A two-grid algorithm based on Newton iteration for the stream function form of the Navier-Stokes equations

In this paper, we propose a two-grid algorithm for solving the stream function formulation of the stationary Navies-Stokes equations. The algorithm is constructed by reducing the original system to one small, nonlinear system on the coarse mesh space and two similar linear systems （with same stiffness matrix but different right-hand side） on the fine mesh space. The convergence analysis and error estimation of the algorithm are given for the case of conforming elements. Furthermore, the Mgorithm produces a numerical solution with the optimal asymptotic H^2-error. Finally, we give a numerical illustration to demonstrate the effectiveness of the two-grid algorithm for solving the Navier-Stokes equations.

A MULTI-GRID ALGORITHM FOR MIXED PROBLEMS WITH PENALTY BY C°-PIECEWISE LINEAR ELEMENT APPROXIMATION

In this paper we describe a multi-grid algorithm for mixed problems with penalty by the linear finite element approximation. It is proved that the convergence rate of the algorithm is bound ed away from 1 independently of the meshsize. For convenience, we only discuss Jacobi relaxation as smoothing operator in detail.

A Polar Coordinate System Based Grid Algorithm for Star Identification

In Cartesian coordinate systems, the angular separation-based star identification algorithms involve much trigon- ometric function computing. That delays the algorithm process. As in a polar coordinate system, the coordinates are denoted by angular values, it is potential to speed up the star identification process by adopting a polar coordinate sys-tem. An angular polar coordinate system is introduced and a grid algorithm based on the coordinate system is proposed to enhance the performances of the star identification process. The simulations demonstrate that the algorithm in the angular polar coordinate system is superior to the grid algorithm in the rectangle Cartesian coordinate system in com-puting cost and identification rate. It can be used in the star sensors for high precision and high reliability in spacecraft navigation.

Multiple QoS modeling and algorithm in computational grid

Multiple QoS modeling and algorithm in grid system is considered. Grid QoS requirements can be formulated as a utility function for each task as a weighted sum of its each dimensional QoS utility functions. Multiple QoS constraint resource scheduling optimization in computational grid is distributed to two subproblems： optimization of grid user and grid resource provider. Grid QoS scheduling can be achieved by solving sub problems via an iterative algorithm.

An Algorithm for Extracting Contour Lines Based on Interval Tree from Grid DEM

这篇论文为决定等高线的起点建议一个新算法。新算法基于间隔树。结果显著地改进算法的效率。进一步，新策略被设计抑制穿的方向，产生轮廓忍受更有意义的信息。

Grid-Based Pseudo-Parallel Genetic Algorithm and Its Application

Aimed at the problems of premature and lower convergence of simple genetic algorithms （SGA）, three ideas --partition the whole search uniformly, multi-genetic operators and multi-populations evolving independently are introduced, and a grid-based pseudo-parallel genetic algorithms （GPPGA） is put forward. Thereafter, the analysis of premature and convergence of GPPGA is made. In the end, GPPGA is tested by both six-peak camel back function, Rosenbrock function and BP network. The result shows the feasibility and effectiveness of GPPGA in overcoming premature and improving convergence speed and accuracy.

An efficient algorithm for generating a spherical multiple-cell grid

This paper presents an efficient algorithm for generating a spherical multiple-cell(SMC)grid.The algorithm adopts a recursive loop structure and provides two refinement methods:(1)an arbitrary area refinement method and(2)a nearshore refinement method.Numerical experiments are carried out,and the results show that compared with the existing grid generation algorithm,this algorithm is more flexible and operable.

Sensitivity Encoding Reconstruction for MRI with Gridding Algorithm

The Sensitivity Encoding (SENSE) parallel reconstruction scheme for magnetic resonance imaging (MRI) is studied and implemented with gridding algorithm in this paper. In this paper, the sensitivity map profile, field map information and the spiral k-space data collected from an array of receiver coils are used to reconstruct un-aliased images from under-sampled data. The gridding algorithm is implemented with SENSE due to its ability in evaluating forward and adjoins operators with non-Cartesian sampled data. This paper also analyzes the performance of SENSE with real data set and identifies the computational issues that need to be improved for further research.

Optimum Tactics of Parallel Multi-Grid Algorithm with Virtual Boundary Forecast Method Running on a Local Network with the PVM Platform

In this paper, an optimum tactic of multi-grid parallel algorithmwith virtual boundary forecast method is disscussed, and a two-stage implementationis presented. The numerical results of solving a non-linear heat transfer equationshow that the optimum implementation is much better than the non-optimum one.

Harmonic Suppression Method Based on Immune Particle Swarm Optimization Algorithm in Micro-Grid

Distributed generation has attracted great attention in recent years, thanks to the progress in new-generation technologies and advanced power electronics. And micro-grid can make full use of distributed generation, so it has been widespread concern. On the other hand due to the extensive use of power electronic devices and many of the loads within micro-grid are nonlinear in nature, Micro-grid generate a large number of harmonics, so harmonics pollution needs to be addressed. Usually we use passive filter to filter out harmonic, in this paper, we propose a new method to optimize the filter parameters, so passive filter can filter out harmonic better. This method utilizes immune particle swarm optimization algorithm to optimize filter parameters. It can be shown from the simulation results that the proposed method is effective for micro-grid voltage harmonics compensation.

Genetic Algorithm-Based Redundancy Optimization Method for Smart Grid Communication Network

This paper proposes a redundancy optimization method for smart grid Advanced Metering Infrastructure(AMI) to realize economy and reliability targets.AMI is a crucial part of the smart grid to measure,collect,and analyze data about energy usage and power quality from customer premises.From the communication perspective,the AMI consists of smart meters,Home Area Network(HAN) gateways and data concentrators;in particular,the redundancy optimization problem focus on deciding which data concentrator needs redundancy.In order to solve the problem,we first develop a quantitative analysis model for the network economic loss caused by the data concentrator failures.Then,we establish a complete redundancy optimization model,which comprehensively consider the factors of reliability and economy.Finally,an advanced redundancy deployment method based on genetic algorithm(GA) is developed to solve the proposed problem.The simulation results testify that the proposed redundancy optimization method is capable to build a reliable and economic smart grid communication network.

Grid-Based Path Planner Using Multivariant Optimization Algorithm

To solve the shortest path planning problems on grid-based map efficiently,a novel heuristic path planning approach based on an intelligent swarm optimization method called Multivariant Optimization Algorithm( MOA) and a modified indirect encoding scheme are proposed. In MOA,the solution space is iteratively searched through global exploration and local exploitation by intelligent searching individuals,who are named as atoms. MOA is employed to locate the shortest path through iterations of global path planning and local path refinements in the proposed path planning approach. In each iteration,a group of global atoms are employed to perform the global path planning aiming at finding some candidate paths rapidly and then a group of local atoms are allotted to each candidate path for refinement. Further,the traditional indirect encoding scheme is modified to reduce the possibility of constructing an infeasible path from an array. Comparative experiments against two other frequently use intelligent optimization approaches: Genetic Algorithm( GA) and Particle Swarm Optimization( PSO) are conducted on benchmark test problems of varying complexity to evaluate the performance of MOA. The results demonstrate that MOA outperforms GA and PSO in terms of optimality indicated by the length of the located path.

A novel decomposition and coordination algorithm for complex networks and its application to power grids

To analyze and control complex networks effectively, this paper puts forward a new kind of scheme, which takes control separately in each area and can achieve the network’s coordinated optimality. The proposed algorithm is made up of two parts: the first part decomposes the network into several independent areas based on community structure and decouples the information flow and control power among areas; the second part selects the center nodes from each area with the help of the control centrality index. As long as the status of center nodes is kept on a satisfactory level in each area, the whole system is under effective control. Finally, the algorithm is applied to power grids, and the simulations prove its effectiveness.

A hybrid differential evolution algorithm for meta-task scheduling in grids

Task scheduling is one of the core steps to effectively exploit the capabilities of heterogeneous re-sources in the grid.This paper presents a new hybrid differential evolution(HDE)algorithm for findingan optimal or near-optimal schedule within reasonable time.The encoding scheme and the adaptation ofclassical differential evolution algorithm for dealing with discrete variables are discussed.A simple but ef-fective local search is incorporated into differential evolution to stress exploitation.The performance of theproposed HDE algorithm is showed by being compared with a genetic algorithm(GA)on a known staticbenchmark for the problem.Experimental results indicate that the proposed algorithm has better perfor-mance than GA in terms of both solution quality and computational time,and thus it can be used to de-sign efficient dynamic schedulers in batch mode for real grid systems.

Nearest neighbor search algorithm based on multiple background grids for fluid simulation

The core of smoothed particle hydrodynamics （SPH） is the nearest neighbor search subroutine. In this paper, a nearest neighbor search algorithm which is based on multiple background grids and support variable smooth length is introduced. Through tested on lid driven cavity flow, it is clear that this method can provide high accuracy. Analysis and experiments have been made on its parallelism, and the results show that this method has better parallelism and with adding processors its accuracy become higher, thus it achieves that efficiency grows in pace with accuracy.

PLC-Oriented Access Point Location Planning Algorithm in Smart-Grid Communication Networks

In this study,we investigate the optimal location of access points(APs) to connect end nodes with a service provider through power-line communication in smartgrid communication networks.APs are the gateways of power-distribution communication networks,connecting users to control centers.Hence,they are vital for the reliable,safe,and economical operation of a power system.This paper proposes a planning method for AP allocation that takes into consideration economics,reliability,network delay,and(n-1) resilience.First,an optimization model for the AP location is established,which minimizes the cost of installing APs,while satisfying the reliability,network delay,and(n-1) resilience constraints.Then,an improved genetic algorithm is proposed to solve the optimization problem.The simulation results indicate that the proposed planning method can deal with diverse network conditions satisfactorily.Furthermore,it can be applied effectively with high flexibility and scalability.

Impact of the Objective Function on the Construction of Internal Grids of Wind Farms Using Genetic Algorithm

The macro complex of the construction industry is energy intensive. Solutions that enable the supply of this demand while meeting the principles of sustainability are needed. The construction of wind farms has been a strategy employed by many countries to produce clean energy. An increase in the construction of wind farms has also been witnessed in Brazil. This calls for different activities, such as the design and construction of infrastructure. This article focuses on the design of internal medium voltage distribution grids for wind farms. The purpose is to find a radial configuration that connects a set of wind generators to the substation, in an optimum way, minimizing operational and construction costs, reducing loss and therefore contributing to sustainability. In large farms, the project design consists of a large combinatorial optimization problem, given the large number of possible configurations to be deployed. Finding the best solution for the internal grid depends on the criterion adopted for the objectives pursued. This article analyzes the different criteria that can be adopted in the design of the wind farm’s internal grid using a methodology based on genetic algorithm (GA). Its aim is to identify their influence on the solution of the problem and help decision-making by finding the most adequate criterion for the objectives pursued. The results show that the design of the internal grid is sensitive to the criteria adopted for the objective function. In addition, the degree of sensitivity is analyzed, showing that, in some cases, the solutions are not economically attractive and do not contribute to the reduction of losses.

An Improved Task Scheduling Algorithm in Grid Computing Environment

Algorithm research of task scheduling is one of the key techniques in grid computing. This paper firstly describes a DAG task scheduling model used in grid computing environment, secondly discusses generational scheduling (GS) and communication inclusion generational scheduling (CIGS) algorithms. Finally, an improved CIGS algorithm is proposed to use in grid computing environment, and it has been proved effectively.