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Attribute weight computation in a decision making problem by particle swarm optimization

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Abstract

The objective of this paper is to introduce a method for computing weights of attributes in a decision making problem under intuitionistic fuzzy environment. Many weight generation methods exist in the literature under intuitionistic fuzzy setting, but they have some limitations which can be pointed out as: the entropy measures used in entropy weight methods are invalid in many situations and also there are lots of entropy formulae for intuitionistic fuzzy sets, which will be better to use, and thus a confusion may arise; the other weight generation methods may lose some information since it needs to transform the intuitionistic fuzzy decision matrix into an interval-valued decision matrix. This conversion distorts experts original opinions. In this point of view, to overcome these demerits, we develop a weight generation method without changing the original decision information. The proposed method maximizes the average degree of satisfiability and minimizes the average degree of non-satisfiability of each alternative over a set of attributes, simultaneously. This leads to formulate a multi-objective programming problem (MOPP) to compute the final comprehensive value for each alternative. The scenario of an MOPP itself is subjective and can be modeled by fuzzy decision making problem due to the conflicting objectives and the way of human choice on conflict resolution. This problem is solved by using particle swarm optimization scheme, and the evaluation procedure is illustrated by means of a numerical example. This work has also justified the proposed approach by analyzing a comparative study.

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References

  1. Atanassov KT (1986) Intuitionistic fuzzy sets. Fuzzy sets Syst 20(1):87–96

    Article  MathSciNet  MATH  Google Scholar 

  2. Das S, Dutta B, Guha D (2015) Weight computation of criteria in a decision-making problem by knowledge measure with intuitionistic fuzzy set and interval-valued intuitionistic fuzzy set. Soft Comput. doi:10.1007/s00500-015-1813-3

    Article  MATH  Google Scholar 

  3. Wei G (2010) Some induced geometric aggregation operators with intuitionistic fuzzy information and their application to group decision making. Appl Soft Comput 10(2):423–431

    Article  Google Scholar 

  4. Wei G, Zhao X (2011) Minimum deviation models for multiple attribute decision making in intuitionistic fuzzy setting. Int J Comput Intell Syst 4(2):174–183

    Article  Google Scholar 

  5. Wei G, Wang H-J, Lin R, Zhao X (2011) Grey relational analysis method for intuitionistic fuzzy multiple attribute decision making with preference information on alternatives. Int J Comput Intell Syst 4(2):164–173

    Article  Google Scholar 

  6. Nguyen H (2015) A new knowledge-based measure for intuitionistic fuzzy sets and its application in multiple attribute group decision making. Expert Syst Appl 42(22):8766–8774

    Article  Google Scholar 

  7. Tzeng G-H, Huang J-J (2011) Multiple attribute decision making: methods and applications. CRC Press, Boca Raton

    Book  MATH  Google Scholar 

  8. Li D-F, Wang Y-C, Liu S, Shan F (2009) Fractional programming methodology for multi-attribute group decision-making using ifs. Appl Soft Comput 9(1):219–225

    Article  Google Scholar 

  9. Yue Z (2011) A method for group decision-making based on determining weights of decision makers using topsis. Appl Math Modelling 35(4):1926–1936

    Article  MathSciNet  MATH  Google Scholar 

  10. Wei G-W (2008) Maximizing deviation method for multiple attribute decision making in intuitionistic fuzzy setting. Knowl Based Syst 21(8):833–836

    Article  Google Scholar 

  11. Chou S-Y, Chang Y-H, Shen C-Y (2008) A fuzzy simple additive weighting system under group decision-making for facility location selection with objective/subjective attributes. Eur J Oper Res 189(1):132–145

    Article  MATH  Google Scholar 

  12. Yeh C-H, Chang Y-H (2009) Modeling subjective evaluation for fuzzy group multicriteria decision making. Eur J Oper Res 194(2):464–473

    Article  MATH  Google Scholar 

  13. Li D-F (2005) Multiattribute decision making models and methods using intuitionistic fuzzy sets. J Comput Syst Sci 70(1):73–85

    Article  MathSciNet  MATH  Google Scholar 

  14. Lin L, Yuan X-H, Xia Z-Q (2007) Multicriteria fuzzy decision-making methods based on intuitionistic fuzzy sets. J Comput Syst Sci 73(1):84–88

    Article  MathSciNet  MATH  Google Scholar 

  15. Xia M, Xu Z (2012) Entropy/cross entropy-based group decision making under intuitionistic fuzzy environment. Inf Fus 13(1):31–47

    Article  Google Scholar 

  16. Ye J (2010) Fuzzy decision-making method based on the weighted correlation coefficient under intuitionistic fuzzy environment. Eur J Oper Res 205(1):202–204

    Article  MATH  Google Scholar 

  17. Wu J-Z, Zhang Q (2011) Multicriteria decision making method based on intuitionistic fuzzy weighted entropy. Expert Syst Appl 38(1):916–922

    Article  Google Scholar 

  18. Wei G-W (2010) GRA method for multiple attribute decision making with incomplete weight information in intuitionistic fuzzy setting. Knowl Based Syst 23(3):243–247

    Article  Google Scholar 

  19. Wei G-W (2011) Gray relational analysis method for intuitionistic fuzzy multiple attribute decision making. Expert Syst Appl 38(9):11671–11677

    Article  Google Scholar 

  20. Deb K (2001) Multi-objective optimization using evolutionary algorithms, vol 16. John Wiley & Sons, Hoboken

    MATH  Google Scholar 

  21. Sakawa M, Yano H (1985) An interactive fuzzy satisficing method using augmented minimax problems and its application to environmental systems. IEEE Trans Syst Man Cybern 6:720–729

    Article  MATH  Google Scholar 

  22. Goldberg DE, Korb B, Deb K (1989) Messy genetic algorithms: motivation, analysis, and first results. Complex Syst 3(5):493–530

    MathSciNet  MATH  Google Scholar 

  23. Ding S, Xu L, Su C, Jin F (2012) An optimizing method of RBF neural network based on genetic algorithm. Neural Comput Appl 21(2):333–336

    Article  Google Scholar 

  24. Kennedy J (2010) Particle swarm optimization. In: Sammut C, Webb GI (eds) Encyclopedia of machine learning. Springer, Berlin, pp 760–766

    Google Scholar 

  25. Marini F, Walczak B (2015) Particle swarm optimization (PSO). A tutorial. Chemom Intell Lab Syst 149:153–165

    Article  Google Scholar 

  26. Hamza MF, Hwa HJ, Choudhury IA (2017) Recent advances on the use of meta-heuristic optimization algorithms to optimize the type-2 fuzzy logic systems in intelligent control. Neural Comput Appl. doi:10.1007/s00521-015-2111-9

    Article  Google Scholar 

  27. Dorigo M, Caro G, Gambardella L (1999) Ant algorithms for discrete optimization. Artif Life 5(2):137–172

    Article  Google Scholar 

  28. Bellman RE, Zadeh LA (1970) Decision-making in a fuzzy environment. Manag Sci 17(4):B-141

    Article  MathSciNet  Google Scholar 

  29. Zimmermann H-J (1978) Fuzzy programming and linear programming with several objective functions. Fuzzy Sets Syst 1(1):45–55

    Article  MathSciNet  MATH  Google Scholar 

  30. Sakawa M, Yano H (1988) An interactive fuzzy satisficing method for multiobjective linear fractional programming problems. Fuzzy Sets Syst 28(2):129–144

    Article  MathSciNet  MATH  Google Scholar 

  31. Cheng H, Huang W, Zhou Q, Cai J (2013) Solving fuzzy multi-objective linear programming problems using deviation degree measures and weighted max–min method. Appl Math Modelling 37(10):6855–6869

    Article  MathSciNet  MATH  Google Scholar 

  32. Baky IA, Abo-Sinna MA (2013) Topsis for bi-level modm problems. Appl Math Modelling 37(3):1004–1015

    Article  MathSciNet  MATH  Google Scholar 

  33. Deep K, Singh KP, Kansal M, Mohan C (2011) An interactive method using genetic algorithm for multi-objective optimization problems modeled in fuzzy environment. Expert Syst Appl 38(3):1659–1667

    Article  Google Scholar 

  34. Sakawa M, Yauchi K (2001) An interactive fuzzy satisficing method for multiobjective nonconvex programming problems with fuzzy numbers through coevolutionary genetic algorithms. IEEE Trans Syst Man Cybern Part B Cybern 31(3):459–467

    Article  MATH  Google Scholar 

  35. Chandra S, Jayadeva MA (2009) Numerical optimization with applications. Alpha Science International, Oxford

    MATH  Google Scholar 

  36. Berhe HW (2012) Penalty function methods using matrix laboratory (MATLAB). Afr J Math Comput Sci Res 5(13):209–246

    Google Scholar 

  37. Zadeh LA (1965) Fuzzy sets. Inf Control 8(3):338–353

    Article  MATH  Google Scholar 

  38. Xu Z (2007) Intuitionistic fuzzy aggregation operators. IEEE Trans Fuzzy Syst 15(6):1179–1187

    Article  Google Scholar 

  39. Chakraborty D, Guha D, Dutta B (2016) Multi-objective optimization problem under fuzzy rule constraints using particle swarm optimization. Soft Comput. doi:10.1007/s00500-015-1639-z

    Article  Google Scholar 

  40. Li L, Lai KK (2000) A fuzzy approach to the multiobjective transportation problem. Comput Oper Res 27(1):43–57

    Article  MathSciNet  MATH  Google Scholar 

  41. Eiben AE, Smith JE (2003) Introduction to evolutionary computing. Springer, Berlin

    Book  MATH  Google Scholar 

  42. Chen S-M, Tan J-M (1994) Handling multicriteria fuzzy decision-making problems based on vague set theory. Fuzzy Sets Syst 67(2):163–172

    Article  MathSciNet  MATH  Google Scholar 

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Acknowledgements

The first author receives the research grant from the Ministry of Human Resource Development, Government of India. The second author acknowledges the support of Grant ECR/2016/001908.

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Correspondence to Satyajit Das.

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Das, S., Guha, D. Attribute weight computation in a decision making problem by particle swarm optimization. Neural Comput & Applic 31, 2495–2505 (2019). https://doi.org/10.1007/s00521-017-3209-z

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