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Evolving reordering algorithms using an ant colony hyperheuristic approach for accelerating the convergence of the ICCG method

机译:使用蚁群化学方法进化重新排序算法来加速ICCG方法的收敛性

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This paper proposes a novel ant colony hyperheuristic approach for reordering the rows and columns of symmetric positive definite matrices. This ant colony hyperheuristic approach evolves heuristics for bandwidth reduction applied to instances arising from specific application areas with the objective of generating low-cost reordering algorithms. This paper evaluates the resulting reordering algorithm in each application area against state-of-the-art reordering algorithms with the purpose of reducing the running times of the zero-fill incomplete Cholesky-preconditioned conjugate gradient method. The results obtained on a wide-ranging set of standard benchmark matrices show that the proposed approach compares favorably with state-of-the-art reordering algorithms when applied to instances arising from computational fluid dynamics, structural, and thermal problems.
机译:本文提出了一种新型蚁群化学方法,用于重新排序对称正定矩阵的行和列。这种蚁群化的高温素气方法演化了启发式的带宽减少,应用于特定应用领域产生的实例,其目的是产生低成本重新排序算法。本文评估了对每个应用领域的所得重新排序算法,其针对最先进的重新排序算法,目的是减少零填充不完全尖孔的运行时间的预处理缀合物梯度方法的运行时间。在广泛的标准基准矩阵上获得的结果表明,当应用于从计算流体动力学,结构和热问题的情况下应用时,所提出的方法与最先进的重新排序算法相比。

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