Handling Uncertain Environment Using OWA Operators: An Overview

  • Conference paper
  • First Online:
Next Generation Systems and Networks (BITS-EEE-CON 2022)

Abstract

Fuzzy sets were presented by Zadeh in 1965 as a method of describing and managing data that was not concrete, but rather fuzzy. Fuzzy logic theory gives a mathematical foundation for capturing the inconsistencies inherent in human cognitive processes such as thinking and reasoning. Yager in 1988 presented a unique aggregation approach focusing on ordered weighted averaging (OWA) operators in response to the application of fuzzy logic. It was referred to as membership aggregation cumulative operators by him. Following on from this concept, other academics have highlighted the importance of the OWA weighting vector in a wide variety of implementations such as modelling and decision-making. The objective of this study is to provide an overview of OWA operators while also demonstrating their application in various domains.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
GBP 19.95
Price includes VAT (United Kingdom)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
GBP 127.50
Price includes VAT (United Kingdom)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
GBP 159.99
Price includes VAT (United Kingdom)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free ship** worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Goguen JA, Zadeh LA (1965) Fuzzy sets. Inf Contr 8:338–353. Zadeh LA (1971) Similarity relations and fuzzy orderings. Inf Sci 3:177–200. (1973) J Symbolic Logic 38:656–657

    Google Scholar 

  2. Atanassov KT (1986) Intuitionistic fuzzy sets. Fuzzy Sets Syst 20:87–96

    Google Scholar 

  3. Beliakov G, Pradera A, Calvo T (2007) Aggregation functions: a guide for practitioners. In: Studies in fuzziness and soft computing

    Google Scholar 

  4. Yager RR (1988) On ordered weighted averaging aggregation operators in multicriteria decisionmaking. IEEE Trans Syst, Man, Cybern 18:183–190

    Google Scholar 

  5. Xu Z (2005) An overview of methods for determining OWA weights. Int J Intell Syst 20:843–865

    Google Scholar 

  6. Kishor A, Singh AK, Sonam S, Pal NR (2020) A new family of OWA operators featuring constant orness. IEEE Trans Fuzzy Syst 28:2263–2269

    Google Scholar 

  7. Beliakov G (2003) How to build aggregation operators from data. Int J Intell Syst 18:903–923

    Google Scholar 

  8. León T, Zuccarello P, Ayala G, de Ves E, Domingo J (2007) Applying logistic regression to relevance feedback in image retrieval systems. Pattern Recogn 40:2621–2632

    Google Scholar 

  9. Yager RR (1993) Families of OWA operators. Fuzzy Sets Syst 59:125–148

    Google Scholar 

  10. Mesiar R, Stupnanova A, Yager RR (2015)Generalizations of OWA operators. IEEE Trans Fuzzy Syst 23:2154–2162

    Google Scholar 

  11. Fullér R (1996) OWA operators in decision making

    Google Scholar 

  12. Yi P, Dong Q, Li W (2021) A family of Iowa operators with reliability measurement under interval-valued group decision-making environment. Group Decis Negot 30:483–505

    Google Scholar 

  13. Gorzin M, Hosseinpoorpia M, Parand F-A, Madine SA (2016) A survey on ordered weighted averaging operators and their application in recommender systems. In: 2016 eighth international conference on information and knowledge technology (IKT)

    Google Scholar 

  14. He X, Wu Y, Yu D, Merigó JM (2017) Exploring the ordered weighted averaging operator knowledge domain: a bibliometric analysis. Int J Intell Syst 32:1151–1166

    Google Scholar 

  15. Arya V, Kumar S (2020) A new picture fuzzy information measure based on Shannon entropy with applications in opinion polls using extended Vikor–TODIM approach. Comput Appl Math 39

    Google Scholar 

  16. Fuller R (2007) On obtaining OWA operator weights: a sort survey of recent developments. 2007 In: IEEE International conference on computational cybernetics

    Google Scholar 

  17. O'Hagan M (1988) Aggregating template or rule antecedents in real-time expert systems with fuzzy set logic. In: Twenty-Second Asilomar conference on signals, systems and computers

    Google Scholar 

  18. Fullér R, Majlender P (2001) An analytic approach for obtaining maximal entropy OWA operator weights. Fuzzy Sets Syst 124:53–57

    Google Scholar 

  19. Fullér R, Majlender P (2003) On obtaining minimal variability OWA operator weights. Fuzzy Sets Syst 136:203–215

    Google Scholar 

  20. Beliakov G, James S (2011) Induced ordered weighted averaging operators. In: Recent developments in the ordered weighted averaging operators: theory and practice, pp 29–47

    Google Scholar 

  21. Filev D, Yager RR (1998) On the issue of obtaining OWA operator weights. Fuzzy Sets Syst 94:157–169

    Google Scholar 

  22. Ahn BS, Park KS (2008) Comparing methods for multiattribute decision making with ordinal weights. Comput Oper Res 35:1660–1670

    Google Scholar 

  23. Csiszar O (2021) Ordered weighted averaging operators: a short review. IEEE Syst, Man, Cybern Mag 7:4–12

    Google Scholar 

  24. Yager RR, Beliakov G (2010) OWA operators in regression problems. IEEE Trans Fuzzy Syst 18:106–113

    Google Scholar 

  25. Yager RR (2009) On the dispersion measure of OWA operators. Inf Sci 179:3908–3919

    Google Scholar 

  26. Chang J-R, Ho T-H, Cheng C-H, Chen A-P (2005) Dynamic fuzzy OWA model for group multiple criteria decision making. Soft Comput 10:543–554

    Google Scholar 

  27. Verbiest N, Cornelis C, Herrera F (2013) Owa-FRPS: a prototype selection method based on ordered weighted average fuzzy rough set theory. In: Lecture notes in computer science, pp 180–190

    Google Scholar 

  28. Riza LS, Janusz A, Bergmeir C, Cornelis C, Herrera F, Ślezak D, Benítez JM (2014) Implementing algorithms of rough set theory and fuzzy rough set theory in the R package “Roughsets.” Inf Sci 287:68–89

    Google Scholar 

  29. Chiclana F (2003) A note on the reciprocity in the aggregation of fuzzy preference relations using OWA operators. Fuzzy Sets Syst 137:71–83

    Google Scholar 

  30. Yager RR (1998) Quantifier guided aggregation using OWA operators. Int J Intell Syst 11:49–73

    Google Scholar 

  31. Yager RR (1992) Applications and extensions of OWA aggregations. Int J Man-Mach Stud 37:103–122

    Google Scholar 

  32. Gou X, Liao H, Xu Z, Herrera F (2017) Double hierarchy hesitant fuzzy linguistic term set and Multimoora method: a case of study to evaluate the implementation status of haze controlling measures. Inf Fusion 38:22–34

    Google Scholar 

  33. Herrera-Viedma E, Chiclana F, Herrera F, Alonso S (2007) Group decision-making model with incomplete fuzzy preference relations based on additive consistency. IEEE Trans Syst Man Cybern, Part B (Cybernetics) 37:176–189

    Google Scholar 

  34. Zhou L-G, Chen H-Y (2011) Continuous generalized OWA operator and its application to decision making. Fuzzy Sets Syst 168:18–34

    Google Scholar 

  35. Yager RR (2004) Owa aggregation over a continuous interval argument with applications to decision making.: IEEE Trans Syst Man Cybern, Part B (Cybernetics) 34:1952–1963

    Google Scholar 

  36. Liu P, Wang Y (2015) Interval neutrosophic prioritized OWA operator and its application to multiple attribute decision making. J Syst Sci Complex 29:681–697

    Google Scholar 

  37. ** LS, Qian G (2016) Owa generation function and some adjustment methods for OWA operators with application. IEEE Trans Fuzzy Syst 24:168–178

    Google Scholar 

  38. Merigó JM, Palacios-Marqués D, Soto-Acosta P (2017) Distance measures, weighted averages, OWA operators and Bonferroni means. Appl Soft Comput 50:356–366

    Google Scholar 

  39. Herrera F, Lozano M (2003) Fuzzy adaptive genetic algorithms: design, taxonomy, and future directions. Soft Comput—A Fusion of Foundations, Methodologies and Applications 7:545–562

    Google Scholar 

  40. Derrac J, García S, Herrera F (2014) Fuzzy nearest neighbor algorithms: taxonomy, experimental analysis and prospects. Inf Sci 260:98–119

    Google Scholar 

  41. Torra V, Godo L (2002) Continuous wowa operators with application to defuzzification. Aggreg Oper 159–176

    Google Scholar 

  42. Wang J-W, Chang J-R, Cheng C-H (2005) Flexible fuzzy owa querying method for hemodialysis database. Soft Comput 10:1031–1042

    Google Scholar 

  43. Eckert JJ, Santiciolli FM, Yamashita RY, Corrêa FC, Silva LCA, Dedini FG (2019) Fuzzy gear shifting control optimisation to improve vehicle performance, fuel consumption and engine emissions. IET Contr Theory Appl 13:2658–2669

    Google Scholar 

  44. Garcia-Trivino P, Fernandez-Ramirez LM, Torreglosa JP, Jurado F (2016) Fuzzy logic control for an electric vehicles fast charging station. In: 2016 international symposium on power electronics, electrical drives, automation and motion (SPEEDAM)

    Google Scholar 

  45. Bastian A. Modeling fuel injection control maps using fuzzy logic. In: Proceedings of 1994. IEEE 3rd international fuzzy systems conference

    Google Scholar 

  46. Puente J, Gomez A, Parreno J, de Fuente D (2003) Applying a fuzzy logic methodology to waiting list management at a hospital emergency unit: a case study. Int J Healthc Technol Manag 5:432

    Google Scholar 

  47. Phuong NH, Kreinovich V (2001) Fuzzy logic and its applications in medicine. Int J Med Inf 62:165–173

    Google Scholar 

  48. Imamverdiev YN, Derakshande SA (2011) Fuzzy OWA model for information security risk management. Autom Contr Comput Sci 45:20–28

    Google Scholar 

  49. Vigier HP, Scherger V, Terceño A (2017) An application of OWA operators in fuzzy business diagnosis. Appl Soft Comput 54:440–448

    Google Scholar 

  50. Bowles JB, Peláez CE (1995) Fuzzy logic prioritization of failures in a system failure mode, effects and criticality analysis. Reliab Eng Syst Saf 50:203–213

    Google Scholar 

  51. Spott M, Sommerfeld T, Dorne R (2008) Using fuzzy techniques in business performance management. In: NAFIPS 2008—2008 annual meeting of the North American fuzzy information processing society

    Google Scholar 

  52. Gupta C, Jain A, Joshi N (2018) Fuzzy logic in natural language processing—a closer view. Procedia Comput Sci 132:1375–1384

    Google Scholar 

  53. Lu K, Liao H, Kazimieras Zavadskas E (2021) An overview of fuzzy techniques in supply chain management: Bibliometrics, methodologies, applications and future directions. Technol Econ Dev Econ 27:402–458

    Google Scholar 

  54. Celikbilek C, Erenay B, Suer GA (2015) A fuzzy approach for a supply chain network design problem. In: Annual production and operations management society conference

    Google Scholar 

  55. P S, D N S, B P (2014) Temperature control using fuzzy logic. Int J Instr Contr Syst 4:1–10

    Google Scholar 

  56. Wakami N, Araki S, Nomura H. Recent applications of fuzzy logic to home appliances. In: Proceedings of IECON ‘93—19th annual conference of IEEE industrial electronics

    Google Scholar 

  57. Hopkins M (1995) Three-input, three-output fuzzy logic print quality controller for an electrophotographic printer

    Google Scholar 

  58. Guo J-G, Zhou J (2008) Altitude control system of autonomous airship based on fuzzy logic. In: 2008 2nd international symposium on systems and control in aerospace and astronautics

    Google Scholar 

  59. Rawea A, Urooj S (2015) Design of fuzzy logic controller to drive autopilot altitude in landing phase. In: Advances in intelligent systems and computing, pp 111–117

    Google Scholar 

  60. Sharma T, Singh V, Sudhakaran S, Verma NK (2019) Fuzzy based pooling in convolutional neural network for image classification. In: 2019 IEEE international conference on fuzzy systems (FUZZ-IEEE)

    Google Scholar 

  61. Huang S-F, Cheng C-H (2008) Forecasting the air quality using OWA based time series model. In: 2008 international conference on machine learning and cybernetics

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Saksham Gupta .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Gupta, S., Gupta, A., Agrawal, S. (2023). Handling Uncertain Environment Using OWA Operators: An Overview. In: Bansal, H.O., Ajmera, P.K., Joshi, S., Bansal, R.C., Shekhar, C. (eds) Next Generation Systems and Networks. BITS-EEE-CON 2022. Lecture Notes in Networks and Systems, vol 641. Springer, Singapore. https://doi.org/10.1007/978-981-99-0483-9_40

Download citation

  • DOI: https://doi.org/10.1007/978-981-99-0483-9_40

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-0482-2

  • Online ISBN: 978-981-99-0483-9

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Navigation