# Software Product Line Maintenance Using Multi-Objective Optimization Techniques
## 6 Results and Discussion
### Fitness Values for Four Objectives Optimization
Table 6's inferences are determined from the results of Table 4.  
\begin{table}
\begin{tabular}{l l c c c c} \hline \hline \multicolumn{2}{c}{**Feature**} & \multicolumn{4}{c}{**Algorithms**} \\ \cline{2-7} \multicolumn{2}{c}{**Models**} & \multicolumn{1}{c}{**IBEA**} & \multicolumn{1}{c}{**MOEAD**} & \multicolumn{1}{c}{**NSGAII**} & \multicolumn{1}{c}{**SPEA2**} \\ \cline{2-7} \multicolumn{2}{c}{} & \(O1\) & \((0.010,0.086,0.291)\) & \(O1\) & \((1,0.010,0.623,0.666)\) & \(O1\) & \((1,0.010,0.666,0.75)\) & \(O1\) & \((1,0.010,0.537,0.666)\) & \(O1\) & \((1,0.010,0.709,0.791)\) \\ Counter & \(O2\) & \((1,0.010,0.086,0.291)\) & \(O2\) & \((1,0.010,0.623,0.666)\) & \(O2\) & \((1,0.010,0.666,0.75)\) & \(O2\) & \((1,0.010,0.537,0.666)\) & \(O2\) & \((1,0.010,0.709,0.791)\) \\ Strike & \(O3\) & \((1,0.010,0.086,0.291)\) & \(O3\) & \((1,0.010,0.107,0.291)\) & \(O3\) & \((1,0.010,0.107,0.291)\) & \(O3\) & \((1,0.010,0.0107,0.291)\) & \(O3\) & \((1,0.010,0.0107,0.291)\) \\  & \(O4\) & \((0.274,0.030,0.365,1)\) & \(O4\) & \((27.01,0.071,0.136,1)\) & \(O4\) & \((0.228,0.051,0.276,1)\) & \(O4\) & \((0.238,0.051,0.219,1)\) & \(O4\) & \((0.260,0.051,0.198,1)\) \\ \hline SPL & \(O1\) & \((0.010,0.358,0.593)\) & \(O1\) & \((0.010,0.312,0.468)\) & \(O1\) & \((0.010,0.343,0.562)\) & \(O1\) & \((0.010,0.458,0.625)\) & \(O1\) & \((1,0.010,0.664,0.656)\) \\ SimSuLES, & \(O2\) & \((1,0.010,0.358,0.593)\) & \(O2\) & \((1,0.010,0.312,0.468)\) & \(O2\) & \((1,0.010,0.343,0.562)\) & \(O2\) & \((1,0.010,0.458,0.625)\) & \(O2\) & \((1,0.010,0.664,0.656)\) \\ PnP & \(O3\) & \((1,0.010,0.305,0.05)\) & \(O3\) & \((1,0.010,0.229,0.50)\) & \(O3\) & \((1,0.010,0.243,0.560)\) & \(O3\) & \((1,0.010,0.244,0.468)\) & \((03,1,0.010,0.236,0.50)\) \\ PnP & \(O4\) & \((0.625,0.030,0.419,1)\) & \(O4\) & \((0.638,0.122,0.279,1)\) & \(O4\) & \((0.654,0.030,0.358,1)\) & \(O4\) & \((0.669,0.408,0.328,1)\) & \(O4\) & \((0.647,0.071,0.312,1)\) \\ \hline \multirow{2}{*}{Smart Home} & \(O1\) & \((0.010,0.419,0.045)\) & \(O1\) & \((0.010,0.389,0.483)\) & \(O1\) & \((0.010,0.551,0.583)\) & \(O1\) & \((0.010,0.507,0.583)\) & \(O1\) & \((0.010,0.441,0.466)\) \\ V2.2 & \(O2\) & \((1,0.010,0.419,0.405)\) & \(O2\) & \((1,0.010,0.389,0.483)\) & \(O2\) & \((1,0.010,0.5  
For the Counter-Strike feature model, it is explored that the MOEA/D, SPEA2, and NSGAIII algorithms originated the best values using the hypervolume and spacing indicators. However, in the case of SPL SimulES, PnP, it is observed that MOEA/D and SPEA2 were the best algorithms for generating the optimal results using hypervolume. However, using the spacing indicator for the SPL SimulES, PnP _FM_, the MOEA/D, SPEA2, and NSGAIII algorithms were capable of producing better values. Similarly, in the case of Smart Homev2.2 _FM_, MOEA/D, SPEA2, and NSGAIII were the best algorithms for generating the optimized results employing hypervolume and spacing. For the Video Player _FM_, the better values using hypervolume originated from MOEA/D, SPEA2, and NSGAIII. However, for the Video Player feature model, better results were obtained only by MOEA/D and SPEA2 using spacing, while in the case of the Model Transformation _FM_, with the same results as those of Smart Homev2.2, the MOEA/D, SPEA2, and NSGAIII algorithms were the best value generators using hypervolume and spacing. In Table 6, it is examined that, for the Coche Ecologic _FM_, MOEA/D, SPEA2, and NSGAIII had the best values using hypervolume, but after utilizing the spacing, only the MOEA/D and SPEA2 algorithms produced good results. To conclude, in Table 6, it is shown that the best optimal results were generated by MOEA/D, SPEA2, and NSGAIII in most of the feature models.