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

Abstract

PurposeThe 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/approachThe 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.FindingsThe 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/valueThe 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.