Flexible optical network architectures are considered a very promising solution where spectrum resources are allocated within flexible frequency grids. This paper presents a minimum spectrum utilization (SU) and average path length (APL) approach to solve the (off-line) routing and spectrum allocation problem (RSA) based on combining a simple ordering pre-computation strategy, namely most subcarriers first (MSF) with three nature-inspired algorithms. These algorithms are ant colony optimization, differential evolution based relative position indexing (DE-RPI), and differential evolution general combinatorial (DE-GC). We begin by showing that MSF is the most effective ordering pre-computation strategy when compared to other well-known typical heuristics in the literature, such as first-fit, and longest path first. Then, we apply MSF in combination with the three nature-inspired algorithms to simultaneously optimize the SU and APL. The usefulness of MSF ordering pre-computation strategy is presented via a comparison of results obtained when using and not using MSF under the same scenarios. The algorithms are evaluated in benchmark optical networks, such as the NSFNet, the European optical network, and the 40-node USA network. We show that DE-RPI with MSF ordering pre-computation is the best option to solve the RSA problem, obtaining an average improvement percentage in the range of 0.9772–4.4086% on the SU and from \(-0.1668\) to 0.8511% on the APL when compared to other meta-heuristics, either with or without the MSF ordering policy.

灵活的光网络架构被认为是非常有前途的解决方案，其中频谱资源在灵活的频率网格内分配。本文提出了一种最小频谱利用率（SU）和平均路径长度（APL）方法，通过结合简单的排序预计算策略（即大多数子载波优先（）来解决（离线）路由和频谱分配问题（RSA） MSF）和三种自然启发算法。这些算法是蚁群优化，基于差分进化的相对位置索引（DE-RPI）和差分进化通用组合（DE-GC）。首先，我们证明与文献中其他众所周知的典型启发式算法（例如“首次拟合”和“最长路径优先”）相比，MSF是最有效的排序预计算策略。然后，我们将MSF与三种自然启发算法结合使用，以同时优化SU和APL。通过比较在相同情况下使用和不使用MSF时获得的结果，介绍了MSF有序预计算策略的有用性。在基准光网络（例如NSFNet，欧洲光网络和40节点美国网络）中对算法进行了评估。我们表明，采用MSF顺序预计算的DE-RPI是解决RSA问题的最佳选择，SU上的平均改进百分比为0.9772–4.4086％。在基准光网络（例如NSFNet，欧洲光网络和40节点美国网络）中对算法进行了评估。我们表明，采用MSF顺序预计算的DE-RPI是解决RSA问题的最佳选择，SU上的平均改进百分比为0.9772–4.4086％。在基准光网络（例如NSFNet，欧洲光网络和40节点美国网络）中对算法进行了评估。我们表明，采用MSF顺序预计算的DE-RPI是解决RSA问题的最佳选择，SU上的平均改进百分比为0.9772–4.4086％。有或没有MSF排序策略时，与其他元启发式算法相比，APL的\（-0.1668 \）达到0.8511％。