# Evolutionary Strategy for Practical Design of Passive Optical Networks
## 2 Discussion
[20] was relatively small, which greatly reduces the search space and simplifies the task of finding the optimal solution. [PERSON] et al. [21] proposed a genetic algorithm for solving PONs projects considering three different types of basic topologies, such as tree, ring and bus. The proposed GA was responsible for determining the optical splitters' placement in the network and also the optical cables' routing, seeking for the lowest cost of implanting the network. However, despite presenting a good performance when compared to manual methods, the developed tool was quite simple and considered only the splitters cost and the cost of a single type of cable, not taking into account several other factors involved in proper network planning. In [21], the used maps were not georeferenced and no further details were provided on the process of importing the maps. [PERSON] et al. [22] introduced an integer linear programming (ILP) model which was capable of designing a single-stage and multiple-stage splitting PON. In order to reduce the computation time for larger sizes, a two-stage heuristic was proposed to tackle with scenarios that were beyond the computational abilities of the optimal method. The aim of the paper was finding the least costly tree topology deployment configuration considering equipment and installation costs. For the single-stage ILP model, the authors adapted the classical concentrator location problem (CLP) applied to telecommunications network. For the multistage problem, new ILP model was formulated to deal with it. The following inputs were considered: OLT and optical network units (ONU) locations; number of candidate locations for splitter placement; and respective interconnection costs. The maximum split ratio allowed by each PON port was assumed to be 64. For the heuristic approach, developed to cope with large-scale multistage PONs, the authors proposed an algorithm that used the same inputs as the ILP model. The basic concept was to obtain an initial cost-effective configuration and then locally to search for alterations in the network layout in order to reduce the overall cost. It should be noted that power budget constraint was considered in both approaches. Although not addressed in the paper, the authors highlighted that presented approaches can be easily adapted to other PON technology types. However, in the simulations, georeferenced or real maps were not used, which provides poor details compared to a real PON planning problem. Moreover, the adopted size of the available splitters placing locations was reduced (search space). These possibilities were quite small, reaching only 50 possibilities (in the worst case), leading to a reduction in the optimization problem complexity. Some recent works involve evaluation scenarios that are not part of the scope of this paper, such as: radio-over-fiber, internet of things and data centers. As an example, in [23], the authors present a novel radio-over-optical fiber network architecture with multi-stratum resources optimization using software-defined networking. The proposed architecture can globally optimize radio frequency, optical spectrum and cloud baseband unit processing resources effectively to maximize radio coverage and meet the quality of service requirement. In [24], the authors propose a brain-like productive service provisioning scheme with federated learning for industrial internet of things. The scheme combines production information into network optimization, and uses the interfactory and intrafactory relations to enhance the accuracy of service prediction. In [25], the authors present a novel architecture that can enable cross-stratum optimization of application and optical network stratum resources, and enhance multiple-layer resource integration in ubiquitous data center optical interconnection. In this paper, we propose an evolutionary strategy to optimize the design of PONs.