4.7 Article

A variable neighborhood search approach for cyclic bandwidth sum problem

期刊

KNOWLEDGE-BASED SYSTEMS
卷 246, 期 -, 页码 -

出版社

ELSEVIER
DOI: 10.1016/j.knosys.2022.108680

关键词

Cyclic bandwidth sum; Graph layout problem; Variable Neighborhood search; Greedy algorithm; Combinatorial optimization

资金

  1. Ministerio de Ciencia, Innovacion y Universidades [PGC2018-095322-B-C22, PID2021-125709OA-C22, FPU19/04098]
  2. Comunidad de Madrid
  3. European Regional Development Fund [P2018/TCS-4566]

向作者/读者索取更多资源

In this paper, the authors tackle the Cyclic Bandwidth Sum Problem by proposing a multistart procedure that includes a new greedy constructive algorithm and an intensification strategy based on the Variable Neighborhood Search metaheuristic. The proposed algorithm is evaluated on previously studied instances as well as new instances, and the results show its effectiveness.
In this paper, we tackle the Cyclic Bandwidth Sum Problem, consisting in minimizing the sum of the bandwidth of the edges of an input graph computed in a cycle-structured host graph. This problem has been widely studied in the literature due to its multiple real-world applications, such as circuit design, migration of telecommunication networks, or graph drawing, among others. Particularly, we tackle this problem by proposing a multistart procedure whose main components are a new greedy constructive algorithm and an intensification strategy based on the Variable Neighborhood Search metaheuristic. The constructive procedure introduces two different greedy criteria to determine each step of the construction phase, which can be used for other related problems. Additionally, we illustrate how to perform an efficient exploration of the neighborhood structure by using an alternative neighborhood. Our algorithmic proposal is evaluated over a set of 40 instances previously studied in the literature and over a new proposed set of 66 well-known instances introduced in this paper. The obtained results have been satisfactory compared with the ones obtained by the best previous algorithm in the state of the art. The statistical tests performed indicate that the differences between the methods are significant. (C)2022 Elsevier B.V. All rights reserved.

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