynamic deployment of static and mobile sensor networks to achieve better performance by trying to increase the coverage area of the network. A binary detection model is considered to obtain realistic results while computing the effectively covered area. Performance of the algorithm is compared with that of the artificial bee colony algorithm, Homo-H-VFCPSO and stud genetic algorithm that are also population-based optimization algorithms. Results show biogeography-based optimization can be preferable in the dynamic deployment of wireless sensor networks.
Probabilistic dynamic deployment of wireless sensor networks by artificial bee colony algorithm.
Ozturk, Celal; Karaboga, Dervis; Gorkemli, Beyza
2011-01-01
As the usage and development of wireless sensor networks are increasing, the problems related to these networks are being realized. Dynamic deployment is one of the main topics that directly affect the performance of the wireless sensor networks. In this paper, the artificial bee colony algorithm is applied to the dynamic deployment of stationary and mobile sensor networks to achieve better performance by trying to increase the coverage area of the network. A probabilistic detection model is considered to obtain more realistic results while computing the effectively covered area. Performance of the algorithm is compared with that of the particle swarm optimization algorithm, which is also a swarm based optimization technique and formerly used in wireless sensor network deployment. Results show artificial bee colony algorithm can be preferable in the dynamic deployment of wireless sensor networks. PMID:22163942
Probabilistic Dynamic Deployment of Wireless Sensor Networks by Artificial Bee Colony Algorithm
Dervis Karaboga
2011-06-01
Full Text Available As the usage and development of wireless sensor networks are increasing, the problems related to these networks are being realized. Dynamic deployment is one of the main topics that directly affect the performance of the wireless sensor networks. In this paper, the artificial bee colony algorithm is applied to the dynamic deployment of stationary and mobile sensor networks to achieve better performance by trying to increase the coverage area of the network. A probabilistic detection model is considered to obtain more realistic results while computing the effectively covered area. Performance of the algorithm is compared with that of the particle swarm optimization algorithm, which is also a swarm based optimization technique and formerly used in wireless sensor network deployment. Results show artificial bee colony algorithm can be preferable in the dynamic deployment of wireless sensor networks.
Zhou, Xu; Liu, Yanheng; Li, Bin; Sun, Geng
2015-10-01
Identifying community structures in static network misses the opportunity to capture the evolutionary patterns. So community detection in dynamic network has attracted many researchers. In this paper, a multiobjective biogeography based optimization algorithm with decomposition (MBBOD) is proposed to solve community detection problem in dynamic networks. In the proposed algorithm, the decomposition mechanism is adopted to optimize two evaluation objectives named modularity and normalized mutual information simultaneously, which measure the quality of the community partitions and temporal cost respectively. A novel sorting strategy for multiobjective biogeography based optimization is presented for comparing quality of habitats to get species counts. In addition, problem-specific migration and mutation model are introduced to improve the effectiveness of the new algorithm. Experimental results both on synthetic and real networks demonstrate that our algorithm is effective and promising, and it can detect communities more accurately in dynamic networks compared with DYNMOGA and FaceNet.
Orly Yadid-Pecht
2013-10-01
Full Text Available Tone mapping algorithms are used to adapt captured wide dynamic range (WDR scenes to the limited dynamic range of available display devices. Although there are several tone mapping algorithms available, most of them require manual tuning of their rendering parameters. In addition, the high complexities of some of these algorithms make it difficult to implement efficient real-time hardware systems. In this work, a real-time hardware implementation of an exponent-based tone mapping algorithm is presented. The algorithm performs a mixture of both global and local compression on colored WDR images. An automatic parameter selector has been proposed for the tone mapping algorithm in order to achieve good tone-mapped images without manual reconfiguration of the algorithm for each WDR image. Both algorithms are described in Verilog and synthesized for a field programmable gate array (FPGA. The hardware architecture employs a combination of parallelism and system pipelining, so as to achieve a high performance in power consumption, hardware resources usage and processing speed. Results show that the hardware architecture produces images of good visual quality that can be compared to software-based tone mapping algorithms. High peak signal-to-noise ratio (PSNR and structural similarity (SSIM scores were obtained when the results were compared with output images obtained from software simulations using MATLAB.
Advanced Dynamically Adaptive Algorithms for Stochastic Simulations on Extreme Scales
Xiu, Dongbin [Purdue Univ., West Lafayette, IN (United States)
2016-06-21
The focus of the project is the development of mathematical methods and high-performance com- putational tools for stochastic simulations, with a particular emphasis on computations on extreme scales. The core of the project revolves around the design of highly e cient and scalable numer- ical algorithms that can adaptively and accurately, in high dimensional spaces, resolve stochastic problems with limited smoothness, even containing discontinuities.
A local flocking algorithm of multi-agent dynamic systems
Pei, Huiqin; Chen, Shiming; Lai, Qiang
2015-11-01
In this paper, the local flocking of multi-agent systems is investigated, which means all agents form some groups of surrounding multiple targets with the partial information exchange. For the purpose of realising local multi-flocking, a control algorithm of local flocking is proposed, which is a biologically inspired approach that assimilates key characteristics of flocking and anti-flocking. In the process of surrounding mobile targets through the control algorithm, all agents can adaptively choose between two work modes to depend on the variation of visual field and the number of pursuing agents with the mobile target. One is a flocking pursuing mode which is that some agents pursue each mobile target, the other is an anti-flocking searching mode that means with the exception of the pursing agents of mobile targets, other agents respectively hunt for optimal the mobile target with a closest principle between the agent and the target. In two work modes, the agents are controlled severally via the different control protocol. By the Lyapunov theorem, the stability of the second-order multi-agent system is proven in detail. Finally, simulation results verify the effectiveness of the proposed algorithm.
A Rama Mohan Rao; T V S R Appa Rao; B Dattaguru
2004-02-01
The work reported in this paper is motivated by the need to develop portable parallel processing algorithms and codes which can run on a variety of hardware platforms without any modiﬁcations. The prime aim of the research work reported here is to test the portability of the parallel algorithms and also to study and understand the comparative efﬁciencies of three parallel algorithms developed for implicit time integration technique. The standard message passing interface (MPI) is used to develop parallel algorithms for computing nonlinear dynamic response of large structures employing implicit time-marching scheme. The parallel algorithms presented in this paper are developed under the broad framework of non-overlapped domain decomposition technique. Numerical studies indicate that the parallel algorithm devised employing the conventional form of Newmark time integration algorithm is faster than the predictor–corrector form. It is also accurate and highly adaptive to ﬁne grain computations. The group implicit algorithm is found to be extremely superior in performance when compared to the other two parallel algorithms. This algorithm is better suited for large size problems on coarse grain environment as the resulting submeshes will obviously be large and thus permit larger time steps without losing accuracy.
Stable algorithm for event detection in event-driven particle dynamics
Bannerman, Marcus N.; Strobl, Severin; Formella, Arno; Poeschel, Thorsten
2012-01-01
Event-Driven Particle Dynamics is a fast and precise method to simulate particulate systems of all scales. In this work it is demonstrated that, despite the high accuracy of the method, the finite machine precision leads to simulations entering invalid states where the dynamics are undefined. A general event-detection algorithm is proposed which handles these situations in a stable and efficient manner. This requires a definition of the dynamics of invalid states and leads to improved algorit...
Rong, Aiying; Hakonen, Henri; Lahdelma, Risto
2009-01-01
This paper addresses the unit commitment (UC) in multi-period combined heat and power (CHP) production planning under the deregulated power market. In CHP plants (units), generation of heat and power follows joint characteristics, which implies that it is difficult to determine the relative cost...... efficiency of the plants. We introduce in this paper the DRDP-RSC algorithm, which is a dynamic regrouping based dynamic programming (DP) algorithm based on linear relaxation of the ON/OFF states of the units, sequential commitment of units in small groups. Relaxed states of the plants are used to reduce the...
Dynamic Communication Performance Enhancement in Hierarchical Torus Network by Selection Algorithm
MM Hafizur Rahman
2012-03-01
Full Text Available A Hierarchical Torus Network (HTN is a 2D-torus network of multiple basic modules, in which the basic modules are 3D-torus networks that are hierarchically interconnected for higher-level networks. The static network performance of the HTN and its dynamic communication performance using the deterministic, dimension-order routing algorithm have already been evaluated and shown to be superior to the performance of other conventional and hierarchical interconnection networks. However, the assessment of the dynamic communication performance improvement of HTN by the efficient use of both the physical link and virtual channels has not yet been evaluated. This paper addresses three adaptive routing algorithms -- link-selection, channel-selection, and a combination of link-selection and channel-selection -- for the efficient use of physical links and virtual channels of an HTN to enhance dynamic communication performance. It also proves that the proposed adaptive routing algorithms are deadlock-free with 3 virtual channels. The dynamic communication performances of an HTN is evaluated by using dimension-order routing and proposed adaptive routing algorithms under various traffic patterns. It is found that the dynamic communication performance of an HTN using these adaptive routing algorithms are better than when the dimension-order routing is used, in terms of network throughput.
Evaluation of dynamic bandwidth allocation algorithms in GPON networks
Ozimkiewicz, J.; Ruepp, Sarah Renée; Dittmann, Lars;
2010-01-01
In this paper, two approaches for Dynamic Bandwidth Allocation in GPON networks are proposed, and validated through simulations in the OPNET modeler. One approach address a Status Reporting scheme, where the bandwidth allocation originates from the client request. The second use a centralized Non...... services....
Graph-theoretic algorithm for hierarchial decomposition of dynamic systems
Pichai, V.; Sezer, M.E.; Siljak, D.D.
1982-03-24
A graph-theoretic scheme is proposed for partitioning of dynamic systems into hierarchially ordered subsystems having independent inputs and outputs. The resulting subsystems are input-output reachable as well as structurally controllable and observable, so that a piece-by-piece design of estimators and controllers can be accomplished for systems with large dimensions without excessive computer requirements.
A new simulation algorithm for lattice QCD with dynamical quarks
Bunk, B.; Jansen, K.; Jegerlehner, B.; Lüscher, M.; Simma, H.; Sommer, R
1994-01-01
A previously introduced multi-boson technique for the simulation of QCD with dynamical quarks is described and some results of first test runs on a $6^3\\times12$ lattice with Wilson quarks and gauge group SU(2) are reported.
Detectability thresholds and optimal algorithms for community structure in dynamic networks
Ghasemian, Amir; Clauset, Aaron; Moore, Cristopher; Peel, Leto
2015-01-01
We study the fundamental limits on learning latent community structure in dynamic networks. Specifically, we study dynamic stochastic block models where nodes change their community membership over time, but where edges are generated independently at each time step. In this setting (which is a special case of several existing models), we are able to derive the detectability threshold exactly, as a function of the rate of change and the strength of the communities. Below this threshold, we claim that no algorithm can identify the communities better than chance. We then give two algorithms that are optimal in the sense that they succeed all the way down to this limit. The first uses belief propagation (BP), which gives asymptotically optimal accuracy, and the second is a fast spectral clustering algorithm, based on linearizing the BP equations. We verify our analytic and algorithmic results via numerical simulation, and close with a brief discussion of extensions and open questions.
A New Multiobjective Evolutionary Algorithm for Community Detection in Dynamic Complex Networks
Guoqiang Chen
2013-01-01
Full Text Available Community detection in dynamic networks is an important research topic and has received an enormous amount of attention in recent years. Modularity is selected as a measure to quantify the quality of the community partition in previous detection methods. But, the modularity has been exposed to resolution limits. In this paper, we propose a novel multiobjective evolutionary algorithm for dynamic networks community detection based on the framework of nondominated sorting genetic algorithm. Modularity density which can address the limitations of modularity function is adopted to measure the snapshot cost, and normalized mutual information is selected to measure temporal cost, respectively. The characteristics knowledge of the problem is used in designing the genetic operators. Furthermore, a local search operator was designed, which can improve the effectiveness and efficiency of community detection. Experimental studies based on synthetic datasets show that the proposed algorithm can obtain better performance than the compared algorithms.
An FDTD algorithm for simulating light propagation in anisotropic dynamic gain media
Al-Jabr, A. A.
2014-05-02
Simulating light propagation in anisotropic dynamic gain media such as semiconductors and solid-state lasers using the finite difference time-domain FDTD technique is a tedious process, as many variables need to be evaluated in the same instant of time. The algorithm has to take care of the laser dynamic gain, rate equations, anisotropy and dispersion. In this paper, to the best of our knowledge, we present the first algorithm that solves this problem. The algorithm is based on separating calculations into independent layers and hence solving each problem in a layer of calculations. The anisotropic gain medium is presented and tested using a one-dimensional set-up. The algorithm is then used for the analysis of a two-dimensional problem.
Bid-based dynamic economic dispatch with an efficient interior point algorithm
An efficient interior point algorithm for solving the bid-based dynamic economic dispatch (BBDED) problem is proposed in this paper. This algorithm is an extension of interior point quadratic programming (IPQP), and is called the predictor-corrector interior point quadratic programming (PCIPQP) algorithm. In a competitive electricity market, the dynamic economic dispatch has evolved to become bid-based framework to maximize the profits and achieve the resource scheduling. To generate a physically feasible dispatch and market spot prices, we form BBDED problem of dealing with various constraints such as ramp rates, transmission line capacity and emission constraints. In this paper, BBDED allows both supply-side and demand-side bids in the spot market, which includes multi-player, multi-period and a large number of constraints. Compared with the pure IPQP algorithm, PCIPQP is more attractive in performance, robustness, and the property of convergence. Many numerical tests reconfirmed the advantages of the predictor-corrector method. (Author)
Hou, Peng; Hu, Weihao; Chen, Cong;
2016-01-01
(MST) algorithm, an improved algorithm, the Dynamic Minimum Spanning Tree (DMST) algorithm is proposed. The current carrying capacity of the cable is considered to be the main constraint and the cable sectional area is changed dynamically. An irregular shaped wind farm is chosen as the studie case...... and the results are compared with the layout obtained by a traditional MST algorithm. Simulation results show that the proposed method is an effective way for offshore wind farm collection system layout design....
A dynamic programming algorithm for the space allocation and aisle positioning problem
Peter Bodnar; Jens Lysgaard
2014-01-01
The space allocation and aisle positioning problem (SAAPP) in a material handling system with gravity flow racks is the problem of minimizing the total number of replenishments over a period subject to practical constraints related to the need for aisles granting safe and easy access to storage locations. In this paper, we develop an exact dynamic programming algorithm for the SAAPP. The computational study shows that our exact algorithm can be used to find optimal solutions for numerous SAAP...
A functional clustering algorithm for the analysis of dynamic network data
Feldt, S.; Waddell, J; Hetrick, V. L.; Berke, J. D.; Zochowski, M.
2008-01-01
We formulate a novel technique for the detection of functional clusters in discrete event data. The advantage of this algorithm is that no prior knowledge of the number of functional groups is needed, as our procedure progressively combines data traces and derives the optimal clustering cutoff in a simple and intuitive manner through the use of surrogate data sets. In order to demonstrate the power of this algorithm to detect changes in network dynamics and connectivity, we apply it to both s...
Frigerio, Marco; Buchli, Jonas; Caldwell, Darwin G.
2013-01-01
Rigid body dynamics algorithms play a crucial role in several components of a robot controller and simulations. Real time constraints in high frequency control loops and time requirements of specific applications demand these functions to be very efficient. Despite the availability of established algorithms, their efficient implementation for a specific robot still is a tedious and error-prone task. However, these components are simply necessary to get high performance controllers. To achieve...
Diamantidis A. C.; Papadopoulos C. T.
2004-01-01
In this study, the buffer allocation problem (BAP) in homogeneous, asymptotically reliable serial production lines is considered. A known aggregation method, given by Lim, Meerkov, and Top (1990), for the performance evaluation (i.e., estimation of throughput) of this type of production lines when the buffer allocation is known, is used as an evaluative method in conjunction with a newly developed dynamic programming (DP) algorithm for the BAP. The proposed algorithm is applied to production ...
Point group identification algorithm in dynamic response analysis of nonlinear stochastic systems
Li, Tao; Chen, Jian-bing; Li, Jie
2016-03-01
The point group identification (PGI) algorithm is proposed to determine the representative point sets in response analysis of nonlinear stochastic dynamic systems. The PGI algorithm is employed to identify point groups and their feature points in an initial point set by combining subspace clustering analysis and the graph theory. Further, the representative point set of the random-variate space is determined according to the minimum generalized F-discrepancy. The dynamic responses obtained by incorporating the algorithm PGI into the probability density evolution method (PDEM) are compared with those by the Monte Carlo simulation method. The investigations indicate that the proposed method can reduce the number of the representative points, lower the generalized F-discrepancy of the representative point set, and also ensure the accuracy of stochastic structural dynamic analysis.
Dynamic scheduling study on engineering machinery of clusters using multi-agent system ant algorithm
Gao, Qiang; Wang, Hongli; Guo, Long; Xiang, Jianping
2005-12-01
In the process of road surface construction, dispatchers' scheduling was experiential and blindfold in some degree and static scheduling restricted the continuity of the construction. Serious problems such as labor holdup, material awaiting and scheduling delay could occur when the old scheduling technique was used. This paper presents ant colony algorithm based on MAS that has the abilities of intelligentized modeling and dynamic scheduling. MAS model deals with single agent's communication and corresponding in engineering machinery of clusters firstly, next we apply ant colony algorithm to solve dynamic scheduling in the plant. Ant colony algorithm can optimize the match of agents and make the system dynamic balance. The effectiveness of the proposed method is demonstrated with MATLAB simulations.
Adaptive Dynamic Programming for Control Algorithms and Stability
Zhang, Huaguang; Luo, Yanhong; Wang, Ding
2013-01-01
There are many methods of stable controller design for nonlinear systems. In seeking to go beyond the minimum requirement of stability, Adaptive Dynamic Programming for Control approaches the challenging topic of optimal control for nonlinear systems using the tools of adaptive dynamic programming (ADP). The range of systems treated is extensive; affine, switched, singularly perturbed and time-delay nonlinear systems are discussed as are the uses of neural networks and techniques of value and policy iteration. The text features three main aspects of ADP in which the methods proposed for stabilization and for tracking and games benefit from the incorporation of optimal control methods: • infinite-horizon control for which the difficulty of solving partial differential Hamilton–Jacobi–Bellman equations directly is overcome, and proof provided that the iterative value function updating sequence converges to the infimum of all the value functions obtained by admissible control law sequences; • finite-...
Symmetry preserving algorithm for a dynamic contact-impact problem
Gabriel, Dušan; Plešek, Jiří
Plzeň : Západočeská univerzita v Plzni, 2004 - (Vimmer, J.), s. 103-108 ISBN 80-7043-314-0. [Conference with international participation Computational mechanics 2004 /20./. Nečtiny (CZ), 08.10.2004-10.10.2004] Institutional research plan: CEZ:AV0Z2076919 Keywords : contact * Gauss point searchl * explicit transient dynamics Subject RIV: JJ - Other Materials
A study on the dynamic tie points ASI algorithm in the Arctic Ocean
HAO Guanghua; SU Jie
2015-01-01
Sea ice concentration is an important parameter for polar sea ice monitoring. Based on 89 GHz AMSR-E (Advanced Microwave Scanning Radiometer for Earth Observing System) data, a gridded high-resolution passive microwave sea ice concentration product can be obtained using the ASI (the Arctic Radiation And Turbulence Interaction Study (ARTIST) Sea Ice) retrieval algorithm. Instead of using fixed-point values, we developed ASI algorithm based on daily changed tie points, called as the dynamic tie point ASI algorithm in this study. Here the tie points are expressed as the brightness temperature polarization difference of open water and 100% sea ice. In 2010, the yearly-averaged tie points of open water and sea ice in Arctic are estimated to be 50.8 K and 7.8 K, respectively. It is confirmed that the sea ice concentrations retrieved by the dynamic tie point ASI algorithm can increase (decrease) the sea ice concentrations in low-value (high-value) areas. This improved the sea ice concentrations by present retrieval algorithm from microwave data to some extent. Comparing with the products using fixed tie points, the sea ice concentrations retrieved from AMSR-E data by using the dynamic tie point ASI algorithm are closer to those obtained from MODIS (Moderate-resolution Imaging Spectroradiometer) data. In 40 selected cloud-free sample regions, 95% of our results have smaller mean differences and 75% of our results have lower root mean square (RMS) differences compare with those by the fixed tie points.
A Dynamic Multistage Hybrid Swarm Intelligence Optimization Algorithm for Function Optimization
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.
A Novel Dynamic Bandwidth Assignment Algorithm for Multi-Services EPONs
CHEN Xue; ZHANG Yang; HUANG Xiang; DENG Yu; SUN Shu-he
2005-01-01
In this paper we propose a novel Dynamic Bandwidth Assignment (DBA) algorithm for Ethernet-based Passive Optical Networks (EPON) which offers multiple kinds of services. To satisfy crucial Quality of Service (QoS) requirement for Time Division Multiplexing (TDM) service and achieve fair and high bandwidth utilization simultaneously, the algorithm integrates periodic, for TDM service, and polling granting for Ethernet service. Detailed simulation shows that the algorithm guarantees carrier-grade QoS for TDM service, high bandwidth utilization and good fairness of bandwidth assignment among Optical Network Units (ONU).
Li, Xiaofeng; Xiang, Suying; Zhu, Pengfei; Wu, Min
2015-12-01
In order to avoid the inherent deficiencies of the traditional BP neural network, such as slow convergence speed, that easily leading to local minima, poor generalization ability and difficulty in determining the network structure, the dynamic self-adaptive learning algorithm of the BP neural network is put forward to improve the function of the BP neural network. The new algorithm combines the merit of principal component analysis, particle swarm optimization, correlation analysis and self-adaptive model, hence can effectively solve the problems of selecting structural parameters, initial connection weights and thresholds and learning rates of the BP neural network. This new algorithm not only reduces the human intervention, optimizes the topological structures of BP neural networks and improves the network generalization ability, but also accelerates the convergence speed of a network, avoids trapping into local minima, and enhances network adaptation ability and prediction ability. The dynamic self-adaptive learning algorithm of the BP neural network is used to forecast the total retail sale of consumer goods of Sichuan Province, China. Empirical results indicate that the new algorithm is superior to the traditional BP network algorithm in predicting accuracy and time consumption, which shows the feasibility and effectiveness of the new algorithm.
Dynamic gradient descent learning algorithms for enhanced empirical modeling of power plants
A newly developed dynamic gradient descent-based learning algorithm is used to train a recurrent multilayer perceptron network for use in empirical modeling of power plants. The two main advantages of the proposed learning algorithm are its ability to consider past error gradient information for future use and the two forward passes associated with its implementation, instead of one forward and one backward pass of the backpropagation algorithm. The latter advantage results in computational time saving because both passes can be performed simultaneously. The dynamic learning algorithm is used to train a hybrid feedforward/feedback neural network, a recurrent multilayer perceptron, which was previously found to exhibit good interpolation and extrapolation capabilities in modeling nonlinear dynamic systems. One of the drawbacks, however, of the previously reported work has been the long training times associated with accurate empirical models. The enhanced learning capabilities provided by the dynamic gradient descent-based learning algorithm are demonstrated by a case study of a steam power plant. The number of iterations required for accurate empirical modeling has been reduced from tens of thousands to hundreds, thus significantly expediting the learning process
The generation algorithm of arbitrary polygon animation based on dynamic correction
Hou Ya Wei
2016-01-01
Full Text Available This paper, based on the key-frame polygon sequence, proposes a method that makes use of dynamic correction to develop continuous animation. Firstly we use quadratic Bezier curve to interpolate the corresponding sides vector of polygon sequence consecutive frame and realize the continuity of animation sequences. And then, according to Bezier curve characteristic, we conduct dynamic regulation to interpolation parameters and implement the changing smoothness. Meanwhile, we take use of Lagrange Multiplier Method to correct the polygon and close it. Finally, we provide the concrete algorithm flow and present numerical experiment results. The experiment results show that the algorithm acquires excellent effect.
An elementary singularity-free Rotational Brownian Dynamics algorithm for anisotropic particles
Ilie, Ioana M.; Briels, Wim J. [Computational Biophysics, Faculty of Science and Technology, University of Twente, P.O. Box 217, 7500 AE Enschede (Netherlands); MESA+ Institute for Nanotechnology, University of Twente, P.O. Box 217, 7500 AE Enschede (Netherlands); Otter, Wouter K. den, E-mail: w.k.denotter@utwente.nl [Computational Biophysics, Faculty of Science and Technology, University of Twente, P.O. Box 217, 7500 AE Enschede (Netherlands); MESA+ Institute for Nanotechnology, University of Twente, P.O. Box 217, 7500 AE Enschede (Netherlands); Multi Scale Mechanics, Faculty of Engineering Technology, University of Twente, P.O. Box 217, 7500 AE Enschede (Netherlands)
2015-03-21
Brownian Dynamics is the designated technique to simulate the collective dynamics of colloidal particles suspended in a solution, e.g., the self-assembly of patchy particles. Simulating the rotational dynamics of anisotropic particles by a first-order Langevin equation, however, gives rise to a number of complications, ranging from singularities when using a set of three rotational coordinates to subtle metric and drift corrections. Here, we derive and numerically validate a quaternion-based Rotational Brownian Dynamics algorithm that handles these complications in a simple and elegant way. The extension to hydrodynamic interactions is also discussed.
An elementary singularity-free Rotational Brownian Dynamics algorithm for anisotropic particles
Brownian Dynamics is the designated technique to simulate the collective dynamics of colloidal particles suspended in a solution, e.g., the self-assembly of patchy particles. Simulating the rotational dynamics of anisotropic particles by a first-order Langevin equation, however, gives rise to a number of complications, ranging from singularities when using a set of three rotational coordinates to subtle metric and drift corrections. Here, we derive and numerically validate a quaternion-based Rotational Brownian Dynamics algorithm that handles these complications in a simple and elegant way. The extension to hydrodynamic interactions is also discussed
Symetry preserving algorithm for a dynamic contact-impact problem
Gabriel, Dušan; Plešek, Jiří; Valeš, František; Okrouhlík, Miloslav
Dordrecht : Springer, 2006 - (Mota Soares, C.; Martins, J.; Rodrigues, H.; Ambrosio, J.). s. 318-318 ISBN 1-4020-4994-3. [European Conference on Computational Mechanics /3./. 05.06.2006-08.06.2006, Lisbon] R&D Projects: GA ČR(CZ) GP101/03/D153; GA ČR(CZ) GA101/06/0914; GA ČR(CZ) GA101/06/0213; GA AV ČR(CZ) 1ET400760509 Institutional research plan: CEZ:AV0Z20760514 Keywords : contact-impact * Gauss point search * explicit dynamics Subject RIV: JJ - Other Materials
Symetry preserving algorithm for a dynamic contact-impact problem
Gabriel, Dušan; Plešek, Jiří; Valeš, František; Okrouhlík, Miloslav
Dordrecht : Springer, 2006 - (Mota Soares, C.; Martins, J.; Rodrigues, H.; Ambrosio, J.), s. 1-7 ISBN 1-4020-4994-3. [European Conference on Computational Mechanics /3./. Lisabon (PT), 05.06.2006-08.06.2006] R&D Projects: GA ČR(CZ) GP101/03/D153; GA ČR(CZ) GA101/06/0914; GA ČR(CZ) GA101/06/0213; GA AV ČR(CZ) 1ET400760509 Institutional research plan: CEZ:AV0Z20760514 Keywords : contact-impact * Gaus point search * explicit dynamics Subject RIV: JJ - Other Materials
A Scheduling Algorithm for Cloud Computing System Based on the Driver of Dynamic Essential Path
Xie, Zhiqiang; Shao, Xia; Xin, Yu
2016-01-01
To solve the problem of task scheduling in the cloud computing system, this paper proposes a scheduling algorithm for cloud computing based on the driver of dynamic essential path (DDEP). This algorithm applies a predecessor-task layer priority strategy to solve the problem of constraint relations among task nodes. The strategy assigns different priority values to every task node based on the scheduling order of task node as affected by the constraint relations among task nodes, and the task node list is generated by the different priority value. To address the scheduling order problem in which task nodes have the same priority value, the dynamic essential long path strategy is proposed. This strategy computes the dynamic essential path of the pre-scheduling task nodes based on the actual computation cost and communication cost of task node in the scheduling process. The task node that has the longest dynamic essential path is scheduled first as the completion time of task graph is indirectly influenced by the finishing time of task nodes in the longest dynamic essential path. Finally, we demonstrate the proposed algorithm via simulation experiments using Matlab tools. The experimental results indicate that the proposed algorithm can effectively reduce the task Makespan in most cases and meet a high quality performance objective. PMID:27490901
Algorithm to illustrate context using dynamic lighting effects
John, Roshy M.; Balasubramanian, T.
2007-09-01
With the invention of Ultra-Bright LED, solid state lighting has come to something which is much more efficient and energy saving when compared to conventional incandescent or fluorescent lighting. With the use of proper driver electronics now a days it is possible to install solid state lighting systems with the cost same as that of any other lighting technology. This paper is a part of the research project we are doing in our lab, which deals with using ultra bright LEDs of different colors for lighting applications. The driver electronics are made in such a way that, the color and brightness of the lights will change according to context. For instance, if one of the users is reading a story or listening to music in a Personal Computer or in a hand held device such as a PDA, the lighting systems and the HVAC (Heating Ventilation Air-conditioning) systems will change dramatically according to the content of the story or the music. The vulnerability of solid-state lighting helps to accomplish such an effect. Such a type of system will help the reader to feel the story mentally and physically as well. We developed complete driver electronics for the system using multiple microcomputers and a full software suite which uses complex algorithms to decode the context from text or music and synchronize it to lighting and HVAC information. The paper also presents some case-study statistics which shows the advantage of using the system to teach kindergarten children, deaf and dumb children and for language learning classes.
Lu, Jianfeng
2016-01-01
In the spirit of the fewest switches surface hopping, the frozen Gaussian approximation with surface hopping (FGA-SH) method samples a path integral representation of the non-adiabatic dynamics in the semiclassical regime. An improved sampling scheme is developed in this work for FGA-SH based on birth and death branching processes. The algorithm is validated for the standard test examples of non-adiabatic dynamics.
Probabilistic Dynamic Deployment of Wireless Sensor Networks by Artificial Bee Colony Algorithm
Dervis Karaboga; Celal Ozturk; Beyza Gorkemli
2011-01-01
As the usage and development of wireless sensor networks are increasing, the problems related to these networks are being realized. Dynamic deployment is one of the main topics that directly affect the performance of the wireless sensor networks. In this paper, the artificial bee colony algorithm is applied to the dynamic deployment of stationary and mobile sensor networks to achieve better performance by trying to increase the coverage area of the network. A probabilistic detection model is ...
Chaudhuri, Arindam
2013-01-01
We present a dynamic algorithm for solving the Longest Common Subsequence Problem using Ant Colony Optimization Technique. The Ant Colony Optimization Technique has been applied to solve many problems in Optimization Theory, Machine Learning and Telecommunication Networks etc. In particular, application of this theory in NP-Hard Problems has a remarkable significance. Given two strings, the traditional technique for finding Longest Common Subsequence is based on Dynamic Programming which cons...
Rogério M. Branco; Antônio S. Coelho; Sérgio F. Mayerle
2016-01-01
This paper discusses the application of heuristic-based evolutionary technique in search for solutions concerning the dynamic job-shop scheduling problems with dependent setup times and alternate routes. With a combinatorial nature, these problems belong to an NP-hard class, with an aggravated condition when in realistic, dynamic and therefore, more complex cases than the traditional static ones. The proposed genetic algorithm executes two important functions: choose the routes using dispatch...
Community Detection in Dynamic Social Networks Based on Multiobjective Immune Algorithm
Mao-Guo Gong; Ling-Jun Zhang; Jing-Jing Ma; Li-Cheng Jiao
2012-01-01
Community structure is one of the most important properties in social networks,and community detection has received an enormous amount of attention in recent years.In dynamic networks,the communities may evolve over time so that pose more challenging tasks than in static ones.Community detection in dynamic networks is a problem which can naturally be formulated with two contradictory objectives and consequently be solved by multiobjective optimization algorithms.In this paper,a novel multiobjective immune algorithm is proposed to solve the community detection problem in dynamic networks.It employs the framework of nondominated neighbor immune algorithm to simultaneously optimize the modularity and normalized mutual information,which quantitatively measure the quality of the community partitions and temporal cost,respectively.The problem-specific knowledge is incorporated in genetic operators and local search to improve the effectiveness and efficiency of our method.Experimental studies based on four synthetic datasets and two real-world social networks demonstrate that our algorithm can not only find community structure and capture community evolution more accurately but also be more steadily than the state-of-the-art algorithms.
SUN Fan; DU Wenli; QI Rongbin; QIAN Feng; ZHONG Weimin
2013-01-01
The solutions of dynamic optimization problems are usually very difficult due to their highly nonlinear and multidimensional nature.Genetic algorithm(GA)has been proved to be a feasible method when the gradient is difficult to calculate.Its advantage is that the control profiles at all time stages are optimized simultaneously,but its convergence is very slow in the later period of evolution and it is easily trapped in the local optimum.In this study,a hybrid improved genetic algorithm(HIGA)for solving dynamic optimization problems is proposed to overcome these defects.Simplex method(SM)is used to perform the local search in the neighborhood of the optimal solution.By using SM,the ideal searching direction of global optimal solution could be found as soon as possible and the convergence speed of the algorithm is improved.The hybrid algorithm presents some improvements,such as protecting the best individual,accepting immigrations,as well as employing adaptive crossover and Gaussian mutation operators.The efficiency of the proposed algorithm is demonstrated by solving several dynamic optimization problems.At last,HIGA is applied to the optimal production of secreted protein in a fed batch reactor and the optimal feed-rate found by HIGA is effective and relatively stable.
A parallel dynamic programming algorithm for multi-reservoir system optimization
Li, Xiang; Wei, Jiahua; Li, Tiejian; Wang, Guangqian; Yeh, William W.-G.
2014-05-01
This paper develops a parallel dynamic programming algorithm to optimize the joint operation of a multi-reservoir system. First, a multi-dimensional dynamic programming (DP) model is formulated for a multi-reservoir system. Second, the DP algorithm is parallelized using a peer-to-peer parallel paradigm. The parallelization is based on the distributed memory architecture and the message passing interface (MPI) protocol. We consider both the distributed computing and distributed computer memory in the parallelization. The parallel paradigm aims at reducing the computation time as well as alleviating the computer memory requirement associated with running a multi-dimensional DP model. Next, we test the parallel DP algorithm on the classic, benchmark four-reservoir problem on a high-performance computing (HPC) system with up to 350 cores. Results indicate that the parallel DP algorithm exhibits good performance in parallel efficiency; the parallel DP algorithm is scalable and will not be restricted by the number of cores. Finally, the parallel DP algorithm is applied to a real-world, five-reservoir system in China. The results demonstrate the parallel efficiency and practical utility of the proposed methodology.
Supercomputer algorithms for reactivity, dynamics and kinetics of small molecules
Even for small systems, the accurate characterization of reactive processes is so demanding of computer resources as to suggest the use of supercomputers having vector and parallel facilities. The full advantages of vector and parallel architectures can sometimes be obtained by simply modifying existing programs, vectorizing the manipulation of vectors and matrices, and requiring the parallel execution of independent tasks. More often, however, a significant time saving can be obtained only when the computer code undergoes a deeper restructuring, requiring a change in the computational strategy or, more radically, the adoption of a different theoretical treatment. This book discusses supercomputer strategies based upon act and approximate methods aimed at calculating the electronic structure and the reactive properties of small systems. The book shows how, in recent years, intense design activity has led to the ability to calculate accurate electronic structures for reactive systems, exact and high-level approximations to three-dimensional reactive dynamics, and to efficient directive and declaratory software for the modelling of complex systems
Dynamic Programming and Genetic Algorithm for Business Processes Optimisation
Mateusz Wibig
2012-12-01
Full Text Available There are many business process modelling techniques, which allow to capture features of those processes, but graphical, diagrammatic models seems to be used most in companies and organizations. Although the modelling notations are more and more mature and can be used not only to visualise the process idea but also to implement it in the workflow solution and although modern software allows us to gather a lot of data for analysis purposes, there is still not much commercial used business process optimisation methods. In this paper the scheduling / optimisation method for automatic task scheduling in business processes models is described. The Petri Net model is used, but it can be easily applied to any other modelling notation, where the process is presented as a set of tasks, i.e. BPMN (Business Process Modelling Notation. The method uses Petri Nets’, business processes’ scalability and dynamic programming concept to reduce the necessary computations, by revising only those parts of the model, to which the change was applied.
A Dynamic Traffic Signal Timing Model and its Algorithm for Junction of Urban Road
Cai, Yanguang; Cai, Hao
2012-01-01
As an important part of Intelligent Transportation System, the scientific traffic signal timing of junction can improve the efficiency of urban transport. This paper presents a novel dynamic traffic signal timing model. According to the characteristics of the model, hybrid chaotic quantum......-time and dynamic signal control of junction. To obtain the optimal solution of the model by hybrid chaotic quantum evolutionary algorithm, the model is converted to an easily solvable form. To simplify calculation, we give the expression of the partial derivative and change rate of the objective function...... such that the implementation of the algorithm only involves function assignments and arithmetic operations and thus avoids complex operations such as integral and differential. Simulation results show that the algorithm has less remain vehicles than Webster method, higher convergence rate and...
Two-stage evolutionary algorithm for dynamic multicast routing in mesh network
Li ZHU; Zhi-shu LI; Liang-yin CHEN; Yan-hong CHENG
2008-01-01
In order to share multimedia transmissions in mesh networks and optimize the utilization of network resources, this paper presents a Two-stage Evolutionary Algorithm (TEA), i.e., unicast routing evolution and multicast path composition, for dynamic multicast routing. The TEA uses a novel link-duplicate-degree encoding, which can encode a multicast path in the link-duplicate-degree and decode the path as a link vector easily. A dynamic algorithm for adding nodes to or removing nodes from a multicast group and a repairing algorithm are also covered in this paper. As the TEA is based on global evaluation, the quality of the multicast path remains stabilized without degradation when multicast members change over time. Therefore, it is not necessary to rearrange the multicast path during the life cycle of the multicast sessions. Simulation results show that the TEA is efficient and convergent.
DOUBLE FOUR-BAR CRANK-SLIDER MECHANISM DYNAMIC BALANCING BY META-HEURISTIC ALGORITHMS
Habib Emdadi
2013-09-01
Full Text Available In this paper, a new method for dynamic balancing of double four-bar crank slider mechanism by metaheuristic-based optimization algorithms is proposed. For this purpose, a proper objective function which is necessary for balancing of this mechanism and corresponding constraints has been obtained by dynamic modeling of the mechanism. Then PSO, ABC, BGA and HGAPSO algorithms have been applied for minimizing the defined cost function in optimization step. The optimization results have been studied completely by extracting the cost function, fitness, convergence speed and runtime values of applied algorithms. It has been shown that PSO and ABC are more efficient than BGA and HGAPSO in terms of convergence speed and result quality. Also, a laboratory scale experimental doublefour-bar crank-slider mechanism was provided for validating the proposed balancing method practically.
Double Four-Bar Crank-Slider Mechanism Dynamic Balancing by Meta-Heuristic Algorithms
Habib Emdadi
2013-09-01
Full Text Available In this paper, a new method for dynamic balancing of double four-bar crank slider mechanism by meta-heuristic-based optimization algorithms is proposed. For this purpose, a proper objective function which is necessary for balancing of this mechanism and corresponding constraints has been obtained by dynamic modeling of the mechanism.Then PSO, ABC, BGA and HGAPSO algorithms have been applied for minimizing the defined cost function in optimization step. The optimization results have been studied completely by extracting the cost function, fitness, convergence speed and run time values of applied algorithms. It has been shown that PSO and ABC are more efficient than BGA and HGAPSO in terms of convergence speed and result quality. Also, a laboratory scale experimental double four-bar crank-slider mechanism was provided for validating the proposedbalancing method practicall
Meng, Lexuan; Dragicevic, Tomislav; Guerrero, Josep M.;
2014-01-01
. Accordingly, this paper proposes a dynamic consensus algorithm based distributed optimization method aiming at improving the system efficiency while offering higher expandability and flexibility when compared to centralized control. Hardware-in-the-loop (HIL) results are shown to demonstrate the effectiveness...
Larentzos, J.P.; Brennan, J.K.; Moore, J.D.; Lísal, Martin; Mattson, w.D.
2014-01-01
Roč. 185, č. 7 (2014), s. 1987-1998. ISSN 0010-4655 Grant ostatní: ARL(US) W911NF-10-2-0039 Institutional support: RVO:67985858 Keywords : dissipative particle dynamics * shardlow splitting algorithm * numerical integration Subject RIV: CF - Physical ; Theoretical Chemistry Impact factor: 3.112, year: 2014
Novotny, M.A.
2010-02-01
The efficiency of dynamic Monte Carlo algorithms for off-lattice systems composed of particles is studied for the case of a single impurity particle. The theoretical efficiencies of the rejection-free method and of the Monte Carlo with Absorbing Markov Chains method are given. Simulation results are presented to confirm the theoretical efficiencies. © 2010.
An Optimal Algorithm towards Successive Location Privacy in Sensor Networks with Dynamic Programming
Zhao, Baokang; Wang, Dan; Shao, Zili; Cao, Jiannong; Chan, Keith C. C.; Su, Jinshu
In wireless sensor networks, preserving location privacy under successive inference attacks is extremely critical. Although this problem is NP-complete in general cases, we propose a dynamic programming based algorithm and prove it is optimal in special cases where the correlation only exists between p immediate adjacent observations.
An anomalous behavior of Gear's predictor-corrector algorithm is observed during a microcanonical molecular dynamics simulation of amorphous silicon. While the amorphous silicon network generated by quenching was being annealed, an anomaly is started: both the total energy and the temperature of the system spontaneously and indefinitely increased, in violation of energy conservation. No such phenomenon was observed when the integrator was switched to the Verlet algorithm. To remove this anomaly with the Gear's algorithm, it was necessary to reduce the size of the time step to 1/32 of the 'usual value (∼ 1/100 of the fastest vibration period in the system)' at least. Our results show that Gear's algorithm may become very unstable in a realistic simulation when the time step is not small enough, that is, orders of magnitude smaller than conventional value.
Genetic Algorithm-based Dynamic Vehicle Route Search using Car-to-Car Communication
KIM, J.
2010-11-01
Full Text Available Suggesting more efficient driving routes generate benefits not only for individuals by saving commute time, but also for society as a whole by reducing accident rates and social costs by lessening traffic congestion. In this paper, we suggest a new route search algorithm based on a genetic algorithm which is more easily installable into mutually communicating car navigation systems, and validate its usefulness through experiments reflecting real-world situations. The proposed algorithm is capable of searching alternative routes dynamically in unexpected events of system malfunctioning or traffic slow-downs due to accidents. Experimental results demonstrate that our algorithm searches the best route more efficiently and evolves with universal adaptability.
A simple and accurate algorithm for path integral molecular dynamics with the Langevin thermostat
Liu, Jian; Li, Dezhang; Liu, Xinzijian
2016-07-01
We introduce a novel simple algorithm for thermostatting path integral molecular dynamics (PIMD) with the Langevin equation. The staging transformation of path integral beads is employed for demonstration. The optimum friction coefficients for the staging modes in the free particle limit are used for all systems. In comparison to the path integral Langevin equation thermostat, the new algorithm exploits a different order of splitting for the phase space propagator associated to the Langevin equation. While the error analysis is made for both algorithms, they are also employed in the PIMD simulations of three realistic systems (the H2O molecule, liquid para-hydrogen, and liquid water) for comparison. It is shown that the new thermostat increases the time interval of PIMD by a factor of 4-6 or more for achieving the same accuracy. In addition, the supplementary material shows the error analysis made for the algorithms when the normal-mode transformation of path integral beads is used.
Chaowei Wang
2013-01-01
Full Text Available This paper proposes an advanced dynamic framed-slotted ALOHA algorithm based on Bayesian estimation and probability response (BE-PDFSA to improve the performance of radio frequency identification (RFID system. The Bayesian estimation is introduced to improve the accuracy of the estimation algorithm for lacking a large number of observations in one query. The probability response is used to adjust responsive probability of the unrecognized tags to make the responsive tag number equal to the frame length. In this way, we can solve the problem of high collision rate with the increase of tag number and improve the throughput of the whole system. From the simulation results, we can see that the algorithm we proposed can greatly improve the stability of RFID system compared with DFSA and other commonly used algorithms.
New dynamic routing algorithm based on MANET in LEO/MEO satellite network
LI Zhe; LI Dong-ni; WANG Guang-xing
2006-01-01
The features of low earth orbit/medium earth orbit (LEO/MEO) satellite networks routing algorithm based on inter-satellite link are analyzed and the similarities between satellite networks and mobile Ad Hoc network (MANET) are pointed out.The similar parts in MANET routing protocol are used in the satellite network for reference.A new dynamic routing algorithm based on MANET in LEO/MEO satellite networks,which fits for the LEO/MEO satellite communication system,is proposed.At the same time,the model of the algorithm is simulated and features are analyzed.It is shown that the algorithm has strong adaptability.It can give the network high autonomy,perfect function,low system overhead and great compatibility.
Prasanth, Ravi K.; Klein, Vladislav; Murphy, Patrick C.; Mehra, Raman K.
2005-01-01
This paper describes model structures and parameter estimation algorithms suitable for the identification of unsteady aerodynamic models from input-output data. The model structures presented are state space models and include linear time-invariant (LTI) models and linear parameter-varying (LPV) models. They cover a wide range of local and parameter dependent identification problems arising in unsteady aerodynamics and nonlinear flight dynamics. We present a residue algorithm for estimating model parameters from data. The algorithm can incorporate apriori information and is described in detail. The algorithms are evaluated on the F-16XL wind-tunnel test data from NAS Langley Research Center. Results of numerical evaluation are presented. The paper concludes with a discussion major issues and directions for future work.
A fast algorithm for parallel computation of multibody dynamics on MIMD parallel architectures
Fijany, Amir; Kwan, Gregory; Bagherzadeh, Nader
1993-01-01
In this paper the implementation of a parallel O(LogN) algorithm for computation of rigid multibody dynamics on a Hypercube MIMD parallel architecture is presented. To our knowledge, this is the first algorithm that achieves the time lower bound of O(LogN) by using an optimal number of O(N) processors. However, in addition to its theoretical significance, the algorithm is also highly efficient for practical implementation on commercially available MIMD parallel architectures due to its highly coarse grain size and simple communication and synchronization requirements. We present a multilevel parallel computation strategy for implementation of the algorithm on a Hypercube. This strategy allows the exploitation of parallelism at several computational levels as well as maximum overlapping of computation and communication to increase the performance of parallel computation.
Advanced time integration algorithms for dislocation dynamics simulations of work hardening
Sills, Ryan B.; Aghaei, Amin; Cai, Wei
2016-05-01
Efficient time integration is a necessity for dislocation dynamics simulations of work hardening to achieve experimentally relevant strains. In this work, an efficient time integration scheme using a high order explicit method with time step subcycling and a newly-developed collision detection algorithm are evaluated. First, time integrator performance is examined for an annihilating Frank–Read source, showing the effects of dislocation line collision. The integrator with subcycling is found to significantly out-perform other integration schemes. The performance of the time integration and collision detection algorithms is then tested in a work hardening simulation. The new algorithms show a 100-fold speed-up relative to traditional schemes. Subcycling is shown to improve efficiency significantly while maintaining an accurate solution, and the new collision algorithm allows an arbitrarily large time step size without missing collisions.
Dynamic Crypto Algorithm for Real-Time Applications DCA-RTA, Key Shifting
Ahmad H. Al-Omari
2016-01-01
Full Text Available The need for fast and attack resistance crypto algorithm is challenging issue in the era of the revolution in the information and communication technologies. The previous works presented by the authors “Dynamic Crypto Algorithm for Real-Time Applications DCA_RTA”, still need more enhancements to bring up the DCA_RTA into acceptable security level. In this work, the author added more enhancements on the Transformation-Table that is generated by the Initial-Table IT, which affects the overall encryption/decryption process. The new TT generation proven to be less correlated with the IT than using the previous TT generation processes. The simulated result indicates more randomness in the TT, which means better attack resistance algorithm. More room for algorithm enhancements is still needed.
Rong, Aiying; Hakonen, Henri; Lahdelma, Risto
2008-01-01
introduce in this paper the DP-RSC1 algorithm, which is a variant of the dynamic programming (DP) algorithm based on linear relaxation of the ON/OFF states of the units and sequential commitment of units one by one. The time complexity of DP-RSC1 is proportional to the number of generating units...... in the system, the number of periods over the planning horizon and the time for solving a single-period economic dispatch problem. We have compared the DP-RSC1 algorithm with realistic power plants against the unit decommitment algorithm and the traditional priority listing method. The results show that the DP......-RSC1 algorithm gives somewhat more accurate results (0.08-0.5% on average, maximum 10% for the individual sub-case) and executes 3-5 times faster on average than the unit decommitment algorithm. It is not surprising that the solution quality of the DP-RSC1 algorithm is much better than...
Pluchino, Alessandro; Latora, Vito
2008-01-01
We have recently introduced an efficient method for the detection and identification of modules in complex networks, based on the de-synchronization properties (dynamical clustering) of phase oscillators. In this paper we apply the dynamical clustering tecnique to the identification of communities of marine organisms living in the Chesapeake Bay food web. We show that our algorithm is able to perform a very reliable classification of the real communities existing in this ecosystem by using different kinds of dynamical oscillators. We compare also our results with those of other methods for the detection of community structures in complex networks.
Lin, Yuan; Samei, Ehsan
2016-07-01
Dynamic perfusion imaging can provide the morphologic details of the scanned organs as well as the dynamic information of blood perfusion. However, due to the polyenergetic property of the x-ray spectra, beam hardening effect results in undesirable artifacts and inaccurate CT values. To address this problem, this study proposes a segmentation-free polyenergetic dynamic perfusion imaging algorithm (pDP) to provide superior perfusion imaging. Dynamic perfusion usually is composed of two phases, i.e., a precontrast phase and a postcontrast phase. In the precontrast phase, the attenuation properties of diverse base materials (e.g., in a thorax perfusion exam, base materials can include lung, fat, breast, soft tissue, bone, and metal implants) can be incorporated to reconstruct artifact-free precontrast images. If patient motions are negligible or can be corrected by registration, the precontrast images can then be employed as a priori information to derive linearized iodine projections from the postcontrast images. With the linearized iodine projections, iodine perfusion maps can be reconstructed directly without the influence of various influential factors, such as iodine location, patient size, x-ray spectrum, and background tissue type. A series of simulations were conducted on a dynamic iodine calibration phantom and a dynamic anthropomorphic thorax phantom to validate the proposed algorithm. The simulations with the dynamic iodine calibration phantom showed that the proposed algorithm could effectively eliminate the beam hardening effect and enable quantitative iodine map reconstruction across various influential factors. The error range of the iodine concentration factors ([Formula: see text]) was reduced from [Formula: see text] for filtered back-projection (FBP) to [Formula: see text] for pDP. The quantitative results of the simulations with the dynamic anthropomorphic thorax phantom indicated that the maximum error of iodine concentrations can be reduced from
A dynamic material discrimination algorithm for dual MV energy X-ray digital radiography.
Li, Liang; Li, Ruizhe; Zhang, Siyuan; Zhao, Tiao; Chen, Zhiqiang
2016-08-01
Dual-energy X-ray radiography has become a well-established technique in medical, industrial, and security applications, because of its material or tissue discrimination capability. The main difficulty of this technique is dealing with the materials overlapping problem. When there are two or more materials along the X-ray beam path, its material discrimination performance will be affected. In order to solve this problem, a new dynamic material discrimination algorithm is proposed for dual-energy X-ray digital radiography, which can also be extended to multi-energy X-ray situations. The algorithm has three steps: α-curve-based pre-classification, decomposition of overlapped materials, and the final material recognition. The key of the algorithm is to establish a dual-energy radiograph database of both pure basis materials and pair combinations of them. After the pre-classification results, original dual-energy projections of overlapped materials can be dynamically decomposed into two sets of dual-energy radiographs of each pure material by the algorithm. Thus, more accurate discrimination results can be provided even with the existence of the overlapping problem. Both numerical and experimental results that prove the validity and effectiveness of the algorithm are presented. PMID:27239987
An Event-Driven Hybrid Molecular Dynamics and Direct Simulation Monte Carlo Algorithm
Donev, A; Garcia, A L; Alder, B J
2007-07-30
A novel algorithm is developed for the simulation of polymer chains suspended in a solvent. The polymers are represented as chains of hard spheres tethered by square wells and interact with the solvent particles with hard core potentials. The algorithm uses event-driven molecular dynamics (MD) for the simulation of the polymer chain and the interactions between the chain beads and the surrounding solvent particles. The interactions between the solvent particles themselves are not treated deterministically as in event-driven algorithms, rather, the momentum and energy exchange in the solvent is determined stochastically using the Direct Simulation Monte Carlo (DSMC) method. The coupling between the solvent and the solute is consistently represented at the particle level, however, unlike full MD simulations of both the solvent and the solute, the spatial structure of the solvent is ignored. The algorithm is described in detail and applied to the study of the dynamics of a polymer chain tethered to a hard wall subjected to uniform shear. The algorithm closely reproduces full MD simulations with two orders of magnitude greater efficiency. Results do not confirm the existence of periodic (cycling) motion of the polymer chain.
A Superstabilizing $\\log(n)$-Approximation Algorithm for Dynamic Steiner Trees
Blin, Lélia; Rovedakis, Stephane
2009-01-01
In this paper we design and prove correct a fully dynamic distributed algorithm for maintaining an approximate Steiner tree that connects via a minimum-weight spanning tree a subset of nodes of a network (referred as Steiner members or Steiner group) . Steiner trees are good candidates to efficiently implement communication primitives such as publish/subscribe or multicast, essential building blocks for the new emergent networks (e.g. P2P, sensor or adhoc networks). The cost of the solution returned by our algorithm is at most $\\log |S|$ times the cost of an optimal solution, where $S$ is the group of members. Our algorithm improves over existing solutions in several ways. First, it tolerates the dynamism of both the group members and the network. Next, our algorithm is self-stabilizing, that is, it copes with nodes memory corruption. Last but not least, our algorithm is \\emph{superstabilizing}. That is, while converging to a correct configuration (i.e., a Steiner tree) after a modification of the network, it...
Jingjing Ma
2014-01-01
Full Text Available Community structure is one of the most important properties in social networks. In dynamic networks, there are two conflicting criteria that need to be considered. One is the snapshot quality, which evaluates the quality of the community partitions at the current time step. The other is the temporal cost, which evaluates the difference between communities at different time steps. In this paper, we propose a decomposition-based multiobjective community detection algorithm to simultaneously optimize these two objectives to reveal community structure and its evolution in dynamic networks. It employs the framework of multiobjective evolutionary algorithm based on decomposition to simultaneously optimize the modularity and normalized mutual information, which quantitatively measure the quality of the community partitions and temporal cost, respectively. A local search strategy dealing with the problem-specific knowledge is incorporated to improve the effectiveness of the new algorithm. Experiments on computer-generated and real-world networks demonstrate that the proposed algorithm can not only find community structure and capture community evolution more accurately, but also be steadier than the two compared algorithms.
An adaptive mass algorithm for Car-Parrinello and Ehrenfest ab initio molecular dynamics
Kadir, Ashraful; Szepessy, Anders
2014-01-01
Ehrenfest and Car-Parrinello molecular dynamics are computational alternatives to approximate Born-Oppenheimer molecular dynamics without solving the electron eigenvalue problem at each time-step. A non-trivial issue is to choose the artificial electron mass parameter appearing in the Car-Parrinello method to achieve both good accuracy and high computational efficiency. In this paper, we propose an algorithm, motivated by the Landau-Zener probability, to systematically choose an artificial mass dynamically, which makes the Car-Parrinello and Ehrenfest molecular dynamics methods dependent only on the problem data. Numerical experiments for simple model problems show that the time-dependent adaptive artificial mass parameter improves the efficiency of the Car-Parrinello and Ehrenfest molecular dynamics.
A structural dynamic factor model for the effects of monetary policy estimated by the EM algorithm
Bork, Lasse
This paper applies the maximum likelihood based EM algorithm to a large-dimensional factor analysis of US monetary policy. Specifically, economy-wide effects of shocks to the US federal funds rate are estimated in a structural dynamic factor model in which 100+ US macroeconomic and financial time...... series are driven by the joint dynamics of the federal funds rate and a few correlated dynamic factors. This paper contains a number of methodological contributions to the existing literature on data-rich monetary policy analysis. Firstly, the identification scheme allows for correlated factor dynamics...... as opposed to the orthogonal factors resulting from the popular principal component approach to structural factor models. Correlated factors are economically more sensible and important for a richer monetary policy transmission mechanism. Secondly, I consider both static factor loadings as well as dynamic...
Dynamic learning rates algorithm for BPNN to forecast time series of dam security
无
2007-01-01
Time series data of dam security have a large number of observed values and should be forecasted accurately in time.Neural networks have the powerful approach ablilities of arbitrary functions and have been broadly utilized in many domains.In this paper,a dynamic learning rate training algorithm of hack-propagation neural networks for time series forecasting is proposed and the networks with this algorithm are built to forecast time series of dam security.The application results demonostrate the efficiency of modelling and the effictiveness of forecasting.
A Dynamic Framed Slotted ALOHA Algorithm Using Collision Factor for RFID Identification
Choi, Seung Sik; Kim, Sangkyung
In RFID systems, collision resolution is a significant issue in fast tag identification. This letter presents a dynamic frame-slotted ALOHA algorithm that uses a collision factor (DFSA-CF). This method enables fast tag identification by estimating the next frame size with the collision factor in the current frame. Simulation results show that the proposed method reduces slot times Required for RFID identification. When the number of tags is larger than the frame size, the efficiency of the proposed method is greater than those of conventional algorithms.
Extracting quantum dynamics from genetic learning algorithms through principal control analysis
Genetic learning algorithms are widely used to control ultrafast optical pulse shapes for photo-induced quantum control of atoms and molecules. An unresolved issue is how to use the solutions found by these algorithms to learn about the system's quantum dynamics. We propose a simple method based on covariance analysis of the control space, which can reveal the degrees of freedom in the effective control Hamiltonian. We have applied this technique to stimulated Raman scattering in liquid methanol. A simple model of two-mode stimulated Raman scattering is consistent with the results. (letter to the editor)
Sokoler, Leo Emil; Standardi, Laura; Edlund, Kristian;
2014-01-01
This paper presents a warm-started Dantzig–Wolfe decomposition algorithm tailored to economic model predictive control of dynamically decoupled subsystems. We formulate the constrained optimal control problem solved at each sampling instant as a linear program with state space constraints, input...... limits, input rate limits, and soft output limits. The objective function of the linear program is related directly to the cost of operating the subsystems, and the cost of violating the soft output constraints. Simulations for large-scale economic power dispatch problems show that the proposed algorithm...
Kumar, Rohit
2016-05-01
We discuss the stability of fragments identified by secondary algorithms used to construct fragments within quantum molecular dynamics model. For this purpose we employ three different algorithms for fragment identification. 1) The conventional minimum spanning tree (MST) method based on the spatial correlations, 2) an improved version of MST with additional binding energy constraints of cold nuclear matter, 3) and that of hot matter. We find significant role of thermal binding energies over cold matter binding energies. Significant role is observed for fragment multiplicities and stopping of fragments. Whereas insignificant effect is observed on fragment's flow.
Hou, Peng; Hu, Weihao; Chen, Zhe
2015-01-01
Anew approach, Dynamic Minimal Spanning Tree (DMST) algorithm, whichisbased on the MST algorithm isproposed in this paper to optimizethe cable connectionlayout for large scale offshore wind farm collection system. The current carrying capacity of the cable is considered as the main constraint....... Thedynamic changing of the cable capacity, therefore, the cost during the searching process is presented in this work. Twowind farms arechosen as the studied case and the final results showthat the proposed methodcan save the investment on cables 1.07% and 6.10% respectively compared with MST method...
Dynamic Crypto Algorithm for Real-Time Applications DCA-RTA, Key Shifting
Ahmad H. Al-Omari
2016-01-01
The need for fast and attack resistance crypto algorithm is challenging issue in the era of the revolution in the information and communication technologies. The previous works presented by the authors “Dynamic Crypto Algorithm for Real-Time Applications DCA_RTA”, still need more enhancements to bring up the DCA_RTA into acceptable security level. In this work, the author added more enhancements on the Transformation-Table that is generated by the Initial-Table IT, which affects the overall e...
Dynamic Routing Algorithm Based on the Channel Quality Control for Farmland Sensor Networks
Dongfeng Xu
2014-04-01
Full Text Available This article reports a Dynamic Routing Algorithm for Farmland Sensor Networks (DRA-FSN based on channel quality control to improve energy efficiency, which combines the distance and communication characteristics of farmland wireless sensor network. The functional architecture of the DRA-FSN algorithm, routing establish the mechanisms, the communication transmission mechanism, the global routing beacon return mechanism, abnormal node handling mechanism and sensor networks timing control mechanisms were designed in detail in this article. This article also evaluates and simulated the performance of DRA-FSN algorithm in different conditions from energy efficiency, packet energy consumption and packet distribution balance by comparing DRA-FSN algorithm with DSDV, EAP algorithm. Simulations showed that the DRA-FSN was more energy efficient than EAP and DSDV, the DRA-FSN algorithm overcame the shortcoming that capacity and bandwidth of the routing table correspondingly increase as more and more nodes joining the network. It has better performance in scalability and network loading balance
The purpose of this study was to develop a leaf-setting algorithm for Dynamic Multileaf Collimator-Intensity-Modulated Radiation Therapy (DMLC-IMRT) for optimal marker visibility. Here, a leaf-setting algorithm (called a Delta algorithm) was developed with the objective of maximizing marker visibility so as to improve the tracking effectiveness of fiducial markers during treatment delivery. The initial leaf trajectories were generated using a typical leaf-setting algorithm, then the leaf trajectories were adjusted by Delta algorithm operations (analytical computations and a series of matrix calculations) to achieve the optimal solution. The performance of the Delta algorithm was evaluated with six test fields (with randomly generated intensity profiles) and 15 clinical fields from IMRT plans of three prostate cancer patients. Compared with the initial solution, the Delta algorithm kept the total delivered intensities (TDIs) constant (without increasing the beam delivery time), but improved marker visibility (the percentage ratio of marker visibility time to beam delivery time). For the artificial fields (with three markers), marker visibility increased from 68.00-72.00% for a small field (5 x 5), from 38.46-43.59% for a medium field (10 x 10), and from 28.57-37.14% for a large field (20 x 20). For the 15 clinical fields, marker visibility increased 6-30% for eight fields and > 50% for two fields but did not change for five fields. A Delta algorithm was proposed to maximize marker visibility for DMLC-IMRT without increasing beam delivery time, and this will provide theoretical fundamentals for future studies of 4D DMLC tracking radiotherapy. (author)
A Dynamic Programming Algorithm for Optimal Design of Tidal Power Plants
Nag, B.
2013-03-01
A dynamic programming algorithm is proposed and demonstrated on a test case to determine the optimum operating schedule of a barrage tidal power plant to maximize the energy generation over a tidal cycle. Since consecutive sets of high and low tides can be predicted accurately for any tidal power plant site, this algorithm can be used to calculate the annual energy generation for different technical configurations of the plant. Thus an optimal choice of a tidal power plant design can be made from amongst different design configurations yielding the least cost of energy generation. Since this algorithm determines the optimal time of operation of sluice gate opening and turbine gates opening to maximize energy generation over a tidal cycle, it can also be used to obtain the annual schedule of operation of a tidal power plant and the minute-to-minute energy generation, for dissemination amongst power distribution utilities.
Dynamic economic dispatch (DED) problem is one of the optimization issues in power system operation. In this paper, an improved chaotic particle swarm optimization (ICPSO) algorithm is proposed to solve DED with value-point effects. In proposed ICPSO, chaotic mutation is embedded to overcome the drawback of premature in PSO. What's more, enhanced heuristic strategies are proposed to handling the various constraints of DED problem effectively. Comparing with penalty function method, the proposed constraints handling method can guide the population to feasible region without violating any constraints. Moreover, the effects of two crucial parameters on the performance of proposed ICPSO are also studied. Finally, the ICPSO algorithm is validated for two test systems consisting of 10 and extended 30 generators. While compared with other method reported in this literature, the experimental results demonstrated the high feasibility and effectiveness of proposed algorithm.
Noon, Abbas; Kadry, Seifedine
2011-01-01
Round Robin, considered as the most widely adopted CPU scheduling algorithm, undergoes severe problems directly related to quantum size. If time quantum chosen is too large, the response time of the processes is considered too high. On the other hand, if this quantum is too small, it increases the overhead of the CPU. In this paper, we propose a new algorithm, called AN, based on a new approach called dynamic-time-quantum; the idea of this approach is to make the operating systems adjusts the time quantum according to the burst time of the set of waiting processes in the ready queue. Based on the simulations and experiments, we show that the new proposed algorithm solves the fixed time quantum problem and increases the performance of Round Robin.
Diamantidis A. C.
2004-01-01
Full Text Available In this study, the buffer allocation problem (BAP in homogeneous, asymptotically reliable serial production lines is considered. A known aggregation method, given by Lim, Meerkov, and Top (1990, for the performance evaluation (i.e., estimation of throughput of this type of production lines when the buffer allocation is known, is used as an evaluative method in conjunction with a newly developed dynamic programming (DP algorithm for the BAP. The proposed algorithm is applied to production lines where the number of machines is varying from four up to a hundred machines. The proposed algorithm is fast because it reduces the volume of computations by rejecting allocations that do not lead to maximization of the line's throughput. Numerical results are also given for large production lines.
Phase based stiffness tuning algorithm for a magnetorheological elastomer dynamic vibration absorber
This paper presents a phase based stiffness tuning algorithm to overcome the uncertainty of the relation between the magnetic current and the natural frequency for magnetorheological elastomer (MRE) dynamic vibration absorbers (DVA) caused by the nonlinearity of the MRE. The phase difference of the relative acceleration of the DVA mass and the absolute acceleration of the primary system was used to check whether the natural frequency of the DVA is adjusted to the excitation frequency. The magnetic current was controlled by the phase difference, which made the proposed algorithm not rely on the model of the MRE DVA. Both the simulation and the experiment demonstrate that the proposed algorithm is efficient for MRE DVA in rapidly tracking the excitation frequency. (paper)
Weeks, Cindy Lou
1986-01-01
Experiments were conducted at NASA Ames Research Center to define multi-tasking software requirements for multiple-instruction, multiple-data stream (MIMD) computer architectures. The focus was on specifying solutions for algorithms in the field of computational fluid dynamics (CFD). The program objectives were to allow researchers to produce usable parallel application software as soon as possible after acquiring MIMD computer equipment, to provide researchers with an easy-to-learn and easy-to-use parallel software language which could be implemented on several different MIMD machines, and to enable researchers to list preferred design specifications for future MIMD computer architectures. Analysis of CFD algorithms indicated that extensions of an existing programming language, adaptable to new computer architectures, provided the best solution to meeting program objectives. The CoFORTRAN Language was written in response to these objectives and to provide researchers a means to experiment with parallel software solutions to CFD algorithms on machines with parallel architectures.
Kodali, Anuradha
In this thesis, we develop dynamic multiple fault diagnosis (DMFD) algorithms to diagnose faults that are sporadic and coupled. Firstly, we formulate a coupled factorial hidden Markov model-based (CFHMM) framework to diagnose dependent faults occurring over time (dynamic case). Here, we implement a mixed memory Markov coupling model to determine the most likely sequence of (dependent) fault states, the one that best explains the observed test outcomes over time. An iterative Gauss-Seidel coordinate ascent optimization method is proposed for solving the problem. A soft Viterbi algorithm is also implemented within the framework for decoding dependent fault states over time. We demonstrate the algorithm on simulated and real-world systems with coupled faults; the results show that this approach improves the correct isolation rate as compared to the formulation where independent fault states are assumed. Secondly, we formulate a generalization of set-covering, termed dynamic set-covering (DSC), which involves a series of coupled set-covering problems over time. The objective of the DSC problem is to infer the most probable time sequence of a parsimonious set of failure sources that explains the observed test outcomes over time. The DSC problem is NP-hard and intractable due to the fault-test dependency matrix that couples the failed tests and faults via the constraint matrix, and the temporal dependence of failure sources over time. Here, the DSC problem is motivated from the viewpoint of a dynamic multiple fault diagnosis problem, but it has wide applications in operations research, for e.g., facility location problem. Thus, we also formulated the DSC problem in the context of a dynamically evolving facility location problem. Here, a facility can be opened, closed, or can be temporarily unavailable at any time for a given requirement of demand points. These activities are associated with costs or penalties, viz., phase-in or phase-out for the opening or closing of a
Dynamic Routing Algorithm for Reliability and Energy Efficiency in Wireless Sensor Networks
Choi, Seong-Yong; Kim, Jin-Su; Han, Seung-Jin; Choi, Jun-Hyeog; Rim, Kee-Wook; Lee, Jung-Hyun
What are important in wireless sensor networks are energy efficiency, reliable data transmission, and topological adaptation to the change of external environment. This study proposes dynamic routing algorithm that satisfies the above-mentioned conditions at the same time using a dynamic single path in wireless sensor networks. In our proposed algorithm, each node transmits data through the optimal single path using hop count to the sink and node average energy according to the change of external environment. For reliable data transmission, each node monitors its own transmission process. If a node detects a damaged path, it switches from the damaged path to the optimal path and, by doing so, enhances network reliability. In case of a topological change, only the changed part is reconstructed instead of the whole network, and this enhances the energy efficiency of the network.
Tracking and Following Algorithms of Mobile Robots for Service Activities in Dynamic Environments
Feng-Li Lian
2015-02-01
Full Text Available By providing the capability of following a human target in an appropriate manner, the robot can assist people in various ways under different environments. One of the main difficulties when performing human tracking and following is the occlusion problem caused by static as well as dynamic obstacles. The aim of the paper is to tackle the occlusion problem by planning a robotic trajectory of maximizing target visibility and following the moving target. Initially, a laser range finder is used to detect the human target and then robustly track the target using the Kalman filter. Afterward, a human following algorithm based on a look-ahead algorithm, DWA*, is implemented to pursue the target while avoiding any static or dynamic obstacles. Fundamental experiments have been extensively tested to evaluate robot maneuvers and several field tests are conducted in more complex environments such as student cafeteria, computer center, and university library.
Thickness determination in textile material design: dynamic modeling and numerical algorithms
Textile material design is of paramount importance in the study of functional clothing design. It is therefore important to determine the dynamic heat and moisture transfer characteristics in the human body–clothing–environment system, which directly determine the heat–moisture comfort level of the human body. Based on a model of dynamic heat and moisture transfer with condensation in porous fabric at low temperature, this paper presents a new inverse problem of textile thickness determination (IPTTD). Adopting the idea of the least-squares method, we formulate the IPTTD into a function minimization problem. By means of the finite-difference method, quasi-solution method and direct search method for one-dimensional minimization problems, we construct iterative algorithms of the approximated solution for the IPTTD. Numerical simulation results validate the formulation of the IPTTD and demonstrate the effectiveness of the proposed numerical algorithms. (paper)
Spectrum of the Dirac operator and multigrid algorithm with dynamical staggered fermions
Complete spectra of the staggered Dirac operator D are determined in quenched four-dimensional SU(2) gauge fields, and also in the presence of dynamical fermions. Periodic as well as antiperiodic boundary conditions are used. An attempt is made to relate the performance of multigrid (MG) and conjugate gradient (CG) algorithms for propagators with the distribution of the eigenvalues of D. The convergence of the CG algorithm is determined only by the condition number k and by the lattice size. Since k's do not vary signigicantly when quarks become dynamic, CG convergence in unquenched fields can be predicted from quenched simulations. On the other hand, MG convergence is not affected by k but depends on the spectrum in a more subtle way. (orig.)
Study on algorithm of dynamic uncalibrated eye-in-hand visual servoing system
无
2007-01-01
Currently, most visual servoing system must be calibrated, while it is impossible to calibrate cameras and robot models precisely in industrial practice, so a novel dynamic uncalibrated eye-in-hand visual servoing system of tracking a moving target is proposed. The method does not require calibration of camera and robot kinematic models. Vision guided algorithm for tracking dynamic image is developed through minimizing nonlinear objective function. For the large residual has not been approximated in dynamic environment and the change of composite image Jacobian with time increment has not been computed in visual servoing system now,large residuals are dynamic approximated and the change of composite image Jacobian at each iterative step is computed.Simulation results demonstrate the validity of these approaches.
Analysis of Ant Colony Optimization and Population-Based Evolutionary Algorithms on Dynamic Problems
Lissovoi, Andrei
exist more complex oscillations that cannot be tracked with a polynomial-size colony. MMAS and (μ+1) EA on Maze We analyse the behaviour of a (μ + 1) EA with genotype diversity on a dynamic fitness function Maze, extended to a finite-alphabet search space. We prove that the (μ + 1) EA is able to track...... the dynamic optimum for finite alphabets up to size μ, while MMAS is able to do so for any finite alphabet size. Parallel Evolutionary Algorithms on Maze. We prove that while a (1 + λ) EA is unable to track the optimum of the dynamic fitness function Maze for offspring population size up to λ = O(n1-ε...... analysis showing how closely the EA can track the dynamically moving optimum over time. These results are also extended to a finite-alphabet search space....
Dynamic Fuzzy Controlled RWA Algorithm for IP/GMPLS over WDM Networks
I-Shyan Hwang; I-Feng Huang; Shin-Cheng Yu
2005-01-01
This paper proposes a dynamic RWA scheme using fuzzy logic control on IP/GMPLS over WDM networks to achieve the best quality of network transmission. The proposed algorithm dynamically allocates network resources and reserves partial bandwidth based on the current network status, which includes the request bandwidth, average utilization for each wavelength and its coefficient of variance (C.V.) of data traffic, to determine whether the connection can be set up. Five fuzzy sets for request bandwidth, average rate and C.V. of data traffic are used to divide the variable space: very large (LP), large (SP), normal (ZE), small (SN), and very small (LN). Setting the fuzzy limit is a key part in the proposed algorithm. The simulation of scenarios in this paper has two steps. In the first step, the adaptive fuzzy limits are evaluated based on average transmission cost pertaining to ten network statuses. The second step is to compare the proposed algorithm with periodic measurement of traffic (PMT) in ATM networks in six network situations to show that the proposed FC-RWA algorithm can provide better network transmission.
LIU Xiao; WANG Cheng-en
2005-01-01
This paper addresses a single item dynamic lot-sizing model with inventory capacity and out-sourcing. The goal is to minimize the total costs of production, setup, inventory holding and out-sourcing. Two versions of an out-sourcing model with time-varying costs are considered: stock out case and conservation case. Zero Inventory Order property has been found and some new properties are obtained in an optimal solution. Dynamic programming algorithms are developed to solve the problem in strongly polynomial time respectively. Furthermore, some numerical results demonstrate that the approach proposed is efficient and applicable.
Lelu, Alain; Cuxac, Pascal
2008-01-01
We address here two major challenges presented by dynamic data mining: 1) the stability challenge: we have implemented a rigorous incremental density-based clustering algorithm, independent from any initial conditions and ordering of the data-vectors stream, 2) the cognitive challenge: we have implemented a stringent selection process of association rules between clusters at time t-1 and time t for directly generating the main conclusions about the dynamics of a data-stream. We illustrate these points with an application to a two years and 2600 documents scientific information database.
LI Qiang; WU Jianxin; SUN Yan
2009-01-01
Dynamic optimization of electromechanical coupling system is a significant engineering problem in the field of mechatronics. The performance improvement of electromechanical equipment depends on the system design parameters. Aiming at the spindle unit of refitted machine tool for solid rocket, the vibration acceleration of tool is taken as objective function, and the electromechanical system design parameters are appointed as design variables. Dynamic optimization model is set up by adopting Lagrange-Maxwell equations, Park transform and electromechanical system energy equations. In the procedure of seeking high efficient optimization method, exponential function is adopted to be the weight function of particle swarm optimization algorithm. Exponential inertia weight particle swarm algorithm(EPSA), is formed and applied to solve the dynamic optimization problem of electromechanical system. The probability density function of EPSA is presented and used to perform convergence analysis. After calculation, the optimized design parameters of the spindle unit are obtained in limited time period. The vibration acceleration of the tool has been decreased greatly by the optimized design parameters. The research job in the paper reveals that the problem of dynamic optimization of electromechanical system can be solved by the method of combining system dynamic analysis with reformed swarm particle optimization. Such kind of method can be applied in the design of robots, NC machine, and other electromechanical equipments.
A Generalized Speckle Tracking Algorithm for Ultrasonic Strain Imaging Using Dynamic Programming
Jiang, Jingfeng; Hall, Timothy J.
2009-01-01
This study developed an improved motion estimation algorithm for ultrasonic strain imaging that employs a dynamic programming technique. In this paper, we model the motion estimation task as an optimization problem. Since tissue motion under external mechanical stimuli often should be reasonably continuous, a set of cost functions combining correlation and various levels of motion continuity constraint were used to regularize the motion estimation. To solve the optimization problem with a rea...
Nair, T. R. Gopalakrishnan; Sooda, Kavitha; Yashoda, M. B.
2011-01-01
In Internet Routing, the static shortest path (SP) problem has been addressed using well known intelligent optimization techniques like artificial neural networks, genetic algorithms (GAs) and particle swarm optimization. Advancement in wireless communication lead more and more mobile wireless networks, such as mobile networks [mobile ad hoc networks (MANETs)] and wireless sensor networks. Dynamic nature of the network is the main characteristic of MANET. Therefore, the SP routing problem in ...