# Improving Waste Management System Efficiency and Mobility with Efficient Path MANET
## 4 Experiment Results and Discussion
### Experiment 2  
In this experiment, the intersection GA-based routing algorithm was applied to find the efficient path based on the multiconstraint's values from source node 0 to a specific destination node. Table 3 presents the constraints and the calculated intersection activation value for the top five chromosomes from the generation during four rounds of iterations. Each chromosome presents a single path from the source node 0 to the destination node. As shown in Table 3, the intersection activation values are affected by the chromosome intersection operation during the processing, which continues for other iterations. The chromosome intersection operation recombined the chromosomes to generate new chromosomes and replaced the worst-case chromosomes in the population. The presented results are the top five chromosomes representing the most efficient paths. The calculated intersection activation value considered the maximum constraint values, which must be acceptable and in compliance with the system requirements. The 4 th and 5 th chromosomes representing paths 3 and 4 were updated in iteration 3. They remain the same in iteration 4. Intersection GA-based routing algorithm selects the paths that have the lowest fitness values based on the intersection activation value results. Therefore, the new offspring replaces the chromosomes that are detrimental to the population. The good chromosomes are kept with the updated values during the iterations that show the ability of the proposed method to find the efficiency of the path. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Iteration** & **Path No.** & **Constraint One** & **Constraint Two** & **Intersection Activation Value** \\ \hline \multirow{4}{*}{1} & Path 0 & 11 & 41 & 41 \\  & Path 1 & 43 & 40 & 43 \\  & Path 2 & 50 & 61 & 61 \\  & Path 3 & 83 & 61 & 83 \\  & Path 4 & 96 & 91 & 96 \\ \hline \multirow{4}{*}{2} & Path 0 & 11 & 41 & 41 \\  & Path 1 & 43 & 40 & 43 \\  & Path 2 & 50 & 61 & 61 \\  & Path 3 & 43 & 40 & 43 \\  & Path 4 & 43 & 40 & 43 \\ \hline \multirow{4}{*}{3} & Path 0 & 11 & 41 & 41 \\  & Path 1 & 43 & 40 & 43 \\ \cline{1-1}  & Path 2 & 50 & 61 & 61 \\ \cline{1-1}  & Path 3 & 25 & 65 & 65 \\ \cline{1-1}  & Path 4 & 83 & 61 & 83 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Constraints of available paths. Furthermore, the quality of the paths based on the two constraints is improved because of the results presented. Figures 10 and 11 show the values of constraints one and two, respectively, and the feasible paths for the same scenario. Path 0 has the minimum value amongst the available paths, that is, 22% from the maximum acceptable value by decreasing 78% in constraint one and 59% in constraint two. Consequently, path 0 is the efficient selected path. Constraint one values of path 1 during iteration 1 are the same and are more efficient than constraint two values by decreasing 60% of the maximum acceptable value of constraint two. Path 2 has the same constraint one values during the second iteration with the maximum acceptable value, whereas constraint two values present 40% of the maximum value. Constraint one values for paths 3 and 4 are improved by 69.8% and 13%, respectively, from the initial value, making path 3 an acceptable path and in compliance with the constraint's restrictions. Additionally, constraint two of path 3 is increased by 6.5% during iteration 3, and that of path 4 is improved by 32% from the initial values. Hence, paths 0, 1, 2, and 3 are the acceptable paths. According to Figure 11, the fitness value for each path is determined by the constraint applied to the path. The intersection activation values calculated remain constant because the constraint values of paths 0, 1, and 2 remain constant throughout the four iterations. However, the intersection activation values of paths 3 and 4 are improved by 21.6% and 13%, respectively. The improved ranking of the individuals produces the acceptable paths at the top of the population list.