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Iterated fast local search algorithm for solving quadratic assignment problems

机译:求解二次分配问题的迭代快速局部搜索算法

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摘要

This paper concentrates on multi-row machine layout problems that can be accurately formulated as quadratic assignment problems (QAPs). A genetic algorithm-based local search approach is proposed for solving QAPs. In the proposed algorithm, three different mutation operators namely adjacent, pair-wise and insertion or sliding operators are separately combined with a local search method to form a mutation cycle. Effectiveness of introducing the mutation cycle in place of mutation is studied. Performance of the proposed iterated approach is analyzed and the solution qualities obtained are reported.
机译:本文关注于多行机器布局问题,这些问题可以准确地表述为二次分配问题(QAP)。提出了一种基于遗传算法的局部搜索方法来解决QAP。在提出的算法中,将三个不同的变异算子,即相邻算子,成对算子和插入或滑动算子分别与局部搜索方法结合起来,形成一个变异周期。研究了引入突变周期代替突变的有效性。分析了所提出的迭代方法的性能,并报告了获得的解决方案质量。

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