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

机译：具有大型现实世界实例的移动网络中频率分配问题的多目标元启发法

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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.
机译：目的-本文的目的是解决多目标FAP（频率分配问题）的表述。更准确地说，两个相互冲突的目标-干扰成本和分离成本-被认为将FAP表征为MO（多目标优化）问题。设计/方法/方法-在实际情况下对特定电信问题的贡献遵循了最近的方法，为此作者已经完成了一些初步的研究结果。在本文中，我们进行了更为完整的分析，包括两种著名的算法（例如NSGA-II和SPEA2），并提供了新的结果，新的比较和统计研究。更具体地说，本文提出并比较了五种不同的算法。将流行的多目标算法NSGA-II和SPEA2与带有帕累托竞赛的差分演化算法（DEPT），贪婪多目标可变邻域搜索（GMO-VNS）算法及其变体贪婪多目标偏斜可变邻域搜索（GMO-SVNS）进行了比较）。此外，作者还为新的多目标元启发式设计做出了贡献，该设计被称为“多目标人工蜂群”（MO-ABC）。它代表了作者为解决FAP而开发的一种新的元启发法。使用两个互补指标来分析结果：超量指标和覆盖率关系。使用两个大规模的现实世界移动网络来验证几种多目标元启发式方法之间的性能比较。研究结果-最终结果表明，多目标提案极具竞争力，明显超过了著名的多目标算法（NSGA-II和SPEA2）获得的结果。原创性/价值-本文对几种多目标元启发式方法进行了比较，以解决FAP这一现实生活中的电信工程问题。还提出了一种新的多目标元启发式方法。两种著名的多目标算法增强了初步结果。据作者所知，他们从未接受过FAP的调查。

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