Fuzzy teaching learning based optimization approach for solving the QoS-aware web service selection problem in uncertain environments

This paper proposes a new fuzzy optimization approach for the quality of service (QoS)-aware web service selection (QWSS) problem under uncertain QoS parameters. To deal with the ambiguity phenomena of some web services’ QoS characteristics like the execution time attribute, which strongly fluctuate...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Journal of ambient intelligence and humanized computing Jg. 12; H. 12; S. 10667 - 10697
Hauptverfasser: Seghir, Fateh, Khababa, Ghizlane
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Berlin/Heidelberg Springer Berlin Heidelberg 01.12.2021
Springer Nature B.V
Schlagworte:
Algorithms
Ambiguity
Artificial Intelligence
Communications traffic
Computational Intelligence
Engineering
Fuzzy systems
Genetic algorithms
Heuristic
Internet service providers
Linear programming
Machine learning
Methods
Numbers
Optimization
Original Research
Parameter uncertainty
Probability distribution
Quality of service
Random variables
Robotics and Automation
Traffic congestion
User Interfaces and Human Computer Interaction
Web services
QoS uncertainty
Generalized trapezoidal fuzzy numbers
QoS-aware web service selection
Fuzzy multi-objective optimization
TLBO algorithm
ISSN:1868-5137, 1868-5145
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:This paper proposes a new fuzzy optimization approach for the quality of service (QoS)-aware web service selection (QWSS) problem under uncertain QoS parameters. To deal with the ambiguity phenomena of some web services’ QoS characteristics like the execution time attribute, which strongly fluctuates depending on the rates of network traffic and systems congestion; hence, the generalized trapezoidal fuzzy number (GTrFN) is used to represent the uncertainty of the QoS values. Subsequently, the QWSS problem with GTrFN-based QoS proprieties is modeled as a fuzzy optimization (FQWSS) problem. To solve the FQWSS model, we design a new fuzzy teaching learning based optimization (FTLBO) algorithm, for which a local search method and an elitism operator are embedded into it to enhance the exploitation ability and speed up the convergence of FTLBO. The experiment results of comparing the FTLBO algorithm to some recent fuzzy optimization approaches demonstrate its effectiveness and performance in solving the FQWSS problem with different scales.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1868-5137
1868-5145
DOI:10.1007/s12652-020-02879-y