Fuzzy Systems and its Applications

Fuzzy Systems and its Applications

Pythagorean Fuzzy Superhypergraphs

Document Type : Original Article

Authors
1 Faculty of Mathematics - Payame Noor University
2 Payame Noor Unoversity, Iran, Tehran
10.22034/jfsa.2026.572395.1298
Abstract
In this paper, Pythagorean fuzzy supersupergraphs are introduced for modeling complex supernetworks. The concepts of supersupervertices, aberrials, and Pythagorean fuzzy links are defined. Based on these concepts, important classes of soft, rough, and smooth supersupergraphs are presented. Then, fuzzy golden intervals are introduced for the existence or non-existence of complements of these types of superstructures and for constructing complements of Pythagorean fuzzy supersupergraphs. An algorithm for calculating this golden interval is presented. The concept of fuzzy entropy for Pythagorean fuzzy supersupergraphs is also introduced. Methods for reducing the entropy of superaberrials using golden intervals and the concept of Sari complement are investigated. Finally, an algorithm is designed that reduces and optimizes the fuzzy entropy of Pythagorean fuzzy supersupergraphs, and practical applications of these concepts are discussed.
Keywords

[1]    M. Akram, F. Wasim, J. C. R. Alcantud, A. N. Al‑Kenani, Multi-criteria Optimization Technique with Complex Pythagorean Fuzzy N-soft Information. Int J Comput Intell Syst, 14(167) (2021), 1-24. https://doi.org/10.1007/s44196-021-00008-x
[2]    A. DeLuca, and S. Termini, A definition of a non-probabilistic entropy in the setting of fuzzy sets, Inform. Control, 20(4) (1972), 301-312.
[3]    M. Hamidi, F. Smarandache, and E. Davneshvar, Spectrum of Superhypergraphs via Flows, J. Math., 2022 (2022), 12 pages.
 
[4]    M. Hamidi, F. Smarandache and M.h Taghinezhad, Decision Making Based on Valued Fuzzy Superhypergraphs, Computer Modeling in Engineering & Sciences, 138(2) (2024), 1907-1923. https://doi.org/10.32604/cmes.2023.030284.
[5]    B. Kosko, Fuzzy entropy and conditioning, Information Sciences, 40( 2) (1986), Pages 165-174.
[6]    B. Kosko. Fuzziness vs. probability. International Journal of General Systems, 17(2-3) (1990), 211– 240.
[7]    S. Naz, S. Ashraf, M. Akram, A novel approach to decision-making with Pythagorean fuzzy information. Mathematics, 6 2018, 1-28.
[8]    F. Smarandache, Introduction to the n-SuperHyperGraph-the most general form of graph today, Neutrosophic Sets Syst., 48 (2022), 483-485.
[9]    M. Shen Yang and Z. Hussain, Fuzzy Entropy for Pythagorean Fuzzy Sets with Application to Multicriterion Decision Making, Complexity Volume 2018, Article ID 2832839, 14 pages https://doi.org/10.1155/2018/2832839.
[10]    R. Verma, J. M. Merigó, M. Sahni, Pythagorean fuzzy graphs: Some results, arXiv preprint arXiv:1806.06721, 2018, pp. 1–56.
[11]    R. R. Yager, Pythagorean fuzzy subsets, In: 2013 Joint IFSA World Congress and NAFIPS Annual Meeting (IFSA/NAFIPS), (2013), 36286152.
[12]    L. Zhang, S. Sun, R. Wang, C. Suo, Graph-based multi-attribute decision-making method with new fuzzy information measures. Complex Intell. Syst. 11(293) (2025), 1-21. https://doi.org/10.1007/s40747-025-01879-9.
Volume 9, Issue 1 - Serial Number 18
Open Access Statement
June 2026
Pages 153-179

  • Receive Date 27 January 2026
  • Revise Date 19 June 2026
  • Accept Date 05 August 2026