In this thesis, we tackle a significant optimization challenge within Vehicular Ad Hoc Networks (VANETs) by employing a simulated annealing approach. We focus on developing an efficient Vehicle Routing Problem (VRP) algorithm to sift through numerous potential solutions and identify the best one. Our VANET scenario revolves around four distinct vehicles traversing four unique routes. The primary objective is to minimize the total distance covered by these vehicles while ensuring that they visit all designated waypoints. We implement this problem using MATLAB to establish initial routes for each simulation uniquely. Simulated annealing proves to be a valuable tool in optimizing VANETs. The gradual cooling process reduces the likelihood of accepting suboptimal solutions over time, allowing the algorithm to escape local optima and converge towards nearly optimal solutions. Regarding routing protocol parameter configuration, simulated annealing is the technique of choice for identifying the most influential parameters. It evaluates the cost and creates new routes based on neighboring nodes, calculating the cost function for these new routes. Starting from an initial configuration, the algorithm iteratively refines it by introducing random changes, retaining only those that enhance the objective function. Our objective function defines the Quality of Service (QoS) and communication efficiency of the routing protocol. The gradual reduction in the acceptance of less favorable configurations over time is called the annealing schedule, enabling the algorithm to escape local optima and approach nearly optimal designs.

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