Multiobjective metaheuristics for frequency assignment problem in mobile networks with large-scale real-world instances

Purpose - The purpose of this paper is to address a multiobjective FAP (frequency assignment problem) formulation. More precisely, two conflicting objectives the interference cost and the separation cost are considered to characterize FAP as an MO (multiobjective optimization) problem. Design/methodology/approach - The contribution to this specific telecommunication problem in a real scenario follows a recent approach, for which the authors have already accomplished some preliminary results. In this paper, a much more complete analysis is performed, including two well-known algorithms (such as the NSGA-II and SPEA2), with new results, new comparisons and statistical studies. More concretely, in this paper five different algorithms are presented and compared. The popular multiobjective algorithms, NSGA-II and SPEA2, are compared against the Differential Evolution with Pareto Tournaments (DEPT) algorithm, the Greedy Multiobjective Variable Neighborhood Search (GMO-VNS) algorithm and its variant Greedy Multiobjective Skewed Variable Neighborhood Search (GMO-SVNS). Furthermore, the authors also contribute with a new design of multiobjective metaheuristic named Multiobjective Artificial Bee Colony (MO-ABC) that is included in the comparison; it represents a new metaheuristic that the authors have developed to address FAP. The results were analyzed using two complementary indicators: the hypervolume indicator and the coverage relation. Two large-scale real-world mobile networks were used to validate the performance comparison made among several multiobjective metaheuristics. Findings - The final results show that the multiobjective proposal is very competitive, clearly surpassing the results obtained by the well-known multiobjective algorithms (NSGA-II and SPEA2). Originality/value - The paper provides a comparison among several multiobjective metaheuristics to solve FAP as a real-life telecommunication engineering problem. A new multiobjective metaheuristic is also presented. Preliminary results were enhanced with two well-known multiobjective algorithms. To the authors' knowledge, they have never been investigated for FAP.