# Evolutionary Strategy for Practical Design of Passive Optical Networks
## 2 Discussion  
There are numerous optical network technologies. However, PON stands out by offering a highly efficient and cost-effective access network [1]. A PON's main feature is that it is implemented as a point-to-multipoint architecture, in which unpowered fiber optic splitters are used to enable a single optical fiber to serve multiple end-points (consumers) [16]. Due to the consideration of many design factors, the optical network planning process often exhibits several challenges from the optimization point of view [17; 18]. To design a cost-effective PON, it is required the consideration of many factors such as the split ratio,position of optical splitters in the optical distribution network (ODN) and the assignment of subscriber premises to the splitters. In addition, all the planning rules must be satisfied including constraints on equipment capacity and the maximum cabling distances which are derived from power budget constraints [19]. For this purpose, several optimization techniques have been used for access network planning in order to find an efficient infrastructure to meet requirements, aiming to obtain the lowest possible deployment cost [20]. Nowadays, network planning is one of the most important topics of PONs. Therefore, several proposals based on mathematical techniques, heuristic and meta-heuristic algorithms have been reported for this optimization problem [18; 20; 21; 22]. [PERSON] et al. [18] proposed a heuristic algorithm based on ant colony optimization (ACO) to minimize the PON deployment cost. In summary, the ACO algorithm is a probabilistic technique for solving computational problems. This technique is inspired by the behavior of ants that leave their nest seeking the shortest path between their colony and a source of food. [PERSON] et al. [18] developed a system that was able to minimize the PON deployment for a specific tree PON topology. The considered topology was limited to use just one level of optical splitters and drop closures for the distribution of optical fibers. The objective function was based on the deployment network cost. In addition, when the cost was evaluated, only optical cables, optical splitters and drop closures were considered, ignoring others important deployment costs such as optical splices, network elements in central office, etc. However, [PERSON] et al. [18] do not inform how the obtained results were validated. Regarding the adopted scenarios for the tests, the authors informed only that they were based on real scenarios, but did not present used maps and neither inform more details about them. [PERSON] et al. [20] proposed an algorithm which deals with optimization of PON designs. The proposed system used a combination of different methods to find the solutions. Firstly, [PERSON] et al. [20] introduced an algorithm based on [PERSON]'s algorithm to find optimum (minimum) distances between optical line terminals (OLT) and optical network terminals (ONT). Then, for the second part (optimum splitter placement), \(k\)-mean clustering and hierarchical clustering techniques were used. The proposed algorithm found an optimal metric, thus creating the optimized tree topology mainly focused on summary trending distance (although endpoint attenuation and minimum summary length of optical fibers were considered). For tests, it was used a real map of a small residential neighborhood of Prague (Czech Republic). Raw data were obtained from Open Street Maps (OSMs) software. For simulations, a scenarios using 40 CNTs and one single OLT, randomly placed on the map, using one to three levels of optical splitters in the outside plant was considered. The proposed algorithm was a good alternative to conventional methods and the results were presented in graphical and table forms. However, important parameters that also influence the deployment cost were not considered (such as splices, cabinets, etc.) and only one type of cable was used for the entire network. The tested scenario considered by [PERSON] et al.