# Evolutionary Strategy for Practical Design of Passive Optical Networks
## 4 Proposed Algorithm for PON Optimization
### Our Proposed Genetic Algorithm for Optimization of PONs Planning
It is worth mentioning that, for optical link budget calculation, the lowest value of launched power was considered (worst case). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Component** & **Average Launch** & **Receiver** & **Receiver** & **Reference** \\  & **Power** & **Optical** & **Sensitivity** & **Part-Number** \\ \hline OLT (SFP & \(1.5\) to \(5\) dBm & \(-8\) dBm & \(-28\) dBm & \(35510197\) \\ CLASSE B+) & & \(0.5\) to \(5\) dBm & \(-8\) dBm & \(-27\) dBm & \(35510133\) \\ CNT (CNT FK-G421W) & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Typical losses adopted for network active elements. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Component** & **Typical Loss** & **Reference or Part-Number** \\ \hline Drop Cable (02F) & 0.37 dB/km & 17042026 \\ Distribution Cable (12F) & (for 1310 nm) & 17045113 \\ Feeder Cable (48F) & (for 1310 nm) & 17040049 \\ Optical Splitter \(1\times 2\) & 3.7 dB & 35500123 \\ Optical Splitter \(1\times 4\) & 7.1 dB & 35505000 \\ Optical Splitter \(1\times 8\) & 10.5 dB & 35505001 \\ Optical Splitter \(1\times 16\) & 13.7 dB & 35505002 \\ Optical Splitter \(1\times 32\) & 17.1 dB & 35505003 \\ Optical Splitter \(1\times 64\) & 20.5 dB & 35505047 \\ Aligned fusion splice & 0.08 dB & ITU-T G.671 \\ Mechanical splice & 0.15 dB & ITU-T G.671 \\ Optical Connector & 0.3 dB & ITU-T G.671 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Typical losses adopted for network passive elements. In the proposed algorithm, the optical attenuation was calculated only for the 1310 nm wavelength (used for GPON upstream). This is due to the greater optical losses in this wavelength and, therefore, it can define the condition of the network functioning properly. The received power and total attenuation can be calculated for each optical link by  
\[P_{r}=P_{tx}-\sum P_{at}, \tag{5}\]  
and  
\[\sum P_{at}=\sum\alpha_{c}+\sum\alpha_{s}+\sum\alpha_{sp}+\sum(\alpha_{f, \lambda}L), \tag{6}\]  
in which \(P_{r}\) represents the received power (in dBm), \(P_{tx}\) the transmitted power (in dBm), \(\sum P_{at}\) the sum of link attenuation (in dB), \(\sum\alpha_{c}\) the total loss of each optical connector (in dB), \(\sum\alpha_{s}\) the total loss of each splice (in dB), \(\sum\alpha_{sp}\) the total loss of optical splitters (in dB), \(\sum(\alpha_{f,\lambda}\times L)\) the fiber loss (in dB/km) for the wavelength \(\lambda\) and \(L\) the optical link length (in km). In this paper, we only considered power budget in our proposal. Crosstalk analysis between different wavelength channels and the impact of the transmission impairments (chromatic dispersion, laser phase noise, fiber nonlinearity and limitations due to Kerr nonlinearity) are part of proposals for future works;  
(5.6) Calculate the network total cost for each individual: using the imported cost table, the shortest distances already stored and knowing the positions of all optical splitters, the network cost (in RS) is calculated (for each individual). (6) Select the most fit individuals: In this step, individuals are organized in ascending order according to each value of total deployment cost obtained by using the fitness function (step 5). Part of the most fit individuals are copied and maintained (to guarantee not worse solutions). Then, the most fit individuals (best solutions) are selected and participate in the crossover and mutation procedures (step 7). The rest of the individuals are discarded (do not participate in next generations). (7) Crossover and mutation procedures: The genetic operators are applied to the selected individuals in the previous step (step 6). The crossover function randomly combines characteristics (genes) of two individuals (among those selected). In the crossover operation, genes not drawn will inherit the values of the first selected individual. The mutation function arbitrarily alters one or more characteristics (genes) of the selected individual. Figure 8 shows a graphical example of the crossover and mutation genetic operators applied to hypothetical individuals.