Solving the location areas management problem with multi-objective evolutionary strategies

The location management is one of the most important tasks in current Public Land Mobile Networks because of the number of mobile subscribers has increased exponentially over the last decade. That is why systems that automatically optimize the operations involved in the location management (subscriber location update and paging) are becoming more necessary. There are several works in which different metaheuristics have been applied to optimize the location management tasks. In these works, the objective functions of the location update and paging were linearly combined into a single objective function with the goal of optimizing these two tasks by using Single-Objective Optimization Algorithms. In this paper, in order to avoid the drawbacks associated with the linear aggregation of the objective functions, we have adapted and modified two Multi-objective Evolutionary Algorithms: Non-dominated Sorting Genetic Algorithm II and Strength Pareto Evolutionary Algorithm 2. Furthermore, we have performed an in-depth analysis of the Location Areas scheme and its relation to the user’s call and mobility patterns. This study concludes that the location areas are as small as possible due to the fast increase of the paging cost, and that the cells with higher mobile activity are located in the center of its location area. Moreover, results show that our algorithms outperform the single-objective optimization algorithms proposed by other authors in the two most complex test networks, as well as the advantages of using a multi-objective approach.