Heuristic-based approaches are widely deployed in solving Spectrum Assignment problem. This causes the results to be unreliable in some test cases when the results are very far from the lowerbound. This paper presents an exhaustive search approach that starts with an initial seed of a solution achieved by a heuristic-based algorithm called “Longest First Fit” (LFF) and tries all possible permutations starting from this initial seed. The algorithm skips some branches and all its descendant permutations if it meets certain criteria that guarantees that those permutations will not lead to a better result. The experimental results show that the new algorithm has succeeded in achieving the lower-bound in 93% of the randomly generated test cases while the heuristic solver LFF can achieve 65%. The algorithm achieves these results in a reasonable running time of less than 10 seconds.

中文翻译：

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详尽搜索弹性光网络中的最佳离线频谱分配

基于启发式的方法广​​泛用于解决频谱分配问题。当结果离下限很远时，这会导致结果在某些测试案例中不可靠。本文提出了一种详尽的搜索方法，该方法从通过称为“最长优先拟合”（LFF）的启发式算法实现的解决方案的初始种子开始，并尝试从该初始种子开始的所有可能置换。如果算法满足某些标准，则该算法将跳过某些分支及其所有后代置换，这些准则保证了这些置换不会导致更好的结果。实验结果表明，新算法成功地实现了93％的随机生成测试用例的下界，而启发式求解器LFF可以达到65％。