Amalgamated Evolutionary Approach for Optimized Routing in Time Varying Ultra Dense Heterogeneous Networks

Abstract

Routing mechanisms in Ultra-Dense Network (UDNs) are expected to be flexible, scalable, and robust in nature and the establishment of the shortest path between the source and destination pairs will always be a critical challenge. Through this projected work, the optimized shortest route of different source-destination pairs is found using a class of evolutionary optimization algorithms namely PSO, GA, and our proposed hybrid PSO–Genetic Mutation (PSO-GM) algorithm which searches for an optimized solution by representing it as a Shortest Path Routing (SPR) problem. The key attribute of the PSO-GM approach is related to the application of an amalgamated strategy with Gaussian, Cauchy, Levy, Single-point, and Chaos mutation operators. Simulation results and application of the above-mentioned algorithms to the SPR problem in UDNs reveal that the hybrid PSO-GM algorithm provides a comparatively enhanced optimized solution. In the case of the rate of convergence to the theoretical limit, the hybrid PSO-GM gives us 20% better results compared to the PSO and GA.