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Breakout variable neighbourhood search for the minimum interference frequency assignment problem

Yasmine Lahsinat (Université des Sciences et de la Technologie Houari Boumediene, Algiers, Algeria)
Dalila Boughaci (Université des Sciences et de la Technologie Houari Boumediene, Algiers, Algeria)
Belaid Benhamou (Aix-Marseille Université, Laboratoire LIS, Marseille, France)

Journal of Systems and Information Technology

ISSN: 1328-7265

Article publication date: 16 November 2018

Issue publication date: 30 November 2018

120

Abstract

Purpose

This paper aims to describe two enhancements of the variable neighbourhood search (VNS) algorithm to solve efficiently the minimum interference frequency assignment problem (MI-FAP) which is a major issue in the radio networks, as well as a well-known NP-hard combinatorial optimisation problem. The challenge is to assign a frequency to each transceiver of the network with limited or no interferences at all. Indeed, considering that the number of radio networks users is ever increasing and that the radio spectrum is a scarce and expensive resource, the latter should be carefully managed to avoid any interference.

Design/methodology/approach

The authors suggest two new enhanced VNS variants for MI-FAP, namely, the iterated VNS (It-VNS) and the breakout VNS (BVNS). These two algorithms were designed based on the hybridising and the collaboration approaches that have emerged as two powerful means to solve hard combinatorial optimisation problems. Therefore, these two methods draw their strength from other meta-heuristics. In addition, the authors introduced a new mechanism of perturbation to enhance the performance of VNS. An extensive experiment was conducted to evaluate the performance of the proposed methods on some well-known MI-FAP datasets. Moreover, they carried out a comparative study with other metaheuristics and achieved the Friedman’s non-parametric statistical test to check the actual effect of the proposed enhancements.

Findings

The experiments showed that the two enhanced methods (It-VNS) and (BVNS) achieved better results than the VNS method. The comparative study with other meta-heuristics showed that the results are competitive and very encouraging. The Friedman’s non-parametric statistical test reveals clearly that the results of the three methods (It-VNS, BVNS and VNS) are significantly different. The authors therefore carried out the Nemenyi’s post hoc test which allowed us to identify those differences. The impact of the operated change on both the It-VNS and BVNS was thus confirmed. The proposed BVNS is competitive and able to produce good results as compared with both It-VNS and VNS for MI-FAP.

Research limitations/implications

Approached methods and particularly newly designed ones may have some drawbacks that weaken the results, in particular when dealing with extensive data. These limitations should therefore be eliminated through an appropriate approach with a view to design appropriate methods in the case of large-scale data.

Practical implications

The authors designed and implemented two new variants of the VNS algorithm before carrying out an exhaustive experimental study. The findings highlighted the potential opportunities of these two enhanced methods which could be adapted and applied to other combinatorial optimisation problems, real world applications or academic problems.

Originality/value

This paper aims at enhancing the VNS algorithm through two new approaches, namely, the It-VNS and the BVNS. These two methods were applied to the MI-FAP which is a crucial problem arising in a radio network. The numerical results are interesting and demonstrate the benefits of the proposed approaches in particular BVNS for MI-FAP.

Keywords

Citation

Lahsinat, Y., Boughaci, D. and Benhamou, B. (2018), "Breakout variable neighbourhood search for the minimum interference frequency assignment problem", Journal of Systems and Information Technology, Vol. 20 No. 4, pp. 468-488. https://doi.org/10.1108/JSIT-10-2017-0094

Publisher

:

Emerald Publishing Limited

Copyright © 2018, Emerald Publishing Limited

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