The routing, modulation level, and spectrum allocation (RMLSA) problem is crucial for efficient elastic optical networks. This problem has been approached by optimal-and-non-scalable and sub-optimal-and-scalable solutions. In the second approach, we can distinguish the routing-based and permutation-based meta-heuristics. These approaches explore a sub-set of the RMLSA solutions, and consequently, the calculation of high-quality solutions can be limited.

This work proposes an RMLSA solution that considers the routing and request permutation simultaneously to explore a larger portion of the set of RMLSA solutions than state-of-the-art meta-heuristics. The proposed RMLSA solution is based on a genetic algorithm (GA) whose chromosome structure encodes routing and permutation genes.

Performance analysis of the proposed route-permutation-based GA (RPGA) has been compared to the state-of-the-art based on integer linear programming (ILP), route-based GA (RGA), and permutation-based GA (PGA) in offline and online traffic scenarios. Offline traffic simulations show that RPGA is promising since it obtains similar results to ILP. RGA gets worst as the traffic load increases compared to PGA and RPGA approaches. RGA, PGA, and RPGA achieve the same performance in all dynamic scenarios concerning blocking and entropy measures, given the set of requests is small.

路由、调制级别和​​频谱分配 (RMLSA) 问题对于高效的弹性光网络至关重要。这个问题已经通过最优和不可扩展和次优和可扩展的解决方案来解决。在第二种方法中，我们可以区分基于路由和基于排列的元启发式。这些方法探索了 RMLSA 解决方案的子集，因此，高质量解决方案的计算可能会受到限制。

这项工作提出了一种 RMLSA 解决方案，该解决方案同时考虑路由和请求排列，以探索 RMLSA 解决方案集合中比最先进的元启发式算法更大的部分。所提出的 RMLSA 解决方案基于遗传算法 (GA)，其染色体结构编码路由和排列基因。

所提出的基于路由排列的遗传算法 (RPGA) 的性能分析已与基于整数线性规划 (ILP)、基于路由的遗传算法 (RGA) 和基于排列的遗传算法 (PGA) 的最新技术进行了比较) 离线和在线流量场景。离线流量模拟表明，RPGA 很有前景，因为它获得了与 ILP 相似的结果。与 PGA 和RPGA 方法相比，随着流量负载的增加，RGA 变得最差。假设请求集很小，RGA、PGA 和RPGA 在所有关于阻塞和熵度量的动态场景中实现相同的性能。