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A hybrid Lagrangian genetic algorithm for the prize collecting Steiner tree problem

机译:奖品斯坦纳树问题的混合拉格朗日遗传算法

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摘要

We consider the version of prize collecting Steiner tree problem (PCSTP) where each node of a given weighted graph is associated with a prize and where the objective is to find a minimum weight tree spanning a subset of nodes and collecting a total prize not less that a given quota Q. We present a lower bound and a genetic algorithm for the PCSTP. The lower bound is based on a Lagrangian decomposition of a minimum spanning tree formulation of the problem. The volume algorithm is used to solve the Lagrangian dual. The genetic algorithm incorporates several enhancements. In particular, it fully exploits both primal and dual information produced by Lagrangian decomposition. The proposed lower and upper bounds are assessed through computational experiments on randomly generated instances with up to 500 nodes and 5000 edges. For these instances, the proposed lower and upper bounds exhibit consistently a tight gap: in 76% of the cases the gap is strictly less than 2%.
机译:我们考虑奖品收集Steiner树问题(PCSTP)的版本,其中给定加权图的每个节点都与奖品关联,并且目标是找到跨越节点子集的最小权重树并收集总奖品不小于给定的配额Q。我们提出PCSTP的下限和遗传算法。下限基于问题的最小生成树公式的拉格朗日分解。体积算法用于求解拉格朗日对偶。遗传算法具有多项增强功能。特别是,它充分利用了由拉格朗日分解产生的原始信息和对偶信息。拟议的下限和上限是通过对多达500个节点和5000个边的随机生成实例进行计算实验来评估的。对于这些情况,建议的下限和上限始终显示出紧密的差距:在76%的情况下,差距严格小于2%。

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