Multiobjective frequency assignment problem using the MO-VNS and MO-SVNS algorithms

Maximiano, Marisa; Vega‐Rodríguez, Miguel A.; Gómez–Pulido, Juan A.; Sánchez‐Pérez, Juan M.

doi:10.1109/nabic.2009.5393695

Cited by 8 publications

(6 citation statements)

References 7 publications

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“…MVNS is an adaptation of the VNS to the multiobjective framework proposed by Geiger (). This method was successfully applied to different multiobjective optimization problems, such as scheduling problem (Geiger, ), frequency assignment problem (Da Silva Maximiano et al., ), and relay node placement problem (Lanza‐Gutiérrez et al., ). MVNS iteratively constructs the Pareto front.…”

Section: Solution Approachesmentioning

confidence: 99%

Metaheuristics for solving the biobjective single‐path multicommodity communication flow problem

Masri¹,

Krichen

Guitouni

2017

Int Trans Operational Res

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Single‐path multicommodity flow problem (SMCFP) is a well‐known combinatorial optimization problem, in which the flow of each commodity can be transmitted using only one path linking its destination to an appropriate origin within the addressed network. In this paper, we study the SMCFP in a multiobjective context by considering the simultaneous optimization of paths' delay and average reliability. The network is modeled as a finite set of nodes that can communicate using preestablished connections where each connection is characterized by a capacity, a lead time, and a reliability. A node can be an information producer or/and information consumer. The contention problem is solved by assigning a path and a dedicated bandwidth to each flow. The problem is formulated as a biobjective nonlinear optimization problem. This biobjective problem has not been considered in the literature. We design three alternative procedures for approximating the Pareto front. We proposed an MGA based on NSGA‐II, a multiobjective variable neighborhood search and a new distance‐based hybrid metaheuristic. The hybridization integrates a local search into the framework of genetic algorithm to effectively drive the search toward a better approximating of the Pareto front. The propounded algorithms' efficiencies are experimentally investigated on a test bed of instances applied to a planar and a grid network. A comparative study is conducted based on different multiobjective performance indicators.

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Section: Solution Approachesmentioning

confidence: 99%

Metaheuristics for solving the biobjective single‐path multicommodity communication flow problem

Masri¹,

Krichen

Guitouni

2017

Int Trans Operational Res

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“…k max ¼ 9. For every metaheuristic, the parameter tuning was performed identically as in Maximiano et al, 2009aMaximiano et al, , b, 2010. The behavior of SPEA2 is compared to NSGA-II.…”

Section: Parameterizationmentioning

confidence: 99%

“…The Greedy Multiobjective Variable Neighborhood Search (GMO-VNS) algorithm (Maximiano et al, 2009a) is a multiobjective version of the Variable Neighborhood Search (VNS) algorithm that implements the dominance concepts and applies a greedy mutation operator developed specifically for FAP.…”

Section: Greedy Multiobjective Variable Neighborhood Search Algorithm (Gmo-vns)mentioning

confidence: 99%

“…To assess the performance of every metaheuristic, preliminary experiments were carried out to determine the best set of parameter values for each one. Next we present the configuration values obtained from the tuning process: For every metaheuristic, the parameter tuning was performed identically as in Maximiano et al, 2009aMaximiano et al, , b, 2010. The behavior of SPEA2 is compared to NSGA-II.…”

Section: Parameterizationmentioning

confidence: 99%

“…This work aims to enrich the performance comparison among several metaheuristics that have already been proposed to address the multiobjective formulation of FAP. Additionally, a new Multiobjective Artificial Bee Colony (MO-ABC) is presented and it is compared with the previously proposed metaheuristics, such as the Differential Evolution with Pareto Tournaments (DEPT) algorithm (Maximiano et al, 2009b), Greedy Multiobjective Variable Neighbourhood Search (GMO-VNS) or its variant Greedy Multiobjective Skewed Variable Neighbourhood Search (GMO-SVNS) (Maximiano et al, 2009a). Furthermore, our approaches are being compared with the Nondominated Sorting Genetic Algorithm -II (NSGA-II) (Deb et al, 2002) and Strength Pareto Evolutionary Algorithm 2 (SPEA2) (Zitzler et al, 2001).…”

Section: Introductionmentioning

confidence: 99%

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

Maximiano

Vega‐Rodríguez

Gómez–Pulido

et al. 2012

Engineering Computations

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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.

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Application of Differential Evolution to a Multi-Objective Real-World Frequency Assignment Problem

Maximiano

Vega‐Rodríguez

Gómez–Pulido

et al. 2010

Differential Evolution in Electromagnetics

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Multiobjective frequency assignment problem using the MO-VNS and MO-SVNS algorithms

Cited by 8 publications

References 7 publications

Metaheuristics for solving the biobjective single‐path multicommodity communication flow problem

Metaheuristics for solving the biobjective single‐path multicommodity communication flow problem

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

Application of Differential Evolution to a Multi-Objective Real-World Frequency Assignment Problem

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