Analysis of Group Intelligence Machine Learning Optimization Algorithms to enhance IPv6 Addressing

Abstract

The current variety of the Internet Protocol is IPv6 for addressing the networking devices as per the mechanisms proposed by past researchers to minimize the delay and improve the efficiency. It is additionally recognized as a classless addressing scheme that locates computing machines throughout the web so they can be located. Among the mechanisms, the Group Intelligence (GI) algorithms that consists of Evolutionary and Group based Optimization techniques have gained attention of the researchers to contribute an effective optimized solution towards solving an optimization problem.  Thus, the aim of this paper is to study the implementation, features and effectiveness of different GI based metaheuristic machine learning optimization algorithms, so as to contribute in future towards designing and upgrade to a new IPv6 addressing scheme by blending the benefits of the metaheuristic algorithms to find good or near –optimal solutions at a reasonable computation cost in IPv6 network to enhance the execution result of addressing scheme on real time data.