A stop-and-start adaptive cellular genetic algorithm for mobility management of GSM-LTE cellular network users

The optimisation of the user tracking process is one of the most challenging tasks in today's advanced cellular networks. In this paper, we propose a new low-complexity adaptive cellular genetic algorithm to solve this problem. The proposed approach uses a torus-like structured population of candidate solutions and regulates interactions inside it by using a bi-dimensional neighbourhood. It also automatically adapts the algorithm's parameters and regenerates the algorithm's population using two algorithmically-light operators. In order to draw reliable conclusions and perform an encompassing assessment, extensive experiments have been conducted on 25 differently-sized realistic networks. The proposed approach has been compared against 26 state-of-the-art algorithms previously designed to solve the mobility management problem, and a thorough statistical analysis of results has been performed. The obtained results have shown that our proposal is more efficient and algorithmically less complex than most of the state-of-the-art solvers.