Application of High-Performance Computing to Numerical Simulation of Human Movement

1995 ◽  
Vol 117 (1) ◽  
pp. 155-157 ◽  
Author(s):  
F. C. Anderson ◽  
J. M. Ziegler ◽  
M. G. Pandy ◽  
R. T. Whalen
Keyword(s):  
Computer Architecture ◽  
High Performance ◽  
Large Scale ◽  
Optimization Problems ◽  
Optimal Solution ◽  
Human Movement ◽  
Parallel Machine ◽  
Optimal Controls ◽  
Processing Machine ◽  
Vector Processing

We have examined the feasibility of using massively-parallel and vector-processing supercomputers to solve large-scale optimization problems for human movement. Specifically, we compared the computational expense of determining the optimal controls for the single support phase of gait using a conventional serial machine (SGI Iris 4D25), a MIMD parallel machine (Intel iPSC/860), and a parallel-vector-processing machine (Cray Y-MP 8/864). With the human body modeled as a 14 degree-of-freedom linkage actuated by 46 musculotendinous units, computation of the optimal controls for gait could take up to 3 months of CPU time on the Iris. Both the Cray and the Intel are able to reduce this time to practical levels. The optimal solution for gait can be found with about 77 hours of CPU on the Cray and with about 88 hours of CPU on the Intel. Although the overall speeds of the Cray and the Intel were found to be similar, the unique capabilities of each machine are better suited to different portions of the computational algorithm used. The Intel was best suited to computing the derivatives of the performance criterion and the constraints whereas the Cray was best suited to parameter optimization of the controls. These results suggest that the ideal computer architecture for solving very large-scale optimal control problems is a hybrid system in which a vector-processing machine is integrated into the communication network of a MIMD parallel machine.

Genetic Algorithm and Other Meta-Heuristics

2005 ◽  
pp. 144-173 ◽  
Author(s):  
Bernard K.S. Cheung
Keyword(s):  
Genetic Algorithm ◽  
Large Scale ◽  
Optimization Problems ◽  
Optimal Solution ◽  
Schema Theory ◽  
Heuristic Methods ◽  
Optimal Solutions ◽  
Corporate Globalization ◽  
Global Optimal ◽  
Mutation And Selection

Genetic algorithms have been applied in solving various types of large-scale, NP-hard optimization problems. Many researchers have been investigating its global convergence properties using Schema Theory, Markov Chain, etc. A more realistic approach, however, is to estimate the probability of success in finding the global optimal solution within a prescribed number of generations under some function landscapes. Further investigation reveals that its inherent weaknesses that affect its performance can be remedied, while its efficiency can be significantly enhanced through the design of an adaptive scheme that integrates the crossover, mutation and selection operations. The advance of Information Technology and the extensive corporate globalization create great challenges for the solution of modern supply chain models that become more and more complex and size formidable. Meta-heuristic methods have to be employed to obtain near optimal solutions. Recently, a genetic algorithm has been reported to solve these problems satisfactorily and there are reasons for this.

Multiagent Coordination Optimization Based Model Predictive Control Strategy With Application to Balanced Resource Allocation

2014 ◽  
Author(s):  
Haopeng Zhang ◽  
Qing Hui
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Control Strategy ◽  
Large Scale ◽  
Optimization Problems ◽  
Optimal Solution ◽  
Nonlinear Problems ◽  
Pso Algorithm ◽  
Network Resource ◽  
Multiagent Coordination

Model predictive control (MPC) is a heuristic control strategy to find a consequence of best controllers during each finite-horizon regarding to certain performance functions of a dynamic system. MPC involves two main operations: estimation and optimization. Due to high complexity of the performance functions, such as, nonlinear, non-convex, large-scale objective functions, the optimization algorithms for MPC must be capable of handling those problems with both computational efficiency and accuracy. Multiagent coordination optimization (MCO) is a recently developed heuristic algorithm by embedding multiagent coordination into swarm intelligence to accelerate the searching process for the optimal solution in the particle swarm optimization (PSO) algorithm. With only some elementary operations, the MCO algorithm can obtain the best solution extremely fast, which is especially necessary to solve the online optimization problems in MPC. Therefore, in this paper, we propose an MCO based MPC strategy to enhance the performance of the MPC controllers when addressing non-convex large-scale nonlinear problems. Moreover, as an application, the network resource balanced allocation problem is numerically illustrated by the MCO based MPC strategy.

A Kind of New Immune Genetic Algorithm and its Application

2011 ◽  
Vol 48-49 ◽  
pp. 25-28
Author(s):  
Wei Jian Ren ◽  
Yuan Jun Qi ◽  
Wei Lv ◽  
Cheng Da Li
Keyword(s):  
Genetic Algorithm ◽  
Large Scale ◽  
Optimization Problems ◽  
Logistic Map ◽  
Optimal Solution ◽  
Optimization Method ◽  
Local Optimum ◽  
Immune Genetic Algorithm ◽  
Chaos Optimization ◽  
Scale Optimization

According to the phenomenon of falling into local optimum during solving large-scale optimization problems and the shortcomings of poor convergence of Immune Genetic Algorithm, a new kind of probability selection method based on the concentration for the genetic operation is presented. Considering the features of chaos optimization method, such like not requiring the solved problems with continuity or differentiability, which is unlike the conventional method, and also with a solving process within a certain range traverse in order to find the global optimal solution, a kind of Chaos Immune Genetic Algorithm based on Logistic map and Hénon map is proposed. Through the application to TSP problem, the results have showed the superior to other algorithms.

Metaheuristics Applied to Biorefinery Supply Chain Problems

2015 ◽  
pp. 23-46
Author(s):  
Krystel K. Castillo-Villar
Keyword(s):  
Supply Chain ◽  
Large Scale ◽  
Optimization Problems ◽  
Optimal Solution ◽  
Large Scale Optimization ◽  
Alternative Source ◽  
Daunting Task ◽  
Mathematical Problems ◽  
To Come ◽  
Scale Optimization

Bioenergy has been recognized as an important alternative source of energy. The production of bioenergy is expected to increase in the years to come, and one of the most important obstacles in increased bioenergy utilization are the logistics problems, which involve complex and large-scale optimization problems. Solving these problems constitutes a daunting task, and often, traditional mathematical approaches fail to converge to the optimal solution within a reasonable time. Thus, more robust methods are required in order to overcome complexity. Metaheuristics are strategies for solving complex and large-scale optimization problems, which provide a near-optimal or practically useful solution. The aim of this chapter is to present a survey of metaheuristics and the available literature regarding the application of metaheuristics in the bioenergy supply chain field as well as the uniqueness and challenges of the mathematical problems applied to bioenergy.

Thallo – Scheduling for High-Performance Large-Scale Non-Linear Least-Squares Solvers

2021 ◽  
Vol 40 (5) ◽  
pp. 1-14
Author(s):  
Michael Mara ◽  
Felix Heide ◽  
Michael Zollhöfer ◽  
Matthias Nießner ◽  
Pat Hanrahan
Keyword(s):  
Least Squares ◽  
High Performance ◽  
Large Scale ◽  
Optimization Problems ◽  
Bundle Adjustment ◽  
Large Set ◽  
Linear Least Squares ◽  
Non Linear ◽  
Recent Developments ◽  
Scale Optimization

Large-scale optimization problems at the core of many graphics, vision, and imaging applications are often implemented by hand in tedious and error-prone processes in order to achieve high performance (in particular on GPUs), despite recent developments in libraries and DSLs. At the same time, these hand-crafted solver implementations reveal that the key for high performance is a problem-specific schedule that enables efficient usage of the underlying hardware. In this work, we incorporate this insight into Thallo, a domain-specific language for large-scale non-linear least squares optimization problems. We observe various code reorganizations performed by implementers of high-performance solvers in the literature, and then define a set of basic operations that span these scheduling choices, thereby defining a large scheduling space. Users can either specify code transformations in a scheduling language or use an autoscheduler. Thallo takes as input a compact, shader-like representation of an energy function and a (potentially auto-generated) schedule, translating the combination into high-performance GPU solvers. Since Thallo can generate solvers from a large scheduling space, it can handle a large set of large-scale non-linear and non-smooth problems with various degrees of non-locality and compute-to-memory ratios, including diverse applications such as bundle adjustment, face blendshape fitting, and spatially-varying Poisson deconvolution, as seen in Figure 1. Abstracting schedules from the optimization, we outperform state-of-the-art GPU-based optimization DSLs by an average of 16× across all applications introduced in this work, and even some published hand-written GPU solvers by 30%+.

Genetic Algorithm for Panel Cutting Stock on CUDA Platform

2013 ◽  
Vol 712-715 ◽  
pp. 2569-2575
Author(s):  
Wen Wu Xie ◽  
Tao Ning
Keyword(s):  
Genetic Algorithm ◽  
Discrete Optimization ◽  
High Performance ◽  
Large Scale ◽  
Optimization Problems ◽  
Raw Material ◽  
Cutting Stock ◽  
Steel Bars ◽  
Material Utilization ◽  
Discrete Optimization Problems

The problem of placing a number of specific shapes on a raw material in order to maximize material utilization is commonly encountered in the production of steel bars and plates, papers, glasses, etc. In this paper, we presented a genetic algorithm for steel grating nesting design. For application in large-scale discrete optimization problems, we also implemented this algorithm with CUDA based on parallel computation. Experimental results show that under genetic algorithm invoking with CUDA scheme, we can obtain satisfied solutions to steel grating nesting problem with high performance.

scholarly journals A Branch-and-Bound Algorithm Embedded with DCA for DC Programming

2012 ◽  
Vol 2012 ◽  
pp. 1-16 ◽  
Author(s):  
Meihua Wang ◽  
Fengmin Xu ◽  
Chengxian Xu
Keyword(s):  
Branch And Bound ◽  
Large Scale ◽  
Optimization Problems ◽  
Optimal Solution ◽  
Dc Programming ◽  
Computational Time ◽  
Special Importance ◽  
Branch Number ◽  
Dc Algorithm ◽  
Computational Performance

The special importance of Difference of Convex (DC) functions programming has been recognized in recent studies on nonconvex optimization problems. In this work, a class of DC programming derived from the portfolio selection problems is studied. The most popular method applied to solve the problem is the Branch-and-Bound (B&B) algorithm. However, “the curse of dimensionality” will affect the performance of the B&B algorithm. DC Algorithm (DCA) is an efficient method to get a local optimal solution. It has been applied to many practical problems, especially for large-scale problems. A B&B-DCA algorithm is proposed by embedding DCA into the B&B algorithms, the new algorithm improves the computational performance and obtains a global optimal solution. Computational results show that the proposed B&B-DCA algorithm has the superiority of the branch number and computational time than general B&B. The nice features of DCA (inexpensiveness, reliability, robustness, globality of computed solutions, etc.) provide crucial support to the combined B&B-DCA for accelerating the convergence of B&B.

scholarly journals Realization of a Framework for Simulation-Based Large-Scale Shape Optimization Using Vertex Morphing

2021 ◽  
Author(s):  
Aditya Ghantasala ◽  
Reza Najian Asl ◽  
Armin Geiser ◽  
Andrew Brodie ◽  
Efthymios Papoutsis ◽  
...  
Keyword(s):  
Shape Optimization ◽  
Numerical Optimization ◽  
High Performance ◽  
Large Scale ◽  
Optimization Problems ◽  
Engineering Design Process ◽  
Current Contribution ◽  
Simulation Based ◽  
2D And 3D ◽  
Performance Computing

AbstractThere is a significant tendency in the industry for automation of the engineering design process. This requires the capability of analyzing an existing design and proposing or ideally generating an optimal design using numerical optimization. In this context, efficient and robust realization of such a framework for numerical shape optimization is of prime importance. Another requirement of such a framework is modularity, such that the shape optimization can involve different physics. This requires that different physics solvers should be handled in black-box nature. The current contribution discusses the conceptualization and applications of a general framework for numerical shape optimization using the vertex morphing parametrization technique. We deal with both 2D and 3D shape optimization problems, of which 3D problems usually tend to be expensive and are candidates for special attention in terms of efficient and high-performance computing. The paper demonstrates the different aspects of the framework, together with the challenges in realizing them. Several numerical examples involving different physics and constraints are presented to show the flexibility and extendability of the framework.

An Efficient and Effective Algorithm for Large Scale Global Optimization Problems

2015 ◽  
Vol 29 (04) ◽  
pp. 1559006 ◽  
Author(s):  
Kanchao Lian ◽  
Xu-Yu Peng ◽  
Aijia Ouyang
Keyword(s):  
Global Optimization ◽  
Information Exchange ◽  
Large Scale ◽  
Optimization Problems ◽  
Optimal Solution ◽  
Local Optimum ◽  
Test Results ◽  
Invasive Weed Optimization ◽  
Invasive Weed ◽  
Intelligent Algorithms

Invasive weed optimization (IWO) algorithm and quantum-behaved particle swarm optimization (QPSO) algorithm are inclined to fall into local optimum with lower convergence accuracy when separately used to deal with large scale global optimization (LSGO) problems. In order to fully utilize the advantages of these two intelligent algorithms and complement each other, following the idea of portfolio optimization, this paper correspondingly adjusts and improves the quantum models of IWO and QPSO, organically integrates the two algorithms, and proposes the quantum-behaved invasive weed optimization (QIWO) algorithm. This mixed algorithm can achieve the purpose of information exchange and cooperative search through alternate search enables the make algorithm converge to the optimal solution quickly, properly overcoming the defects of falling into local optimum and premature convergence. Test results of 20 LSGO functions show that compared with other algorithms, QIWO has stronger global optimization capability, faster convergence speed and higher convergence accuracy.

Focus Group: An Optimization Algorithm Inspired by Human Behavior

2018 ◽  
Vol 17 (01) ◽  
pp. 1850002 ◽  
Author(s):  
Edris Fattahi ◽  
Mahdi Bidar ◽  
Hamidreza Rashidy Kanan
Keyword(s):  
Focus Group ◽  
Optimization Algorithm ◽  
High Performance ◽  
Optimization Problems ◽  
Optimal Solution ◽  
Genetic Operators ◽  
Specific Subject ◽  
Group Members ◽  
The Subject

This paper presents a novel optimization algorithm, namely focus group (FG) algorithm, for solving optimization problems. The proposed algorithm is inspired by the behavior of group members to share their ideas (solutions) about a specific subject and trying to improve the solutions based on the cooperation and discussion. In the proposed algorithm, all the members present their solutions about the subject and all the suggested solutions proportional to their fitness, leave impact on the other solutions and incline them towards themselves. While trying to improve the quality of the candidate solutions, they converge to the optimal solution. To improve the performance of the proposed algorithm, two genetic operators are incorporated into the algorithm. The proposed algorithm is evaluated using several constrained and unconstrained benchmark functions commonly used in the area of optimization. Experimental results, in comparison with different well-known evolutionary techniques, confirm the high performance of the proposed algorithm in dealing with the optimization problems.

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