Journal articles: 'Grid Optimization Method'

1

Miura, Hiroaki, and Masahide Kimoto. "A Comparison of Grid Quality of Optimized Spherical Hexagonal–Pentagonal Geodesic Grids." Monthly Weather Review 133, no. 10 (October 1, 2005): 2817–33. http://dx.doi.org/10.1175/mwr2991.1.

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Abstract Construction and optimization methods of spherical hexagonal–pentagonal geodesic grids are investigated. The objective is to compare grid structures on common ground. The distinction between two types of hexagonal–pentagonal grids is made. Three conventional grid optimization methods are summarized. In addition, three new optimization methods are proposed. Six desirable conditions for an ideal grid are described, and the grid optimization methods are organized in view of such conditions. Interval uniformity, area uniformity, isotropy, and bisection of cell faces are systematically investigated for optimized grids. There are compensations of preferable grid features in each optimization method, and an optimal method cannot be decided based only on the research of grid features. It is suggested that grid optimization methods should be selected based on research of numerical schemes.

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Li, Dong Liang, Xiao Feng Zhang, Ming Zhong Qiao, and Gang Cheng. "A Short-Term Load Forecasting Method of Warship Based on PSO-SVM Method." Applied Mechanics and Materials 127 (October 2011): 569–74. http://dx.doi.org/10.4028/www.scientific.net/amm.127.569.

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The power load characteristics of warship on a specific task was analyzed,and a task-based forecasting method for warship short-term load forecasting was presented. the new influencing factors of warship power load were used in modeling which is different with the land grid and civilian vessels grid. Theory of particle swarm optimization and Support vector machine was disscused first, and the method of particle swarm optimization was improved to have the ability of adaptive parameter optimization. and the method of support vector machine was improved by the adaptive PSO optimizational method. then a new adaptive short-term load forecasting model was established by the adaptive PSO-SVM method. finally Through simulation results show that the adaptive PSO-SVM method is highly feasible to predict with high accuracy and high generalization capability.

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3

Wang, Cheng. "Optimization of SVM Method with RBF Kernel." Applied Mechanics and Materials 496-500 (January 2014): 2306–10. http://dx.doi.org/10.4028/www.scientific.net/amm.496-500.2306.

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Usually there is no a uniform model to the choice of SVMs kernel function and its parameters for SVM. This paper presents a bilinear grid search method for the purpose of getting the parameter of SVM with RBF kernel, with the approach of combining grid search with bilinear search. Experiment results show that the proposed bilinear grid search has combined both the advantage of moderate training quantity by the bilinear search and of high predict accuracy by the grid search.

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Song, Ying Wei, Jian Liu, Liao Yi Ning, Zhen Tao Han, Hong Liu, and Shi Ju Wang. "Comprehensive Assessment System and Method of Smart Distribution Grid." Advanced Materials Research 860-863 (December 2013): 1901–8. http://dx.doi.org/10.4028/www.scientific.net/amr.860-863.1901.

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According to key values and demands of smart distribution grids in China, a smart distribution grid comprehensive assessment index system and method is established. The index system is divided into the demand index set and assessment index set. As the former represents the macroscopic features while the latter represents the microscopic factors of the smart distribution grid. A causal connection exists between them. Also a hierarchical optimization model with combination weight, which is based on the DEMATEL-ANP-anti-entropy weight method and the improved Grey incidence approach, is proposed. The DEMATEL-ANP-anti-entropy weight method is used for demand indices weight analysis, while the improved Grey incidence approach is put forward for the evaluation on assessment indices. Results of applying the proposed method in practical case shows that the proposed method is valuable in following aspects: scientifically assessing the intelligent development level and effectiveness of distribution grid, analyzing and recognizing the grids operation condition and weak link, giving reference to planning and construction of smart distribution grid, and so on. Index Terms Smart distribution grid; comprehensive assessment; index system; combination weight; hierarchical optimization.

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Ingber, Marc S., and Ambar K. Mitra. "Grid optimization for the boundary element method." International Journal for Numerical Methods in Engineering 23, no. 11 (November 1986): 2121–36. http://dx.doi.org/10.1002/nme.1620231110.

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Qian, Lin, Dong Hui Li, Xiao Zhi Wu, Guang Xin Zhu, and Jiang Hui Liu. "Performance Optimization Method on Smart Grid Information Platform." Advanced Materials Research 765-767 (September 2013): 1041–45. http://dx.doi.org/10.4028/www.scientific.net/amr.765-767.1041.

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In recent years, with the development of economics, Chinese government feels obliged to build a strong smart grid, state grid company starts the construction process of SG-ERP system comprehensively. However, system performance bottleneck gradually has been spotted due to expanding of IT systems. So it has important theoretical value and strong practical significance to do research on smart grid information platform. This paper proposed a framework of performance tuning for large transaction database used in smart grid based on state grid information platform construction, this method will break system performance bottlenecks, improving the service quality of the platform. The results show that the proposed method can greatly increase the quality of system running, and provide a reliable guarantee for the running in strong smart grid.

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Cui, Pengcheng, Bin Li, Jing Tang, Jiangtao Chen, and Youqi Deng. "A modified adjoint-based grid adaptation and error correction method for unstructured grid." Modern Physics Letters B 32, no. 12n13 (May 10, 2018): 1840020. http://dx.doi.org/10.1142/s0217984918400201.

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Grid adaptation is an important strategy to improve the accuracy of output functions (e.g. drag, lift, etc.) in computational fluid dynamics (CFD) analysis and design applications. This paper presents a modified robust grid adaptation and error correction method for reducing simulation errors in integral outputs. The procedure is based on discrete adjoint optimization theory in which the estimated global error of output functions can be directly related to the local residual error. According to this relationship, local residual error contribution can be used as an indicator in a grid adaptation strategy designed to generate refined grids for accurately estimating the output functions. This grid adaptation and error correction method is applied to subsonic and supersonic simulations around three-dimensional configurations. Numerical results demonstrate that the sensitive grids to output functions are detected and refined after grid adaptation, and the accuracy of output functions is obviously improved after error correction. The proposed grid adaptation and error correction method is shown to compare very favorably in terms of output accuracy and computational efficiency relative to the traditional featured-based grid adaptation.

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Zhao, Chunhui, Bin Fan, Jinwen Hu, Zhiyuan Zhang, and Quan Pan. "Matching Algorithm of Statistical Optimization Feature Based on Grid Method." Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University 37, no. 2 (April 2019): 249–57. http://dx.doi.org/10.1051/jnwpu/20193720249.

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The matching algorithm based on image feature points is widely used in image retrieval, target detection, identification and other image processing fields. Aiming at the problem that the feature matching algorithm has low recall rate, a statistical optimization feature based on grid of the normalized cross correlation function is proposed. The matching main direction difference and scale ratio are introduced to feature matching process, for comprehensively utilizing SIFT feature points' information, such as the main direction, scale and position constrains, to accelerate the solution of matching position constraint under the grid framework, which optimizes the feature matching results and improves the recall rate and comprehensive match performance. Firstly, the nearest neighbor matching feature points of each feature point in the original image are found in the target image, and the initial matching results are obtained. Secondly, the matching main direction difference is used to eliminate most mismatches of the initial matching. Thirdly, the matching images are meshed based on the matching scale ratio information, and the position information of the matching feature points distributed among the grids is gathered statistics. Finally, the normalized cross correlation function of each small grid in the original image is calculated to determine whether the matching in the small grid is correct, and the optimized feature matching results are obtained. The experimental results show that the matching accuracy of the new algorithm is similar to that of classical feature matching algorithms, meanwhile the matching recall rate is increased by more than 10%, and a better comprehensive matching performance is obtained.

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Li, Dong Liang, Xiao Feng Zhang, Ming Zhong Qiao, and Gang Cheng. "An Improved Short-Term Load Forecasting Method of Warship." Applied Mechanics and Materials 127 (October 2011): 575–81. http://dx.doi.org/10.4028/www.scientific.net/amm.127.575.

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The power load characteristics of warship on a specific task was analyzed,and a task-based forecasting method for warship short-term load forecasting was presented. the new influencing factors of warship power load were used in modeling which is different with the land grid and civilian vessels grid. Theory of particle swarm optimization and Support vector machine was disscused first, and the method of particle swarm optimization was improved to have the ability of adaptive parameter optimization. and the method of support vector machine was improved by the adaptive PSO optimizational method. then a new adaptive short-term load forecasting model was established by the adaptive PSO-SVM method. finally Through simulation results show that the adaptive PSO-SVM method is highly feasible to predict with high accuracy and high generalization capability.

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10

Dolinyuk, Stanislav, and Volodimir Bagenov. "Optimization of the grid configuration by the method of contour optimization." Bulletin of NTU "KhPI". Series: Problems of Electrical Machines and Apparatus Perfection. The Theory and Practice 4, no. 2 (December 22, 2020): 30–32. http://dx.doi.org/10.20998/2079-3944.2020.2.06.

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Deng, Chao, Yao Xiong, Yuan Hang Wang, and Jun Wu. "Machining Process Parameters Optimization Based on Grid Optimization Algorithm." Advanced Materials Research 562-564 (August 2012): 2021–25. http://dx.doi.org/10.4028/www.scientific.net/amr.562-564.2021.

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Machining process parameters directly affect the machining quality and efficiency of heavy-duty CNC machine tools, selecting correctly machining process parameters can improve the machine’s machining performance effectively. This paper presents a machining process parameters optimization method based on grid optimization algorithm for heavy-duty CNC machine tools. In this method, a multi-objective optimization model will be established, which considers not only the linear constrains of machining process parameters, such as machining time and machining cost, but also the non-linear constrain, such as chatter in machining process. Grid optimization algorithm will be adapted to search the optimal combination of machining process parameters from the multi-objective optimization model. In the end, this paper will present an example to verify superiority of the multi-objective optimization method by comparing with single-objective optimization method.

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12

Tuzikova, Valeriya, Josef Tlusty, and Zdenek Muller. "A Novel Power Losses Reduction Method Based on a Particle Swarm Optimization Algorithm Using STATCOM." Energies 11, no. 10 (October 22, 2018): 2851. http://dx.doi.org/10.3390/en11102851.

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In the modern electric power industry, Flexible AC Transmission Systems (FACTS) have a special place. In connection with the increased interest in the development of “smart energy”, the use of such devices is becoming especially urgent. Their main function is the ability to manage modes in real time: maintain the necessary level of voltage in the grids, control the power flow, increase the capacity of power lines and increase the static and dynamic stability of the power grid. The problem of system reliability and stability is related to the task of definitions and optimizations and planning indicators, design and exploitation. The main aim of this article is the definition of the best placement of the STATCOM compensator in case to provide stability and reliability of the grid with the minimization of the power losses, using Particle Swarm Optimization algorithms. All calculations were performed in MATLAB.

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13

Yun, Pingping, Yongfeng Ren, and Yu Xue. "Energy-Storage Optimization Strategy for Reducing Wind Power Fluctuation via Markov Prediction and PSO Method." Energies 11, no. 12 (December 4, 2018): 3393. http://dx.doi.org/10.3390/en11123393.

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Wind power penetration ratios of power grids have increased in recent years; thus, deteriorating power grid stability caused by wind power fluctuation has caused widespread concern. At present, configuring an energy storage system with corresponding capacity at the grid connection point of a large-scale wind farm is an effective solution that improves wind power dispatchability, suppresses potential fluctuations, and reduces power grid operation risks. Based on the traditional energy-storage battery dispatching scheme, in this study, a multi-objective hybrid optimization model for joint wind-farm and energy-storage operation is designed. The impact of two new aspects, the energy-storage battery output and wind-power future output, on the current energy storage operation are considered. Wind-power future output assessment is performed using a wind-power-based Markov prediction model. The particle swarm optimization algorithm is used to optimize the wind-storage grid-connected power in real time, to develop an optimal operation strategy for an energy storage battery. Simulations incorporating typical daily wind power data from a several-hundred-megawatt wind farm and rolling optimization of the energy storage output reveal that the proposed method can reduce the grid-connected wind power fluctuation, the probability of overcharge and over-discharge of the stored energy, and the energy storage dead time. For the same smoothing performance, the proposed method can reduce the energy storage capacity and improve the economic efficiency of the wind-storage joint operation.

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Yang, Yanhua, and Ligang Yao. "Optimization Method of Power Equipment Maintenance Plan Decision-Making Based on Deep Reinforcement Learning." Mathematical Problems in Engineering 2021 (March 15, 2021): 1–8. http://dx.doi.org/10.1155/2021/9372803.

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The safe and reliable operation of power grid equipment is the basis for ensuring the safe operation of the power system. At present, the traditional periodical maintenance has exposed the abuses such as deficient maintenance and excess maintenance. Based on a multiagent deep reinforcement learning decision-making optimization algorithm, a method for decision-making and optimization of power grid equipment maintenance plans is proposed. In this paper, an optimization model of power grid equipment maintenance plan that takes into account the reliability and economics of power grid operation is constructed with maintenance constraints and power grid safety constraints as its constraints. The deep distributed recurrent Q-networks multiagent deep reinforcement learning is adopted to solve the optimization model. The deep distributed recurrent Q-networks multiagent deep reinforcement learning uses the high-dimensional feature extraction capabilities of deep learning and decision-making capabilities of reinforcement learning to solve the multiobjective decision-making problem of power grid maintenance planning. Through case analysis, the comparative results show that the proposed algorithm has better optimization and decision-making ability, as well as lower maintenance cost. Accordingly, the algorithm can realize the optimal decision of power grid equipment maintenance plan. The expected value of power shortage and maintenance cost obtained by the proposed method is $71.75$ $MW·H$ and $496000$ $yuan$.

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15

Farooqi, Muhammad Nufail, Daulet Izbassarov, Metin Muradoğlu, and Didem Unat. "Communication analysis and optimization of 3D front tracking method for multiphase flow simulations." International Journal of High Performance Computing Applications 33, no. 1 (March 15, 2017): 67–80. http://dx.doi.org/10.1177/1094342017694426.

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This paper presents a scalable parallelization of an Eulerian–Lagrangian method, namely the three-dimensional front tracking method, for simulating multiphase flows. Operating on Eulerian–Lagrangian grids makes the front tracking method challenging to parallelize and optimize because different types of communication (Lagrangian–Eulerian, Eulerian–Eulerian, and Lagrangian–Lagrangian) should be managed. In this work, we optimize the data movement in both the Eulerian and Lagrangian grids and propose two different strategies for handling the Lagrangian grid shared by multiple subdomains. Moreover, we model three different types of communication emerged as a result of parallelization and implement various latency-hiding optimizations to reduce the communication overhead. Good scalability of the parallelization strategies is demonstrated on two supercomputers. A strong scaling study using 256 cores simulating 1728 interfaces or bubbles achieves 32.5x speedup. We also conduct weak scaling study on 4096 cores simulating 27,648 bubbles on a 1024×1024×2048 Eulerian grid resolution.

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Permal, Navinesshani, Miszaina Osman, Azrul Mohd Ariffin, Navaamsini Boopalan, and Mohd Zainal Abidin Ab Kadir. "Optimization of substation grounding grid design for horizontal and vertical multilayer and uniform soil condition using Simulated Annealing method." PLOS ONE 16, no. 9 (September 7, 2021): e0256298. http://dx.doi.org/10.1371/journal.pone.0256298.

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Grounding systems are critical in safeguarding people and equipment from power system failures. A grounding system’s principal goal is to offer the lowest impedance path for undesired fault current. Optimization of the grounding grid designs is important in satisfying the minimum cost of the grounding system and safeguarding those people who work in the surrounding area of the grounded installations. Currently, there is no systematic guidance or standard for grounding grid designs that include two-layer soil and its effects on grounding grid systems, particularly vertically layered soil. Furthermore, while numerous studies have been conducted on optimization, relatively limited study has been done on the problem of optimizing the grounding grid in two-layer soil, particularly in vertical soil structures. This paper presents the results of optimization for substation grounding systems using the Simulated Annealing (SA) algorithm in different soil conditions which conforms to the safety requirements of the grounding system. Practical features of grounding grids in various soil conditions discussed in this paper (uniform soil, two-layer horizontal soil, and two-layer vertical soil) are considered during problem formulation and solution algorithm. The proposed algorithm’s results show that the number of grid conductors in the X and Y directions (Nx and Ny), as well as vertical rods (Nr), can be optimized from initial numbers of 35% for uniform soil, 57% for horizontal two-layer soil for ρ1> ρ2, and 33% for horizontal two-layer soil for ρ1< ρ2, and 29% for vertical two-layer soil structure. In other words, the proposed technique would be able to utilize square and rectangle-shaped grounding grids with a number of grid conductors and vertical rods to be implemented in uniform, two-layer horizontal and vertical soil structure, depending on the resistivity of the soil layer.

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Lee, Minhyung, Gwanyong Park, Hyangin Jang, and Changmin Kim. "Development of Building CFD Model Design Process Based on BIM." Applied Sciences 11, no. 3 (January 29, 2021): 1252. http://dx.doi.org/10.3390/app11031252.

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This paper proposes the design process of optimized building Computational Fluid Dynamics (CFD) model based on Building Information Modelling (BIM). The proposed method consists of five-step processes: BIM data extraction, geometry simplification, grid optimization, attribute data matching, and finally, exporting a CFD case folder for OpenFOAM. Validation is performed to evaluate the improvement of the grid model and the accuracy of the simulation result. Validation is conducted for four indoor ventilation models. The number of grids increased or decreased, according to the optimization method, but did not change significantly. On the other hand, the maximum non-orthogonality improved by up to 20.78%, according to the optimization function. This proves that it is sufficiently effective in improving the grid quality. The accuracy of the proposed method is evaluated by relative error rate with the ANSYS simulation result. The error rates for flow and temperature are evaluated. The relative error rate is less than 5% under all conditions. Therefore, the accuracy of the proposed method is verified.

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Liao, Li, and Chengjun Ji. "Smart Grid Dispatching Optimization for System Resilience Improvement." Complexity 2020 (November 6, 2020): 1–12. http://dx.doi.org/10.1155/2020/8884279.

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A large number of modern communication technologies and sensing technologies are incorporated into the smart grid, which makes its structure unique. The centralized optimized dispatch method of traditional power grids is difficult to achieve effective dispatch of smart grids. Based on the analysis of power generation plan and maintenance plan optimization model, this paper establishes a smart grid power generation and maintenance collaborative optimization model with distributed renewable energy. The objective function of this collaborative optimization problem is the operating cost of conventional units, the cost of wind power generation, and the cost of overhauling units; the constraints considered mainly include system constraints and overhaul constraints. The solution method of combinatorial optimization is analyzed, and the genetic optimization algorithm adopted in this paper is selected and discussed. According to the characteristics of the system, various loads are modeled, and power supply constraints are considered. By establishing an effective objective function, the adjustable load scheduling problem is transformed into a solvable optimal control problem. Taking into account the uncertain factors in the system, the advantage of the real-time control system is that it can realize the dynamic update scheduling of the load, so it is more in line with the requirements of the actual system. The real-time algorithm proposed in the paper is based on a distributed control strategy, which can not only realize dynamic compensation for random fluctuations in renewable energy power generation but also satisfy the load curve optimization under the premise of making full use of power supply resources. In addition, simulation experiments compare the load dispatching capabilities of the proposed algorithm with the existing algorithms, thereby verifying the performance of the proposed method.

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Nie, Liang, Xu Jiang, Jian Wu, and Rong Li Guo. "Optimization Method of Wavefront Reconstruction Based on Spatial Light Modulator." Advanced Materials Research 601 (December 2012): 209–15. http://dx.doi.org/10.4028/www.scientific.net/amr.601.209.

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To measure the aspheric surface, the reconstruction of the reference wavefront based on spatial light modulator (SLM) is studied in this paper. Computer-generated hologram (CGH) interference encoding method has been selected and the hologram is been loaded into the SLM to reconstruct standard wavefront. The principles of wavefront reconstruction and the experimental system are introduced at first. Considering the effect of the SLM grid structure on wavefront accuracy, the optimization methods are proposed. With the use of 4f spatial filtering system, the multiple diffraction patterns due to the effect of the SLM grid structure can be removed. The reconstruction wavefront based on dislocation superposition method is used to reduce the impact of black grid. The analysis result shows that the quality of reconstruction wavefront has been improved effectively through the optimization methods.

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Fang, Xian Mei. "A Heuristic Resource Management Method in Grid Computing Environment." Applied Mechanics and Materials 50-51 (February 2011): 521–25. http://dx.doi.org/10.4028/www.scientific.net/amm.50-51.521.

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Grid is an emerging infrastructure which enables effective coordinate access to various distributed computing resources in order to serve the needs of collaborative research and work across the world. Grid resource management is always a key subject in the grid computing. We first analyze the resource management in the grid computing environment, then according to the load imbalance question in the ant colony optimization algorithm, propose an improved algorithm that suits to be used in the grid environment.

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Zhang, Yuxin, Zhijin Guan, Longyong Ji, Qin Fang Luan, and Yizhen Wang. "A method of mapping and nearest neighbor optimization for 2-D quantum circuits." Quantum Information and Computation 20, no. 3&4 (March 2020): 194–212. http://dx.doi.org/10.26421/qic20.3-4-2.

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In some practical quantum physical architectures, the qubits need to be distributed on 2-dimensional (2-D) grid structure to implement quantum computation. In order to map an 1-dimensional (1-D) quantum circuit into a 2-D grid structure and satisfy the nearest neighbor constraint of qubit interaction in the grid structure, a mapping method from 1-D quantum circuit to 2-D grid structure is proposed in this paper. This method firstly determines the order of placing qubits, and then presents the layout strategy of qubits in 2-D grid. We also proposed an algorithm for establishing interaction paths between non-adjacent qubits in 2-D grid structure, which can satisfy the physical constraints of the interaction of quantum bits in the grid in the process of mapping an 1-D quantum circuit to a 2-D grid structure. For some benchmark circuits, after using the method of this paper to place qubits, it is possible to make every 2-qubit gate in the circuit have a nearest neighbor, so that there is no need to use SWAP gate to establish channel routing. Compared with the latest available methods, the average optimization rate is 82.38%.

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Liu, Shanshan, Xiaoqiu Wang, Yueli Feng, Xianlu Cai, Pengyin Yan, and Binwang Li. "Efficient Visualization Method and Implementation of Reservoir Model Based on WPF." Mathematical Problems in Engineering 2021 (June 10, 2021): 1–17. http://dx.doi.org/10.1155/2021/5581282.

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In view of the slow speed and poor effect of real-time rendering of large-scale reservoir geological grid model, a new grid model hidden algorithm is proposed by analyzing the Eclipse reservoir grid model storage format, grid model representation, and cell sorting rule, which optimizes the original grid data and improves the rendering speed of reservoir grid model. The algorithm in this paper eliminates hidden points and faces according to the topological relationship of the grid, and finally, only the visible point and face data are extracted as the final visual input data. Through the realization of 3D visualization software of reservoir geological model and well trajectory, the correctness and efficiency of the hidden algorithm are verified. In the software, firstly, the number of display grids is effectively reduced by preprocessing, and the 3D graphics technology of WPF and helix is adopted to realize the high-efficiency display of reservoir grids. The comparison test of different scale reservoir models shows that the method can reduce the point and surface data by more than 85% and shows that the speed optimization effect is significant. The 3D display function realizes the interactive functions such as roaming, zooming, and viewpoint switching of the reservoir model, truly reveals again the geological environment and borehole information of underground drilling, which is helpful for drilling interpretation and decision-making, provides a reasonable drilling tracking geological target drilling scheme for the drilling process, and realizes the seamless connection of geological engineering integration.

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Zhong, Xian-Ci, Jia-Ye Chen, and Zhou-Yang Fan. "A Particle Swarm Optimization-Based Method for Numerically Solving Ordinary Differential Equations." Mathematical Problems in Engineering 2019 (December 12, 2019): 1–11. http://dx.doi.org/10.1155/2019/9071236.

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The Euler method is a typical one for numerically solving initial value problems of ordinary differential equations. Particle swarm optimization (PSO) is an efficient algorithm for obtaining the optimal solution of a nonlinear optimization problem. In this study, a PSO-based Euler-type method is proposed to solve the initial value problem of ordinary differential equations. In the typical Euler method, the equidistant grid points are always used to obtain the approximate solution. The existing shortcoming is that when the iteration number is increasing, the approximate solution could be greatly away from the exact one. Here, it is considered that the distribution of the grid nodes could affect the approximate solution of differential equations on the discrete points. The adopted grid points are assumed to be free and nonequidistant. An optimization problem is constructed and solved by particle swarm optimization (PSO) to determine the distribution of grid points. Through numerical computations, some comparisons are offered to reveal that the proposed method has great advantages and can overcome the existing shortcoming of the typical Euler formulae.

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Zeiselmair, Andreas, and Simon Köppl. "Constrained Optimization as the Allocation Method in Local Flexibility Markets." Energies 14, no. 13 (June 30, 2021): 3932. http://dx.doi.org/10.3390/en14133932.

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Local flexibility markets or smart markets are new tools used to harness regional flexibility for congestion management. In order to benefit from the available flexibility potential for grid-oriented or even grid-supportive applications, complex but efficient and transparent allocation is necessary. This paper proposes a constrained optimization method for matching the flexibility demand of grid operators to the flexibility supply using decentralized flexibility options located in the distribution grid. Starting with a definition of the operational and stakeholder environment of smart market design, various existing approaches are analyzed based on a literature review and a resulting meta-analysis. In the next step, a categorization of the allocation method is conducted followed by the definition of the optimization goal. The optimization problem, including all relevant input parameters, is identified and formulated by introducing the relevant boundary conditions and constraints of flexibility demand and offers. A proof of concept of the approach is presented using a case study and the Altdorfer Flexmarkt (ALF) field test within the project C/sells. In this paper, we analyze the background of the local flexibility market, provide the methodology (including publishing the code of the matching mechanism), and provide the results of the field test.

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Dong, Runnan, Shi Liu, and Geng Liang. "Research on Control Parameters for Voltage Source Inverter Output Controllers of Micro-Grids Based on the Fruit Fly Optimization Algorithm." Applied Sciences 9, no. 7 (March 29, 2019): 1327. http://dx.doi.org/10.3390/app9071327.

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Due to the strong intermittency of micro-resources, the poor grid-tied power quality, and the high generation-demand sensitivity in micro-grids, research into the control methods of micro-grid systems has always been a notable issue in the field of micro-grids. The inverter is the core control equipment at the primary control level of the micro-grid, and the key factors affecting its output performance can be divided into three categories: control methods, hardware configuration, and control parameter design. Taking the classical active and reactive power (P-Q) control structure and the three-phase, two-stage inverter topology model as an example, this paper designs a parameter for offline tuning, and an online self-tuning optimization method for an inverter control system based on the fruit fly optimization algorithm (FOA). By simulating and comparing the inverter controllers with non-optimized parameters in the same object and environment, the designed parameter tuning method is verified. Specifically, it improves the dynamic response speed of the inverter controller, reduces the steady-state error and oscillation, and enhances the dynamic response performance of the controller.

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Wu, Yong Yi, and Shuang Hu Wang. "The Study of Hierarchical Control Method for Micro-Grid Control." Applied Mechanics and Materials 260-261 (December 2012): 482–86. http://dx.doi.org/10.4028/www.scientific.net/amm.260-261.482.

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The micro-grid structure and operation mode are analyzed in this paper. By listing micro-grid typical operation mode, and explains the energy management system, micro-grid controller, electric primary equipment is how to coordinate between control. Analysis of the usual method for the energy management system optimization control strategy will be passed to the micro-grid controller. A kind of brand-new control command transmission mode is put forward for improving the micro-grid control strategy by the efficiency.

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Yang, Yu De, and Yu Sheng Qiu. "A Method of Automatic Voltage Optimization Control Based on Real Time Digital Simulation." Applied Mechanics and Materials 336-338 (July 2013): 653–58. http://dx.doi.org/10.4028/www.scientific.net/amm.336-338.653.

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With the development of smart grid, regional grid automatic voltage control (AVC) system has been widely used in power systems, but the effect is uneven, and there are not relating tools to evaluate its performance. The paper proposes a Closed-loop test method combining Real Time Digital Simulation system (RTDS) with reactive optimization procedures to simulate and optimization the status of actual grid. An optimal control scheme of the actual grid from the new method can be used to judge the advantages and disadvantages of actual AVC system. Simulation tests show online RTDS-based power system reactive power and voltage control simulation is good. It can be regard as reference to evaluate the control effectiveness of the actual AVC system.

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Osmani, Amjad, Abolfazl Toroghi Haghighat, and Shirin Khezri. "Location Update Improvement Using Fuzzy Logic Optimization in Location Based Routing Protocols in MANET." International Journal of Grid and High Performance Computing 3, no. 3 (July 2011): 1–19. http://dx.doi.org/10.4018/jghpc.2011070101.

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Several position-based routing protocols have been developed for mobile ad hoc networks. Many of these protocols assume that a location service is available which provides location information on the nodes in the network. This paper introduces a new schema in management of mobile nodes location in mobile ad hoc networks. Fuzzy logic optimization is applied to a better management of location update operation in hierarchical location services. Update management overhead is decreased without significant loss of query success probability. One-hop-chain-technique is used for Auto compensation. A new composed method can update mobile nodes location when the nodes cross a grid boundary. The proposed method uses a dynamic grid area that ?solves the ping-pong problem between grids. Simulation results show that these methods are effective. The algorithms are distributed and can keep scalability in the scenario of increasing nodes density?. The described solutions are not limited to a special network grid ordering, and can be used in every hierarchical ordering like GLS if the ordering can be mappable on these methods.

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Luzin, V. "Optimization of Texture Measurements—Part I: Method: Optimal Grid Parameter." Textures and Microstructures 33, no. 1-4 (January 1, 1999): 343–55. http://dx.doi.org/10.1155/tsm.33.343.

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In texture experiments one always measures a sample with some constrained number of grains N (see the discussion in Bunge (1996). Proc. of Workshop Math. Methods of Texture Analysis, Textures and Microstructures 25, 71–108). It is clear that the orientation distribution function (ODF) and pole figures (PFs) measured for this limited N may differ from actual ones. How well do texture measurements reproduce the actual distribution densities? The statistical relevance of such measurements is the main area of interest in the present paper.In this article the RP-value is adopted as the value quantitatively characterizing this relevance. From this point of view the problem of evaluation of true distribution densities means the minimization of the RP-value over some variables. For evaluation of the pole density (for some PF), we consider the parameter of the measurement grid as the variable of the minimization problem. The number of grains N and the sharpness of the texture are the additional parameters of the problem.Two approaches to solve the mentioned problem are proposed. One is the numerical simulation of the given distribution as the normal (Gaussian) distribution. The other is based on some estimation of the expected RP-value between the actual and experimental PFs.It turns out that for the given type of the measurement grid (an equidistant grid) the optimal measurement grid parameter exists. This is one that minimizes the RP-value in dependence on the number of grains N in the sample and sharpness of the texture.

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Zou, Hong Bo, Fei Wang, Lina Ke, and Ting Ting Yin. "Searching Method of Core Backbone Grid Based on Biogeography-Based Optimization Algorithm." Advanced Materials Research 1008-1009 (August 2014): 790–95. http://dx.doi.org/10.4028/www.scientific.net/amr.1008-1009.790.

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Constructing backbone grid is important means of carrying out differential planning to improve power system’s ability of resisting natural disasters. A searching method of core backbone grid with the target of the smallest total lines and nodes and the largest integrated survivability index based on the index system of survivability was put forward with constraint conditions of network connectivity and power grid’s safe operation. The biogeography-based optimization algorithm was introduced to search for the optimal core backbone grid. Compared with particle swarm optimization (PSO), binary ant colony algorithm (BACA), genetic algorithm (GA), the proposed method is accurate and effective, and it has the merits of better convergence speed and convergence precision.

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Xu, Shu Feng, Huai Fa Ma, and Yong Fa Zhou. "Moving Grid Method for Simulating Crack Propagation." Applied Mechanics and Materials 405-408 (September 2013): 3173–77. http://dx.doi.org/10.4028/www.scientific.net/amm.405-408.3173.

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A moving grid nonlinear finite element method was used in this study to simulate crack propagation. The relevant elements were split along the direction of principal stress within the element and thus automatic optimization processing of local mesh was realized. We discussed the moving grid nonlinear finite element algorithm was proposed, compiled the corresponding script files based on the dedicated finite element language of Finite Element Program Generator (FEPG), and generate finite element source code programs according to the script files. Analyses show that the proposed moving grid finite element method is effective and feasible in crack propagation simulation.

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Li, Hongzhong, Xinyu Zhang, Wenhua Han, Yingchuang Li, and Aimin Kang. "A power grid partitioning optimization method based on fractal theory." International Transactions on Electrical Energy Systems 29, no. 3 (October 16, 2018): e2741. http://dx.doi.org/10.1002/etep.2741.

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Butt, Muhammad Munir. "Multigrid Method for Optimal Control Problem Constrained by Stochastic Stokes Equations with Noise." Mathematics 9, no. 7 (March 29, 2021): 738. http://dx.doi.org/10.3390/math9070738.

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Optimal control problems governed by stochastic partial differential equations have become an important field in applied mathematics. In this article, we investigate one such important optimization problem, that is, the stochastic Stokes control problem with forcing term perturbed by noise. A multigrid scheme with three-factor coarsening to solve the corresponding discretized control problem is presented. On staggered grids, a three-factor coarsening strategy helps in simplifying the inter-grid transfer operators and reduction in computation (CPU time). For smoothing, a distributive Gauss–Seidel scheme with a line search strategy is employed. To validate the proposed multigrid staggered grid framework, numerical results are presented with white noise at the end.

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Lu, Peng, Mo Li, Xiang Zhang, Yibo Zou, and Shengmao Shu. "Multi-objective hydropower purchase optimization method for inter-provincial power grids under time-sharing electricity price." MATEC Web of Conferences 246 (2018): 01043. http://dx.doi.org/10.1051/matecconf/201824601043.

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The existing electric grid purchase ratio research focuses on the methods of various types of combinations of purchasing and selling business of electricity, and the methods of reasonable risk allocation between different businesses, First, it lacks the consideration of the sensitivity of customer requirements to electricity price changes . Second, it lacks the consideration of optimization of the revenue and risk of hydropower purchase in the provincial electric grid. Therefore, this paper establishes the multi-objective time-of-use power price model based on customer requirements for time-of-use power price response.It introduces a VaR-based risk assessment method. Also, it proposes a multi-objective optimization model that maximizes the expected revenue on electricity purchase and minimizes the risk of purchasing electricity. The electricity purchase ratio scheme and the electrictiy purchase risk scheme are jointly optimized to obtain the electric grid inter-provincial electrictiy purchase risk decision and the optimal electricity purchase ratio. The results show that the grid company will obtain greater economic benefits while avoiding risks as much as possible after using the power purchase optimization method of this paper.

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Shi, Zejing, Ninghui Zhu, and Jinsong Yu. "The Electric Vehicle Time-of-Use Price Optimization Model Considering the Demand Response." MATEC Web of Conferences 160 (2018): 02009. http://dx.doi.org/10.1051/matecconf/201816002009.

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A large number of electric vehicles connecting to the distribution grids usually introduce significant fluctuations to the grid and the loads. To solve the problem, guiding the users coordinated charging is proposed. Firstly, the uncontrolled charging power prediction models of electric vehicles are established, and the Monte Carlo method is adopted to simulate the power demands of different electric vehicles, and the influences on the load peak-valley ratios and the voltages and losses of the grid are all analyzed. Then the vehicle responses model considering the time-of-use price is analyzed, and the vehicle response ratios are obtained under different time-of-use prices. Finally the multi-objective optimization model is constructed including the minimum peak-valley ratio, maximum consumption satisfaction index and cost satisfaction index. In the procedure, vehicles and the grid are both taken into account. The results indicate the proposed method could guide the users coordinated charging, and the peak shaving and valley filling is also achieved, and the operation of the distribution grid is improved.

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Zhang, Shengfeng, Jie Zhao, Yixuan Weng, Dichen Liu, Weizhe Ma, and Yuhui Ma. "Construction Method of a Guaranteed Grid Considering the Specific Recovery Process." Sustainability 12, no. 9 (May 11, 2020): 3935. http://dx.doi.org/10.3390/su12093935.

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For the planning of the guaranteed power grid, only the operation capacity of the target grid is considered. The recovery process and steps of the backbone grid are not considered enough, which leads to two defects: the minimum guaranteed power grid is not conducive to the unit recovery and the recovery time is too long. In this paper, a method of constructing the grid with the specific recovery process is proposed. Considering the influence of the grid structure and the position of the black start power supply on the recovery steps, the recovery success rate and the recovery time of the grid, the optimization of the grid structure of the guaranteed grid can meet the demand of power supply and at the same time make the recovery of the target grid less time-consuming and achieve a higher recovery success rate in the event of a blackout. In this method, two aspects are considered: the power failure recovery scenario in the recovery process of the target grid and the normal power supply scenario, reflecting the power supply performance after the recovery of the target grid. In the normal power supply scenario, a three-objective optimization model including power supply capacity, smooth transmission and safety margin is constructed, with power supply capacity and safe operation as constraints. In the scenario of power failure recovery, the process of power grid recovery and the mechanism affecting the success of recovery are analyzed to form the line recovery index. The Dijkstra algorithm is used to search for the optimal recovery path and calculate the recovery index, so as to reoptimize the backbone power grid. The validity of the method is verified by standard and practical examples.

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Dancila, Bogdan D., and Ruxandra M. Botez. "Geographical area selection and construction of a corresponding routing grid used for in-flight management system flight trajectory optimization." Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering 231, no. 5 (April 13, 2016): 809–22. http://dx.doi.org/10.1177/0954410016643104.

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This paper proposes a new method for selecting an ellipse-shaped geographical area and constructing a routing grid that circumscribes the contour of the designated area. The resulting grid describes the set of points used by the flight trajectory optimization algorithms to determine an aircraft’s optimal flight trajectory as a function of given particular atmospheric conditions. This method was developed with the intent of its employment in the context of Flight Management System trajectory optimization algorithms, but can be used in Air Traffic Management environments as well. The routing grid limits the trajectory’s maximal total ground distance (between the departure and destination airports), maximizes the geographical area (for a better consideration of the wind conditions) and minimizes the number of grid nodes. The novelty of the proposed method resides in the fact that it allows a distinct and independent parameterization and control of the ellipse’s total surface, and the required size of the take-off/landing procedure maneuvering areas at the departure/destination airports. The ellipse contour constructed using this method is, therefore, well adapted to the particular configuration of the trajectory for which the optimization is performed. Each design variables’ influence is presented, as well as a set of routing grids generated for trajectories corresponding to different total flight distances, and were further compared with real flight trajectory data retrieved using the website Flight Aware.

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Wei, Wei, Li Jie Ding, and Yan Jiao Liu. "A Review of Regional Reactive Power Optimization Techniques." Advanced Materials Research 986-987 (July 2014): 1360–64. http://dx.doi.org/10.4028/www.scientific.net/amr.986-987.1360.

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With increasingly complex of the power grid structure and increasingly user requirements of power quality, Power grid voltage reactive power optimization is still the difficult points in power system operation control. This paper introduces the general optimization methods of multi-objective reactive power optimization, intelligent algorithm, the development of the hybrid method, and their respective advantages, disadvantages and improvement; It also analyzes and summarizes the simplification of search space for reactive power optimization and the key issues and research development trend of the real-time reactive voltage control system.

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Zhao, Qinghai, Xiaokai Chen, Zheng-Dong Ma, and Yi Lin. "Robust Topology Optimization Based on Stochastic Collocation Methods under Loading Uncertainties." Mathematical Problems in Engineering 2015 (2015): 1–14. http://dx.doi.org/10.1155/2015/580980.

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A robust topology optimization (RTO) approach with consideration of loading uncertainties is developed in this paper. The stochastic collocation method combined with full tensor product grid and Smolyak sparse grid transforms the robust formulation into a weighted multiple loading deterministic problem at the collocation points. The proposed approach is amenable to implementation in existing commercial topology optimization software package and thus feasible to practical engineering problems. Numerical examples of two- and three-dimensional topology optimization problems are provided to demonstrate the proposed RTO approach and its applications. The optimal topologies obtained from deterministic and robust topology optimization designs under tensor product grid and sparse grid with different levels are compared with one another to investigate the pros and cons of optimization algorithm on final topologies, and an extensive Monte Carlo simulation is also performed to verify the proposed approach.

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Raju, Leo, R. S. Milton, and S. Sakthiyanandan. "Energy Optimization of Solar Micro-Grid Using Multi Agent Reinforcement Learning." Applied Mechanics and Materials 787 (August 2015): 843–47. http://dx.doi.org/10.4028/www.scientific.net/amm.787.843.

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In this paper, two solar Photovoltaic (PV) systems are considered; one in the department with capacity of 100 kW and the other in the hostel with capacity of 200 kW. Each one has battery and load. The capital cost and energy savings by conventional methods are compared and it is proved that the energy dependency from grid is reduced in solar micro-grid element, operating in distributed environment. In the smart grid frame work, the grid energy consumption is further reduced by optimal scheduling of the battery, using Reinforcement Learning. Individual unit optimization is done by a model free reinforcement learning method, called Q-Learning and it is compared with distributed operations of solar micro-grid using a Multi Agent Reinforcement Learning method, called Joint Q-Learning. The energy planning is designed according to the prediction of solar PV energy production and observed load pattern of department and the hostel. A simulation model was developed using Python programming.

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Barillas, Francisco, and Jesús Fernández-Villaverde. "A generalization of the endogenous grid method." Journal of Economic Dynamics and Control 31, no. 8 (August 2007): 2698–712. http://dx.doi.org/10.1016/j.jedc.2006.08.005.

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Izvorski, Ivailo. "A nonuniform grid method for solving PDE’s." Journal of Economic Dynamics and Control 22, no. 8-9 (July 1998): 1445–52. http://dx.doi.org/10.1016/s0165-1889(98)00020-7.

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Liu, Hong Da, Li Zhang, Wen Hao Zhang, and Sheng Yue Qu. "Research on Optimization Design for Independent Island Micro-Grid." Advanced Materials Research 827 (October 2013): 292–97. http://dx.doi.org/10.4028/www.scientific.net/amr.827.292.

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nland grid cannot reach and cover the islands, and it is also difficult to complete the construction of the cross-sea grid, for these islands are usually far from the mainland. The research on the island micro-grid (solar/wind/tide/battery) is carried out based on the corresponding practical project on a island. The multi-object optimizing design method mainly based on the Deficiency of Power Supply Probability (DPSP) and the Levelised Unit Electricity Cost (LUEC) which represent the reliability of the power supply and the economical efficiency of the micro-grid system, respectively. The traversal algorithm is employed to obtain the capacity optimization configuration for the distributed generation units and the battery bank in the independent micro-grid. Meanwhile, a software designed for the capacity optimization configuration of the island hybrid system is presented. At last, the specific design for a independent island micro-grid is proposed, which validate the effeteness of the method presented in this paper.

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Tang, Yong Hong, Lin Xu, Hong Fan, and Yu De Yang. "A Method for Online Reactive Power Optimization Problem in Regional Power Grid." Applied Mechanics and Materials 347-350 (August 2013): 1515–19. http://dx.doi.org/10.4028/www.scientific.net/amm.347-350.1515.

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The automatic voltage control system in regional power grid is a significant measure to improve the system voltage and the power quality. It relies on online reactive power optimization. This paper proposes a novel algorithm aimed to build an online reactive power optimization model, which uses a hybrid algorithm combined the primal-dual interior point method and branch and bound method. Dealing with the discrete variables and continuous variables separately, the solution speed and accelerate convergence problem is improved rapidly. Finally, the efficiency of the proposed method is verified by numerical simulation in a regional power grid system.

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Ahmad Yasmin, Nur Sakinah, Norhaliza Abdul Wahab, and Aznah Nor Anuar. "Improved support vector machine using optimization techniques for an aerobic granular sludge." Bulletin of Electrical Engineering and Informatics 9, no. 5 (October 1, 2020): 1835–43. http://dx.doi.org/10.11591/eei.v9i5.2264.

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Aerobic granular sludge (AGS) is one of the treatment methods often used in wastewater systems. The dynamic behavior of AGS is complex and hard to predict especially when it comes to a limited data set. Theoretically, support vector machine (SVM) is a good prediction tool in handling limited data set. In this paper, an improved SVM using optimization approaches for better predictions is proposed. Two different types of optimization are built which are particle swarm optimization (PSO) and genetic algorithm (GA). The prediction of the models using SVM-PSO, SVM-GA and SVM-Grid Search are developed and compared prior to several feature analysis for verification purposes. The experimental data under hot temperature of 50˚C obtained from sequencing batch reactor is used. From simulation results, the proposed SVM with optimizations improve the prediction of chemical oxygen demand compared to the conventional grid search method and hence provide better prediction of effluent quality using AGS wastewater treatment systems.

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Wang, Zhi Dong, Zi Fa Liu, Jin Chao Li, Ziang You, Hui Li, and Zhi Yong Cao. "The Quantitative Evaluation of Grid Planning Based on Optimized Weighting Method." Applied Mechanics and Materials 278-280 (January 2013): 2252–60. http://dx.doi.org/10.4028/www.scientific.net/amm.278-280.2252.

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It is crucial for the integral future development of the power grid to measure grid planning rationality during the rapid growth period of it. Through the analysis of the evaluation characteristics and influencing factors of China's power grid planning, this paper determined the value orientation of the grid planning assessment and evaluation content based on a quantitative evaluation of the existing power grid planning model, and established comprehensive assessment index system of grid planning covering security, economic, excellence and coordination based on the safe and stable operation of the power grid, and proposed optimize weight assessment methods applicable to the assessment of the overall level of grid planning based on improved particle swarm optimization. Finally this paper provides the rationality and feasibility of the comprehensive evaluation index system and optimized weight assessment method through engineering empirical analysis.

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Wang, Yang, Tang, Sun, and Zhao. "A Stochastic-CVaR Optimization Model for CCHP Micro-Grid Operation with Consideration of Electricity Market, Wind Power Accommodation and Multiple Demand Response Programs." Energies 12, no. 20 (October 19, 2019): 3983. http://dx.doi.org/10.3390/en12203983.

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Combined cooling, heating and power (CCHP) micro-grids have the advantage of high energy efficiency, and can be integrated with renewable energies and demand response programs (DRPs). With the deepening of electricity market (EM) reforms, how to carry out operation optimization under EM circumstances will become a key problem for CCHP micro-grid development. This paper proposed a stochastic-CVaR (conditional value at risk) optimization model for CCHP micro-grid operation with consideration of EM participation, wind power accommodation and multiple DRPs. Specifically, based on the stochastic scenarios for EM clearing prices and wind power outputs uncertainties, the stochastic optimization method was applied to ensure the realization of operational cost minimization and wind power accommodation; the CVaR method was implemented to control the potential risk of operational cost increase. Moreover, by introducing multiple DRPs, the electrical, thermal and cooling loads can be transformed as flexible sources for CCHP micro-grid operation. Simulations were performed to show the following outcomes: (1) by applying the proposed stochastic-CVaR approach and considering multiple DRPs, CCHP micro-grid operation can reach better performance in terms of cost minimization, risk control and wind power accommodation etc.; (2) higher energy utilization efficiency can be achieved by coordinately optimizing EM power biddings; etc.

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Gopagoni, Praveen Kumar, and Mohan Rao S K. "Distributed elephant herding optimization for grid-based privacy association rule mining." Data Technologies and Applications 54, no. 3 (May 15, 2020): 365–82. http://dx.doi.org/10.1108/dta-07-2019-0104.

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PurposeAssociation rule mining generates the patterns and correlations from the database, which requires large scanning time, and the cost of computation associated with the generation of the rules is quite high. On the other hand, the candidate rules generated using the traditional association rules mining face a huge challenge in terms of time and space, and the process is lengthy. In order to tackle the issues of the existing methods and to render the privacy rules, the paper proposes the grid-based privacy association rule mining.Design/methodology/approachThe primary intention of the research is to design and develop a distributed elephant herding optimization (EHO) for grid-based privacy association rule mining from the database. The proposed method of rule generation is processed as two steps: in the first step, the rules are generated using apriori algorithm, which is the effective association rule mining algorithm. In general, the extraction of the association rules from the input database is based on confidence and support that is replaced with new terms, such as probability-based confidence and holo-entropy. Thus, in the proposed model, the extraction of the association rules is based on probability-based confidence and holo-entropy. In the second step, the generated rules are given to the grid-based privacy rule mining, which produces privacy-dependent rules based on a novel optimization algorithm and grid-based fitness. The novel optimization algorithm is developed by integrating the distributed concept in EHO algorithm.FindingsThe experimentation of the method using the databases taken from the Frequent Itemset Mining Dataset Repository to prove the effectiveness of the distributed grid-based privacy association rule mining includes the retail, chess, T10I4D100K and T40I10D100K databases. The proposed method outperformed the existing methods through offering a higher degree of privacy and utility, and moreover, it is noted that the distributed nature of the association rule mining facilitates the parallel processing and generates the privacy rules without much computational burden. The rate of hiding capacity, the rate of information preservation and rate of the false rules generated for the proposed method are found to be 0.4468, 0.4488 and 0.0654, respectively, which is better compared with the existing rule mining methods.Originality/valueData mining is performed in a distributed manner through the grids that subdivide the input data, and the rules are framed using the apriori-based association mining, which is the modification of the standard apriori with the holo-entropy and probability-based confidence replacing the support and confidence in the standard apriori algorithm. The mined rules do not assure the privacy, and hence, the grid-based privacy rules are employed that utilize the adaptive elephant herding optimization (AEHO) for generating the privacy rules. The AEHO inherits the adaptive nature in the standard EHO, which renders the global optimal solution.

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Xu, Yan. "Task Scheduling Algorithm Research in Grid Computing." Applied Mechanics and Materials 380-384 (August 2013): 2841–44. http://dx.doi.org/10.4028/www.scientific.net/amm.380-384.2841.

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This paper studies effective task scheduling problem in the process of grid computing. Generally, task scheduling in the process of grid computing can be realized in shorter time, which guarantees the efficiency of task scheduling in grid computing. Traditional algorithm can not fully consider the resources load balance in calculating task scheduling in grid computing, resulting in network resources idleness. Finally, it can't reasonably use network resources. In order to avoid the above defects, this paper proposes a task scheduling method in grid computing based on double fitness particle swarm optimization algorithm. In the process of grid computing, channel perception method is applied to forecast the amount of grid computing tasks in the channel so as to provide the basis for task scheduling in grid computing. Realize task scheduling in grid computing by the use of double fitness particle swarm optimization algorithm. Experimental results show that under the condition of larger tasks of grid computing, the performance of task scheduling in grid computing by using the algorithm presented in this paper is superior to the traditional particle swarm optimization algorithm and can get ideal task scheduling result.

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Tahyudin, Imam, Hidetaka Nambo, and Yoshitaka Goto. "An Optimization of the Autoregressive Model Using the Grid Search Method." International Journal of Engineering & Technology 7, no. 2.2 (March 5, 2018): 84. http://dx.doi.org/10.14419/ijet.v7i2.2.12739.

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The purpose of this study is to find the parameters that can produce the best value on the model Autoregressive (AR). The parameter evaluation method used is the Maximum Likelihood Estimator (MLE) and using Grid Search optimization methods. The experimental data used in this study was a sunspot dataset. Based on our analysis, the best Autoregressive model was a 3rd order AR model.

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Journal articles on the topic 'Grid Optimization Method'

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