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Application of the 'descent with mutations' metaheuristic to a clique partitioning problem

机译:元启发式“下降的突变”在集团划分问题中的应用

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We study here the application of a metaheuristic, issued from the noising methods and that we call "descent with mutations", to a problem arising in the field of the aggregation of symmetric relations: the clique partitioning of a weighted graph. This local search metaheuristic, of which the design is very simple, is compared with another very efficient metaheuristic, which is a simulated annealing improved by the addition of some ingredients coming from the noising methods. These experiments show that the descent with mutations is at least as efficient for the studied problem as this improved simulated annealing, usually a little better, while, above all, it is much easier to design and to apply.
机译:我们在这里研究一种从启发方法发出的元启发式方法的应用,我们称其为“变异下降”,该方法适用于对称关系聚合领域中出现的一个问题:加权图的集团划分。将这种局部搜索元启发式方法(其设计非常简单)与另一种非常有效的元启发式方法进行了比较,后者是通过添加来自噪声方法的某些成分而改进的模拟退火方法。这些实验表明,具有突变的下降对于研究的问题至少与改进的模拟退火一样有效,通常要好一些,而最重要的是,它更易于设计和应用。

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