A Multi-Level Optimization Method for Stencil Computation on the Domain that is Bigger than Memory Capacity of GPU

2013 ◽  
Author(s):  
Guanghao Jin ◽  
Toshio Endo ◽  
Satoshi Matsuoka
Keyword(s):  
Optimization Method ◽  
Memory Capacity ◽  
Stencil Computation ◽  
Multi Level

scholarly journals The Method of MMC Redundancy Optimization Allocation Based on Multi-objective Optimization

2018 ◽  
Vol 173 ◽  
pp. 03075
Author(s):  
Enshu Jin ◽  
Yagu Zhang
Keyword(s):  
Optimization Method ◽  
Normal Operation ◽  
Multi Objective Optimization ◽  
Configuration Optimization ◽  
Multi Objective ◽  
Optimal Value ◽  
Optimization Function ◽  
Multi Level ◽  
Optimization Allocation ◽  
Level Converter

When the modular multi-level converter of the sub-module faults, which will cause the unbalanced system, affect the normal operation of the system, so to the MMC arm configuration redundant sub-module is necessary, if the sub-module faults, redundant sub-modules will replace the fault sub-module maintaining the normal operation of system. In this paper, we propose a reasonable and effective method for the configuration optimization of redundant sub-module based on the three indexes, namely, efficient utilization of redundant sub-module, the number of redundant sub-module and the reliability of the system MMC, considering the three factors, establishing a multi-objective optimization function of redundant configuration, By calculating the optimal value to accurately calculate the number of redundant sub-module of MMC, based on the proposed redundancy configuration optimization method, building a simulation model of MMC 5 level in PSCAD, the results verify the rationality and feasibility of the proposed optimization method.

scholarly journals A Multi-Level Optimization Method for Elastic Constants Identification of Composite Laminates

2019 ◽  
Vol 9 (20) ◽  
pp. 4267
Author(s):  
Chien Yang Huang ◽  
Tai Yan Kam
Keyword(s):  
Optimal Design ◽  
Elastic Constants ◽  
Composite Laminates ◽  
Composite Laminate ◽  
Optimization Problem ◽  
Optimization Method ◽  
Global Minimization ◽  
Minimization Problems ◽  
Design Variables ◽  
Multi Level

A new and effective elastic constants identification technique is presented to extract the elastic constants of a composite laminate subjected to uniaxial tensile testing. The proposed technique consists of a new multi-level optimization method that can solve different types of minimization problems, including the extraction of material constants of composite laminates from given strains. In the identification process, the optimization problem is solved by using a stochastic multi-start dynamic search minimization algorithm at the first level in order to obtain the statistics of the quasi-optimal design variables for a set of randomly generated starting points. The statistics of the quasi-optimal elastic constants obtained at this level are used to determine the reduced feasible region in order to formulate the second-level optimization problem. The second-level optimization problem is then solved using the particle swarm algorithm in order to obtain the statistics of the new quasi-optimal elastic constants. The iteration process between the first and second levels of optimization continues until the standard deviations of the quasi-optimal design variables at any level of optimization are less than the prescribed values. The proposed multi-level optimization method, as well as several existing global optimization algorithms, is used to solve a number of well-known mathematical minimization problems to verify the accuracy of the method. For the adopted numerical examples, it has been shown that the proposed method is more efficient and effective than the adopted global minimization algorithms to produce the exact solutions. The proposed method is then applied to identify four elastic constants of a [0°/±45°]s composite laminate using three strains in 0°, 45°, and 90° directions, respectively, of the composite laminate subjected to uniaxial testing. For comparison purposes, several existing global minimization techniques are also used to solve the elastic constants identification problem. Again, it has been shown that the proposed method is capable of producing more accurate results than the adopted available methods. Finally, experimental data are used to demonstrate the applications of the proposed method.

Optimal Concurrent Design Based Upon Distributed Database and Knowledge Base Modeling

2001 ◽  
Author(s):  
F. Zhang ◽  
D. Xue
Keyword(s):  
Knowledge Base ◽  
Optimal Parameter ◽  
Distributed Database ◽  
Optimization Method ◽  
Concurrent Design ◽  
Swarm Optimization ◽  
Multi Level ◽  
Realization Process ◽  
Product Realization ◽  
Parameter Values

Abstract This research introduces an optimal concurrent design approach based upon a previously developed distributed database and knowledge base modeling method. In this approach, the product realization process alternatives and relevant activities are modeled at different locations that are connected through the Internet. The optimal product realization process alternative and its parameter values are identified using a multi-level optimization method. Genetic Programming (GP) and Particle Swarm Optimization (PSO) are employed for identifying the optimal product realization process alternative and the optimal parameter values of the feasible alternatives, respectively.

scholarly journals 3D focusing acoustic lens optimization method using multi-factor and multi-level orthogonal test designing theory

2020 ◽  
Vol 170 ◽  
pp. 107538 ◽  
Author(s):  
Hongyu Sun ◽  
Shen Wang ◽  
Songling Huang ◽  
Lisha Peng ◽  
Qing Wang ◽  
...  
Keyword(s):  
Optimization Method ◽  
Orthogonal Test ◽  
Acoustic Lens ◽  
Multi Level

A New Structural Optimization Method Based on Group Hunting of Animals: Hunting Search (HuS)

2010 ◽  
Author(s):  
R. Oftadeh ◽  
M. J. Mahjoob
Keyword(s):  
Structural Optimization ◽  
Numerical Optimization ◽  
Optimization Problems ◽  
Optimization Technique ◽  
Harmony Search ◽  
Optimization Methods ◽  
Optimization Method ◽  
Global Optimization Methods ◽  
Multi Level ◽  
Group Hunting

This paper presents a novel structural optimization algorithm based on group hunting of animals such as lions, wolves, and dolphins. Although these hunters have differences in the way of hunting but they are common in that all of them look for a prey in a group. The hunters encircle the prey and gradually tighten the ring of siege until they catch the prey. In addition, each member of the group corrects its position based on its own position and the position of other members. If the prey escapes from the ring, the hunters reorganize the group to siege the prey again. A benchmark numerical optimization problems is presented to show how the algorithm works. Three benchmark structural optimization problems are also presented to demonstrate the effectiveness and robustness of the proposed Hunting Search (HuS) algorithm for structural optimization. The objective in these problems is to minimize the weight of bar trusses. Both sizing and layout optimization variables are included, too. The proposed algorithm is compared with other global optimization methods such as CMLPSA (Corrected Multi-Level & Multi-Point Simulated Annealing) and HS (Harmony Search). The results indicate that the proposed method is a powerful search and optimization technique. It yields comparable and in some cases, better solutions compared to those obtained using current algorithms when applied to structural optimization problems.

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