A stop-and-start adaptive cellular genetic algorithm for mobility management of GSM-LTE cellular network users. (15th September 2018)

• Record Type: Journal Article
• Authors: Dahi, Zakaria Abdelmoiz; Alba, Enrique; Draa, Amer
• Is Part Of: Expert systems with applications. Volume 106(2018)
• Page Start: 290
• Page End: 304
• Publication Date: 2018-09-15
• Subjects: Cellular networks -- Cellular Genetic Algorithms -- Adaptation; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2018.02.041
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: An adaptive metaheuristic to solve the user's mobility problem in cellular networks. Uses a mechanism to dynamically stop the algorithm and start it again. Uses a low-complexity formula to adapt dynamically the algorithm's parameters. Tests over 25 realistic networks and comparison against 26 top-ranked algorithms. Experiments showed that the proposed approach is more efficient and less complex. Abstract: 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.