Optimal routing analysis based on metaheuristic algorithms

Optimal routing is one of the most widely used network problems in transportation planning and its goal is to find the shortest path among the available paths. The shortest path problem focuses on finding the path with the least distance, time, or cost from the source node to the destination node. On the other hand, the time to perform this processing becomes very important in applications related to intelligent and user-based transportation. Various deterministic and heuristic algorithms are used to perform routing. Advanced technologies have led to the development of small sensor nodes that have data processing and data transmission capabilities. These nodes, after being distributed in the target area, create a network that can sense environmental data and react to environmental conditions. Nature is a huge source of inspiration and a very diverse, dynamic, powerful exhibition with complex and attractive phenomena for solving difficult and very complex problems in various sciences. Nature-inspired metaheuristic algorithms, given the importance of optimization in branches of science and the nonlinear fitting of the model and the curve of researchers in operations, should find the optimal allocation of resources in society and industry. These algorithms are intelligent technologies that imitate nature to solve optimization problems and create a new method in computer computing. Although in the past decade, many research efforts have been focused on this area, this science is still young and the results obtained from it are very surprising, and the expansion of the scope and feasibility of nature-inspired algorithms has increased the exploration of new areas of applications and opportunities in computer science, computing, industries, etc. In this research, some routing and meta-heuristic algorithms have been discussed.

Keywords:

Meta-heuristic algorithm , Internet of Things , Wireless network , Clustering , Ant colony system