Review and Comparison of Bipolar Fuzzy Number Types.

Document Type : Review article

Authors

Department of Mathematics, Gonbad Kavous University,, Gonbad Kavous, Iran

Abstract

Many human decision-making processes such as competition, hostility, partnership, etc. are based on bipolar or two-sided thinking, which are positive on one side and negative on the other. Binary logic only includes zero and one, and fuzzy logic is a tool for representing ambiguity and uncertainty, but both have shortcomings in modeling bipolar relationships, as they are limited to the range from zero to one, which is suitable for representing unipolar models in nature. Bipolar fuzzy sets are actually a combination of ambiguity and its polarity, which is a tool for representing subjects of nature that have both positive or negative poles. Therefore, in this article, we will introduce bipolar fuzzy sets and numbers, and then, by defining addition and scalar multiplication for triangular bipolar fuzzy numbers, we will introduce a new ranking based on the centroid for this category of numbers, and prove the properties of the new method by stating several theorems and propositions. Finally, numerical examples demonstrate the practical applications of the proposed ranking.

Keywords

Main Subjects


[1] Akram, M., Ali, M. and Allahviranloo, T. (2020). Certain methods to solve bipolar fuzzy linear system of equations. Computational and Applied Mathematics, 39(3), 213.
 
[2] Akram, M., Allahviranloo, T., Pedrycz, W. and Ali, M. (2021). Methods for solving LR­bipolar fuzzy linear systems. Soft Computing, 25(1), 85­108.
 
[3] Akram, M. and Arshad, M. (2019). A novel trapezoidal bipolar fuzzy TOPSIS method for group decision­making. Group Decision and Negotiation, 28, 565­584.
 
[4] Akram, M., Muhammad, G. and Allahviranloo, T. (2019). Bipolar fuzzy linear system of equations. Computational and Applied Mathematics, 38, 1­29.
 
[5] Akram, M., Shumaiza and Rodríguez Alcantud, J. C. (2023). Extended PROMETHEE Method with Bipolar Fuzzy Sets Multi­criteria Decision Making Methods with Bipolar Fuzzy Sets (pp. 151­175): Springer.
 
[6] Akram, M., Shumaiza and Rodríguez Alcantud, J. C. (2023). TOPSIS and ELECTRE I Methodologies: Bipolar Fuzzy Formulations Multi­criteria Decision Making Methods with Bipolar Fuzzy Sets (pp. 1­34): Springer.
 
[7] Akram, M., Shumaiza and Rodríguez Alcantud, J. C. (2023). VIKOR Method with Trapezoidal Bipolar Fuzzy Sets Multi­criteria Decision Making Methods with Bipolar Fuzzy Sets (pp. 67­91): Springer.
 
[8] Alolaiyan, H., Mateen, M. H., Pamucar, D., Mahmmod, M. K. and Arslan, F. (2021). A certain structure of bipolar fuzzy subrings. Symmetry, 13(8), 1397.
 
[9] Ashraf, S., Abdullah, S., Aslam, M., Qiyas, M. and Kutbi, M. A. (2019). Spherical fuzzy sets and its representation of spherical fuzzy t­norms and t­conorms. Journal of Intelligent Fuzzy Systems, 36(6), 6089­6102.
 
[10] Atanassov, K. T. and Atanassov, K. T. (1999). Intuitionistic fuzzy sets: Springer.
 
[11] Babakordi, F. (2022). Market Equilibrium Point Analysis by a Fuzzy Approach. Journal of Operational Research In Its Applications (Applied Mathematics)­Lahijan Azad University, 19(3), 17­28.
 
[12] Babakordi, F. (2023). An Efficient Method for Solving the Fuzzy AH1N1/09 Influenza Model Using the Fuzzy Atangana­Baleanu­Caputo Fractional Derivative. Fuzzy Optimization and Modeling Journal, 4(1), 57­70.
 
[13] Babakordi, F. (2023). An Efficient Method for Solving the Fuzzy AH1N1/09 Influenza Model Using the Fuzzy Atangana­Baleanu­Caputo Fractional Derivative. Fuzzy Optimization and Modeling Journal, 4(1), 57­70.
 
[14] Babakordi, F. and Taghi­Nezhad, N. (2021). Introducing hesitant fuzzy equations and determining market equilibrium price. Control and Cybernetics, 50.
 
[15] Ghanbari, R., Ghorbani­Moghadam, K. and Mahdavi­Amiri, N. (2018). A Direct Method to Compare Bipolar LR Fuzzy Numbers. Advances in Fuzzy Systems.
 
[16] Ghanbari, R., Ghorbani­Moghadam, K. and Mahdavi­Amiri, N. (2019). Duality in bipolar fuzzy number linear programming problem. Fuzzy Information and Engineering, 11(2), 175­185.
 
[17] Gong, S. and Hua, G. (2023). Bipolar interval­valued fuzzy set in graph and hypergraph settings. Journal of Intelligent Fuzzy Systems, 44(2), 1755­1767.
 
[18] Han, Y., Shi, P. and Chen, S. (2015). Bipolar­valued rough fuzzy set and its applications to the decision information system. IEEE Transactions on Fuzzy Systems, 23(6), 2358­2370.
 
[19] Jana, C., Pal, M. and Wang, J. (2019). A robust aggregation operator for multi­criteria decisionmaking method with bipolar fuzzy soft environment. Iranian Journal of Fuzzy Systems, 16(6), 1­16.
 
[20] Jeevaraj, S. (2021). Ranking of Trapezoidal Bipolar Fuzzy Numbers Based on a New Improved Score Function Fuzzy Systems and Data Mining VII (pp. 41­53): IOS Press.
 
[21] Krishnaveni, J., Rajalakshmi, B. and Santhanaathiveeralakshmi, V. (2022). Fuzzy bipolar sets in rank­ordering system. Paper presented at the AIP Conference Proceedings.
 
[22] Kutlu Gündoğdu, F. and Kahraman, C. (2019). Spherical fuzzy sets and spherical fuzzy TOPSIS method. Journal of Intelligent Fuzzy Systems, 36(1), 337­352.
 
[23] Lu, M. and Busemeyer, J. R. (2014). Do traditional chinese theories of Yi Jing (’Yin­Yang’and Chinese medicine go beyond western concepts of mind and matter. Mind and Matter, 12(1), 37­59.
 
[24] Mandal, W. A. (2021). Bipolar pythagorean fuzzy sets and their application in Multiattribute decision making problems. Annals of Data Science, 1­33.
 
[25] Mehmood, M. A., Akram, M., Alharbi, M. G. and Bashir, S. (2021). Optimization of LR­type fully bipolar fuzzy linear programming problems. Mathematical Problems in Engineering, 2021, 1­36.
 
[26] Mehmood, M. A., Akram, M., Alharbi, M. G. and Bashir, S. (2021). Solution of fully bipolar fuzzy linear programming models. Mathematical Problems in Engineering 2021, 1­31.
 
[27] Princy, R. and Mohana, K. (2019). Spherical bipolar fuzzy sets and its application in multi criteria decision making problem. Journal of New Theory(32), 58­70.
 
[28] Singh, P. K. (2022). Bipolar fuzzy concepts reduction using granular­based weighted entropy. Soft Computing, 26(19), 9859­9871.
 
[29] Sriram, S. and Sivaranjani, K. (2023). Operations on Bipolar Pythagorean Fuzzy Matrix. Paper presented at the 2023 Third International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT).
 
[30] Taghi­Nezhad, N. and Babakordi, F. (2023). Fully hesitant parametric fuzzy equation. Soft Computing, 1­12.
 
[31] Taghi­nezhad, N., Naseri, H., Khalili Goodarzi, F. and Taleshian Jelodar, F. (2015). Reactive Scheduling Presentation for an Open Shop problem Focused on jobs’ due Dates. Journal of Production and Operations Management, 6(2), 95­112.
 
[32] Taghi­Nezhad, N. A. (2019). The p­median problem in fuzzy environment: proving fuzzy vertex optimality theorem and its application. Soft Computing, 23(22), 11399­11407.
 
[33] Taghi­Nezhad, N. A. (2022). A revisit of the proposed model for solving fuzzy linear fractional programming problem. International Journal of Mathematics in Operational Research, 23(2), 215­231.
 
[34] Taghi­Nezhad, N. A. (2023). Fuzzy Linear Fractional Programming for Container Transportation Optimization. Iranian Journal of Marine Science and Technology.
 
[35] Taleshian, F., Fathali, J. and Allah Taghi­Nezhad, N. (2022). Finding the absolute and vertex center of a fuzzy tree. Transportation Letters, 14(6), 591­599.
 
[36] Taleshian, F., Fathali, J. and Taghi­Nezhad, N. A. (2018). Fuzzy majority algorithms for the 1­median and 2­median problems on a fuzzy tree. Fuzzy Information and Engineering, 10(2), 1­24.
 
[37] Wang, Y.­M., Yang, J.­B., Xu, D.­L. and Chin, K.­S. (2006). On the centroids of fuzzy numbers. Fuzzy Sets and Systems, 157(7), 919­926. doi:https://doi.org/10.1016/j.fss.2005.11.006
 
[38] Yager, R. R. (2013). Pythagorean membership grades in multicriteria decision making. IEEE Transactions on Fuzzy Systems, 22(4), 958­965.
 
[39] Yiarayong, P. (2021). A new approach of bipolar valued fuzzy set theory applied on semigroups. International Journal of Intelligent Systems, 36(8), 4415­4438.
 
[40] Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8, 338 ­ 353.
 
[41] Zhang, W.­R. (1998). (Yin)(Yang) bipolar fuzzy sets. Paper presented at the 1998 IEEE international conference on fuzzy systems proceedings. IEEE world congress on computational intelligence (Cat. No. 98CH36228).
 
[42] Zhang, W.­R. (2013). Bipolar quantum logic gates and quantum cellular combinatorics–a logical extension to quantum entanglement. Journal of Quantum Information Science, 3(2), 93.
 
[43] Zhang, W.­R. (2016). G­CPT Symmetry of Quantum Emergence and Submergence–An Information Conservational Multiagent Cellular Automata Unification of CPTSymmetry and CP Violation for Equilibrium­Based Many­World Causal Analysis of Quantum Coherence and Decoherence. Journal of Quantum Information Science, 6(2), 62.
 
[44] Zhang, W.­R., Pandurangi, A. K., Peace, K. E., Zhang, Y.­Q. and Zhao, Z. (2011). MentalSquares: a generic bipolar support vector machine for psychiatric disorder classification, diagnostic analysis and neurobiological data mining. International journal of data mining and bioinformatics, 5(5), 532­557.
 
[45] Zhang, W.­R. and Peace, K. E. (2014). Causality is logically definable—toward an equilibriumbased computing paradigm of quantum agents and quantum intelligence (QAQI)(Survey and research). Journal of Quantum Information Science, 4, 227­268.
 
[46] Zhang, W.­R., Zhang, J. H., Shi, Y. and Chen, S.­S. (2009). Bipolar linear algebra and YinYangN­element cellular networks for equilibrium­based biosystem simulation and regulation. Journal of Biological Systems, 17(04), 547­576.
 
[47] Zhang, W.­R. and Zhang, L. (2004). YinYang bipolar logic and bipolar fuzzy logic. Information sciences, 165(3­4), 265­287.
 
[48] Zhang, X. and Xu, Z. (2014). Extension of TOPSIS to multiple criteria decision making with Pythagorean fuzzy sets. International Journal of Intelligent Systems, 29(12), 1061­1078.