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Subdomain generation using emergent ant colony optimization

机译:使用紧急蚁群优化生成子域

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摘要

Finite elements mesh decomposition is a well known optimization problem and is used to split a computationally expensive finite elements mesh into smaller subdomains for parallel finite elements analysis. The ant colony optimization is a type of algorithm that seeks to model the emergent behaviour observed in ant colonies and utilize this behaviour to solve combinatorial problems. This technique has been applied to several problems, most of which are graph related because the ant colony metaphor can be most easily applied to such types of problems. This paper examines the application of ant colony optimization algorithm to the partitioning of unstructured adaptive meshes for parallel explicit time-stepping finite elements analysis. The concept of ant colony optimization technique in addition to the notion of swarm intelligence for finding approximate solutions to combinatorial optimization problems is described. This algorithm combines the features of the classical ant colony optimization technique with swarm intelligence to form a model which is an artificial system designed to perform a certain task. The application of the ant colony optimization for partitioning finite elements meshes based on triangular elements using the swarm intelligence concept is described. A recursive greedy algorithm optimization method is also presented as a local optimization technique to improve the quality of the solutions given by the ant colony optimization algorithm. The partitioning is based on the recursive bisection approach. The mesh partitioning is carried out using normal and predictive modes for which the predictive mode uses a trained multi-layered feedforward neural network that estimates the number of triangular elements that will be generated after finite elements mesh generation is carried out. The performance of the proposed hybrid approach for the recursive bisection of finite elements meshes is examined by decomposing two mesh examples and comparing them with a well known finite elements domain decomposer.
机译:有限元网格分解是众所周知的优化问题,用于将计算上昂贵的有限元网格划分为较小的子域,以进行并行有限元分析。蚁群优化是一种算法,旨在对在蚁群中观察到的紧急行为进行建模,并利用该行为解决组合问题。该技术已应用于几个问题,其中大多数与图形相关,因为蚁群隐喻可以最容易地应用于此类问题。本文研究了蚁群优化算法在非结构化自适应网格划分中的应用,用于并行显式时步有限元分析。除了用于寻找组合优化问题的近似解的群体智能概念之外,还介绍了蚁群优化技术的概念。该算法将经典蚁群优化技术的特征与群体智能相结合,形成了一个模型,该模型是旨在执行特定任务的人工系统。描述了蚁群优化技术在群体智能概念的基础上对基于三角形单元的有限元网格划分的应用。还提出了一种递归贪婪算法优化方法作为一种局部优化技术,以提高蚁群优化算法给出的解的质量。分区基于递归二等分方法。网格划分使用常规模式和预测模式执行,对于该模式,预测模式使用经过训练的多层前馈神经网络,该网络估计在执行有限元网格生成后将要生成的三角形元素的数量。通过分解两个网格示例并将它们与一个众所周知的有限元域分解器进行比较,可以检验所提出的混合方法对有限元网格的递归二分法的性能。

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