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A framework for constructing complete algorithms based on local search

机译：基于本地搜索构建完整算法的框架

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Backtrack search enhanced by local consistency techniques can be effective at finding solutions for (or proving the infeasibility of) tightly-constrained problems with complex and overlapping constraints. By contrast, local search can be superior at optimizing problems that are loosely constrained, but is weaker on problems with a complex constraint satisfaction element, and cannot prove problem infeasibility. This paper describes local probing which marries the strengths of local search and backtrack search and is capable of finding a solution or proving that none exists. Local probing is a local search extension to an existing hybridization framework, probe backtrack search. Here, a master backtrack search algorithm hybridizes a slave local search algorithm, which solves dynamically created subproblems that are easier to solve than the original problem addressed. We present comparison results on resource constrained scheduling, showing how local probing successfully adds infeasibility proofs to its slave local search algorithm.
机译：通过本地一致性技术增强的回溯搜索可以有效地找到解决方案（或证明其不可行），这些问题具有复杂且重叠的约束。相比之下，局部搜索在优化那些松散约束的问题上会更胜一筹，而在具有复杂约束满足元素的问题上则较弱，并且不能证明问题不可行。本文介绍了结合了本地搜索和回溯搜索优势的本地探测，并且能够找到解决方案或证明不存在任何解决方案。本地探测是对现有杂交框架（探测回溯搜索）的本地搜索扩展。在这里，主回溯搜索算法与从属本地搜索算法混合，从而解决了动态创建的子问题，这些子问题比原始问题要容易解决。我们在资源受限调度上提供了比较结果，显示了本地探测如何成功地为其从属本地搜索算法添加了不可行证明。

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