New network interface selection based on MADM and multi-objective whale optimization algorithm in heterogeneous wireless networks

Multi-attribute decision-making (MADM) approaches are commonly used to model and solve the network interface selection problem in heterogeneous wireless networks. Despite their importance and advantages to deal with this issue, they suffer from the rank reversal problem (RRP) and the selection of th...

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Bibliographic Details
Published in:The Journal of supercomputing Vol. 79; no. 4; pp. 3580 - 3615
Main Authors: Mefgouda, Brahim, Idoudi, Hanen
Format: Journal Article
Language:English
Published: New York Springer US 01.03.2023
Springer Nature B.V
Subjects:
Algorithms
Analytic hierarchy process
Compilers
Computer Science
Decision making
Hierarchies
Interpreters
Multiple objective analysis
New Trends in Autonomous Systems Engineering
Objectivity
Optimization
Processor Architectures
Programming Languages
Subjectivity
Wireless networks
Heterogeneous wireless networks
Rank reversal problem
Network interface selection
Whale optimization algorithm
Quality of service
Multi-attribute decision making
ISSN:0920-8542, 1573-0484
Online Access:Get full text
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Summary:Multi-attribute decision-making (MADM) approaches are commonly used to model and solve the network interface selection problem in heterogeneous wireless networks. Despite their importance and advantages to deal with this issue, they suffer from the rank reversal problem (RRP) and the selection of the highest-ranking score network without considering the user’s/service requirements. In this paper, a novel method is introduced to solve the MADM limitations. Besides, the weights assignment technique is modelled as a multi-objective problem. Then, an extended version of the Whale Optimization Algorithm is applied to obtain the suitable weights of the decision criteria. The obtained results showed that applying the developed technique with MADM approaches reduces (sometimes avoids completely) the RRP by an average up to 94%, compared to Analytical Hierarchy Process. It also allows meeting the user’s/service requirements by optimizing data rate and packet loss, for streaming services, by an average up to 14.3 (kbps) and 20 × 10 6 (ms) , respectively.
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ISSN:0920-8542
1573-0484
DOI:10.1007/s11227-022-04791-y