# Improving Waste Management System Efficiency and Mobility with Efficient Path MANET
## 4 Experiment Results and Discussion
### Experiment 3  
In this experiment, the performance of the intersection GA-based routing algorithm was compared with that of DSR. In this experiment, the source node was fixed at point (x = 0; y = 0), and the destination node was fixed at position (x = 950; y = 950) to find the efficient path between the two end points. Figure 12 shows the intermediate node number of the proposed method and DSR for the selected path, as a result of this experiment. Figure 13 presents the travel distance taken by the selected path between the two end points for the same network. The proposed method selected alternative paths in addition to the efficient selected path, whereas DSR selected only one path. The result indicated that DSR selects a single path between the two end points with 13 intermediate nodes. This number is the same as that of the intermediate nodes of the efficient selected path in the proposed method. Given that the chromosome intersection method was used, the proposed method selected the efficient path in terms of distance. The distance of the efficient path selected by the proposed method is less than that by DSR. However, the transmission time is decreased, and the connection is strengthened. Figure 12: Intermediate node counts. We can understand from the experiments above the most important effects of the chromosome's intersection method on the processing time as follows:  
* The newly generated chromosomes, which results from the recombination operation, display the currently available pathways;
* Checking the chromosomes that have been generated is unnecessary;
* This operation is used in place of two genetic operations that would otherwise be used. The intersection activation function is the most important function in evaluating the chromosomes' performance. The chromosomes were ranked based on their activation function values to identify the most efficient path amongst the available paths in the genome. An abnormally high intersection activation value was considered the worst possible chromosome and should not be passed on to the next generation. Thus, the good chromosomes continue to replicate throughout the processing process until the termination condition is satisfied. These experiments confirmed that the efficiency of the available paths based on the two constraints is improved using the proposed method during the iterations. This research contributes to design the intersection GA-based routing algorithm to improve WMS efficiency and mobility with efficient path MANET. Its goal is to find an efficient path among the available paths. The genetic structure was designed to present the route from the source to the destination and the information required for finding the efficient path. The genetic structure includes the chromosomes that present the path, and the priority of constraints needed for obtaining the efficient path. In addition, the use of the chromosome intersection method helped to minimize the processing time. The scenarios that applied to find the efficient path based on two constraints, show the improvement in the quality of chromosomes during the iterations, which is affected by the priority of constraints. The minimum RIAV of the chromosomes produces the efficient path. The good chromosomes continue during the processing unless it generates a new and better chromosome, then it will take its place based on the RIAV. In addition, the proposed method offers better results for different network sizes and different destination nodes. It has been shown that the priority has an effect on the constraints values of the available paths. The constraints values are far from the maximum bounded values, which present efficient paths in the scenario that concerns priority constraints more than other scenarios that do not concern the priority of constraints. Moreover, the performance comparison of the proposed method to DSR showed that the proposed method provides an efficient path in terms of the number of intermediate nodes and distance.