# An Efficient Framework Model for Optimizing Routing Performance in VANETs
## 4 Optimization-Based Multiple Objective  
### Performance Index Model  
To detect a fit configuration for routing protocol, we introduce a reliable and robust communication cost function to aggregate the weighted cost of each factor in the network to a single scalar value. This value function has been defined to maximize the network connectivity during the path discovery phase and to reduce the routing overhead. Our approach can take the advantage of the communication capabilities among vehicles to provide an accurate status for different urban scenarios. The overall routing performance is influenced by numerous factors such as the degree of node mobility patterns, network density, traffic load, frequent topology changes, and wireless channel effects. Moreover, this function balances the trade-off level between the QoS requirements and the routing limitations during the route discovery mechanism, which achieves the best overall performance in respect to each performance metric. The advantage of this strategy is its ability to independently examine the different metrics for a variety of urban scenarios. In addition, the proposed model provides an efficient method for comparing two or more effective factors, which index the set of response metrics in one function, as shown in Figure 1.  
The performance model represents arithmetically the weighted response of each performance metric, comparing the communication cost with the respective upper and lower bounds of each factor. In the literature, a number of studies address the routing configuration problems in the high dynamic network, however, none of these studies considered balancing routing performance according to the nature of VANET environment. This work maps factors of performance metrics to a single function based on balancing the trade-off between the QoS constraints and the degree of dynamic mobility. This model is carried out by coupling two different stages: an optimization procedure and a simulation stage. We considered Throughput, Packet Delivery Ratio (PDR), End-to-End Delay (E2 ED) and Normalized routing Load (NRL) as the main QoS factors to calculate the communication cost (fitness function). Let \(WT,\ WP,\ WD\) and \(WO\) refer to the weight vectors that index Throughput, PDR, E2 ED and NRL factors, respectively. In this case, the four metrics are equally important, and the weight value assigned to each metric is 0.25. The main reason behind assigning a weight value for each metric is to give an accurate priority for each metric in order to increase its sensitivity to detect any change in network topology [39]. The quality of communication then can be defined as \(U_{T},\ U_{P},\ L_{D}\), and \(L_{O}\), in which \(U_{T}\) represents the throughput metric and \(U_{P}\) represents the PDR metric. \(U_{T}\) and \(U_{P}\) values must be maintained as high as possible. On the other hand, the \(L_{D}\) and \(L_{O}\) metrics refer to E2 ED and NRL respectively, in which they must be as low as possible. When evaluating the factors' value of \(U_{T},\ U_{P},\ L_{D}\), and \(L_{O}\) metrics, the effectiveness of the formula should express the distribution of these response metrics. This is to offer a high level of individual ability to each metric and their global influence on the network performance. To define the best-fit formula that is able to reflect the correct trade-off status between the OLSR configuration and frequent change in network topology, we need to identify the appropriate QoS constraints to control the values for each metric. Let \(V_{T,i};V_{P,i};V_{D,i};V_{O,i}\) be the values that seek to maximize the performance ratio of throughput and PDR, and minimize the ratio value of E2 ED and NLR, respectively. These factors are used to balance the values for the trade-off status between the QoS requirements and rapid topology exchange in urban scenarios.