# Cooperative Content Caching Framework Using Cuckoo Search Optimization in Vehicular Edge Networks
## 3 Related Work
Furthermore, GA is fast and easy in terms of computational resources but there is no optimal solution in both the shortest path and time. However, ACO is greedier and gives better results with large problem sizes with more ants. Algorithms such as GA and PSO are population based and have more challenging objective problems that may have noisy function evaluations and many global optima. The number of candidate solutions causes robustness to the find the local optima. While cross-entropy and simulated annealing use random numbers for objective functions, and a lot of sampling is required, they are able to handle the problems with local optima [32]. As mentioned in the related work, several optimization techniques have been used in VEN such as GA, PSO, ACO, SA, and cross entropy-based caching. Study [16] shows the comparison of optimization algorithms, in which cuckoo search performs better than GA, PSO, ACO, and SA in terms of global and local search. A better algorithm uses less computation and less iteration. Cuckoo search (CS) is more efficient than GA and SA in random walk and similarity between eggs can produce a better new solution in terms of good mixing ability. The way that an algorithm converges is critically important impacting the speed, hence the minimum computational cost (in terms of time) is needed to find an optimal solution. For intelligent transportation systems, the internet of things (IoTs) is widely used in VANETs; the number of sensors is deployed in vehicles [33]. For effective information routing, finding the shortest path is very challenging. This paper uses a discrete cuckoo search based on Levy flight and random walk against GA to find the optimal path in minimum time spam [34]. The inverse mutation operator is used for exploitation in CS to find the optimal path. If a vehicle wants to pass a message but there is already traffic congestion in the case of a road accident and traffic jam, then a message cannot be broadcast. Thus, a loop-free (single route) is required to deliver a message in high mobility; CS based on levy flight with a random walk can find the path, by flooding the welcome message. But a random walk is not suitable for the continuous optimization problem. Study [35] focused on the discrete cuckoo search algorithm (DCSO) using MPI and Beowulf cluster to improve the quality of the traveling salesman problem for small datasets. Code runs sequentially 'n' times, while parallel in 'n' nodes. MPI and Beowulf cluster are created to achieve parallelism. The results show that sequential programming may be faster for small datasets, but for large datasets, it takes more time. As time complexity of CS is much better than other nature-inspired meta-heuristic algorithms in terms of assigning initial parameters, system dependencies, and storage. We will use a novel cuckoo search algorithm based on Gaussian distribution instead of levy flight; because the random steps that are taken from levy distribution are large. A large step has infinite variance and mean. Gaussian distribution takes smaller steps without missing the optimal path; moreover, it has a high convergence rate [36]. In [37], the authors proposed that CS WKCA (weighted K-medoids clustering algorithm) with clustering provides routing information in vehicular networks to reduce road accidents and develop an intelligent transportation system. The cluster head will change periodically due to mobility; moreover, the nodes that are not in the neighbor are called worst nodes. Therefore, the border node is replaced with a robust one for safety measurement. While in our proposed scheme, clustering information is handled by RSUs in the form of a routing table. There is no need to change the cluster head repeatedly. The given work provides user safety in mobility but does not predict the caching node to provide the data to the expected upcoming user.