Version 1
: Received: 30 June 2024 / Approved: 8 July 2024 / Online: 9 July 2024 (03:10:15 CEST)
Version 2
: Received: 17 September 2024 / Approved: 18 September 2024 / Online: 20 September 2024 (03:32:19 CEST)
How to cite:
Aazagreyir, P.; Appiahene, P.; Ami-Narh, J.; Brown-Acquaye, W. L. Entrop-Hesitationistic-Fuzz-TOPSIS: A Novel Entropy Based Hesitant Intuitionistic Fuzzy TOPSIS for Web Service Optimization. Preprints2024, 2024070643. https://doi.org/10.20944/preprints202407.0643.v2
Aazagreyir, P.; Appiahene, P.; Ami-Narh, J.; Brown-Acquaye, W. L. Entrop-Hesitationistic-Fuzz-TOPSIS: A Novel Entropy Based Hesitant Intuitionistic Fuzzy TOPSIS for Web Service Optimization. Preprints 2024, 2024070643. https://doi.org/10.20944/preprints202407.0643.v2
Aazagreyir, P.; Appiahene, P.; Ami-Narh, J.; Brown-Acquaye, W. L. Entrop-Hesitationistic-Fuzz-TOPSIS: A Novel Entropy Based Hesitant Intuitionistic Fuzzy TOPSIS for Web Service Optimization. Preprints2024, 2024070643. https://doi.org/10.20944/preprints202407.0643.v2
APA Style
Aazagreyir, P., Appiahene, P., Ami-Narh, J., & Brown-Acquaye, W. L. (2024). Entrop-Hesitationistic-Fuzz-TOPSIS: A Novel Entropy Based Hesitant Intuitionistic Fuzzy TOPSIS for Web Service Optimization. Preprints. https://doi.org/10.20944/preprints202407.0643.v2
Chicago/Turabian Style
Aazagreyir, P., James Ami-Narh and William Leslie Brown-Acquaye. 2024 "Entrop-Hesitationistic-Fuzz-TOPSIS: A Novel Entropy Based Hesitant Intuitionistic Fuzzy TOPSIS for Web Service Optimization" Preprints. https://doi.org/10.20944/preprints202407.0643.v2
Abstract
In the fast-evolving landscape of web services, success relies on optimizing efficiency, reliability, and user satisfaction. Decision-making in real-world applications demands a blend of quantitative (numerical values of quality of Service factors) and qualitative data of experts’ ratings of the quality of service factors. This paper proposes an optimization technique that integrates Hesitant and Intuitionistic Fuzzy Linguistic Terms with TOPSIS, leveraging Entropy Objective weights. There is limited research on Hesitant Intuitionistic Fuzzy Linguistic terms and the need to model fuzziness, haste and intuition in data precisely from experts rating. Thus, this article introduces an Entropy-based model tailored for web service optimization. Using the WS-DREAM dataset, quantitative data was extracted and objective weights were determined through the Entropy algorithm and qualitative evaluations from five experts, employing Hesitant Intuitionistic Fuzzy Linguistic Terms, were integrated. The process involved computing criteria weights and experts' assessment of Quality of Service factors for four services (S1, S2, S3, and S4) to form the Fuzzy Decision Matrix using modified Online output software. Applying the proposed technique to a web service optimization problem validated its effectiveness. The results indicated optimal selection of S1 as the best web service, supported by Coefficient Index values (0.785). This study introduces a novel approach, Entrop – Hesitationistic - Fuzz-TOPSIS, for web service optimization, integrating quantitative and qualitative data through an Entropy-based model. The effectiveness of the proposed technique, validated through application on the WS-DREAM dataset, underscores its potential in addressing real-world decision-making challenges. The optimal selection of S1 as the best web service suggests practical utility. The study encourages further exploration of this technique's applicability to diverse Multiple Criteria Decision Making (MCDM) problems, opening avenues for future research and application.
Keywords
Web service optimization; Quality of Service (QoS); Hesitant Intuitionistic Fuzzy Linguistic Terms; TOPSIS; Entropy Objective weights
Subject
Computer Science and Mathematics, Computer Science
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.