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Collaboration Between Hyperheuristics to Solve Strip-Packing Problems

机译:超启发式方法之间的协作以解决带包装问题

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In this paper we introduce a collaboration framework for hyperheuristics to solve hard strip packing problems. We have designed a genetic based hyperheuristic to cooperate with a hill-climbing based hyperheuristic. Both of them use the most recently proposed low-level heuristics in the literature. REVAC, which has recently been proposed for tuning [18], has been used to find the best operators parameter values. The results obtained are very encouraging and have improved the results from both the single heuristics and the single hyperheuristics' tests. Thus, we conclude that the collaboration among hyperheuristics is a good way to solve hard strip packing problems.
机译:在本文中,我们介绍了一种超启发式方法的协作框架,以解决硬带包装问题。我们设计了一种基于遗传的超启发式方法,以与基于爬山的超启发式方法配合使用。他们俩都使用文献中最新提出的低级启发式方法。最近提出的REVAC用于调谐[18],已被用来寻找最佳的操作员参数值。所获得的结果令人鼓舞,并且改善了单一启发式测试和单一超启发式测试的结果。因此,我们得出结论,超启发式方法之间的协作是解决硬带包装问题的好方法。

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