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Intuitionistic Fuzzy Sets and Dynamic Programming for Multi-objective Non-linear Programming Problems

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Abstract

The practical applications of engineering designs are often concerned by the presence of interrelated decisions. Naturally, these decisions are induced by solving many conflicting and incommensurable objectives. To deal with the interlinked decisions as well as the paradox natures among objectives, this paper presents an integrated approach based on dynamic programming approach (DPA) and intuitionistic fuzzy set (IFS) denoted as DPA-IFS for solving multi-objective optimization problem (MOOP). In DPA-IFS, the principle of DPA aims to generate an efficient solution of MOOP. In contrast, IFS aims to handle conflicting natures among the objective functions by means of the satisfaction (maximization the degree of membership) and dissatisfaction (minimization the degree of non-membership) concepts. The illustration is investigated by numerical illustrations taken from the literature. Furthermore, a new closeness strategy-based distance function is introduced to measure the worth of a satisfactory solution. The proposed methodology is validated on the IEEE 30-bus with six generators as a real test paradigm in the electrical power system. The results obtained by the DPA-IFS show the superior results than those obtained by different approaches.

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References

  1. Zadeh, L.A.: Fuzzy sets. Inf. Control 8, 338–353 (1965)

    Article  MATH  Google Scholar 

  2. Atanassov, K.: Intuitionistic fuzzy interpretations of Barcan formulas. Inf. Sci. 460, 469–475 (2018)

    Article  MathSciNet  MATH  Google Scholar 

  3. Atanassov, K.: Intuitionistic fuzzy sets. Fuzzy Sets Syst. 20, 87–96 (1986)

    Article  MATH  Google Scholar 

  4. Angelov, P.P.: Optimization in an intuitionistic fuzzy environment. Fuzzy Sets Syst. 86, 299–306 (1997)

    Article  MathSciNet  MATH  Google Scholar 

  5. Vishnu, S., Shiv, P.: Modeling and optimization of multi-objective programming problems in intuitionistic fuzzy environment: optimistic, pessimistic and mixed approaches. Expert Syst. Appl. 102, 143–157 (2018)

    Article  Google Scholar 

  6. Bellman, R.E., Zadeh, L.A.: Decision making in a fuzzy environment. Manage. Sci. 17, 141–164 (1970)

    Article  MathSciNet  MATH  Google Scholar 

  7. Ismat, B., Tabasam, R.: Multi-criteria trapezoidal valued intuitionistic fuzzy decision making with Choquet integral based TOPSIS. OPSEARCH 51, 98–129 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  8. Abo-Sinna, M.A., Abo-Elnaga, Y.Y., Mousa, A.A.: An interactive dynamic approach based on hybrid swarm optimization for solving multiobjective programming problem with fuzzy parameters. Appl. Math. Model. 38, 2000–2014 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  9. Stanciulescu, C.V., Fortemps, P., Installe, M., Wertz, V.: Multiobjective fuzzy linear programming problems with fuzzy decision variables. Eur. J. Oper. Res. 149, 654–675 (2003)

    Article  MathSciNet  MATH  Google Scholar 

  10. Oumayma, B., El-Ghazali, T., Nahla, B.A.: A generic fuzzy approach for multi-objective optimization under uncertainty. Swarm and Evolutionary Computation 40, 166–183 (2018)

    Article  MATH  Google Scholar 

  11. Duran, T.M.: Taylor series approach to fuzzy multi-objective linear fractional programming. Inf. Sci. 178, 1189–1204 (2008)

    Article  MATH  Google Scholar 

  12. Huang, C.H.: An effective method for a fuzzy multiobjective program with Quasiconcave membership functions and fuzzy coefficients. Int. J. Fuzzy Syst. 16, 256–264 (2014)

    MathSciNet  Google Scholar 

  13. Samir, D., Roy, T.K.: Intuitionistic fuzzy goal programming technique for solving non linear multi-objective structural problem. J. Fuzzy Set Valued Anal 3, 179–193 (2015)

    MATH  Google Scholar 

  14. Samir, D., Roy, T.K.: Optimized solution of two bar truss design using intuitionistic fuzzy optimization technique. Int. J. Inf. Eng. Electr. Bus. 6, 45–51 (2014)

    Google Scholar 

  15. Meng, F., Tang, J., Hamido, F.: Linguistic intuitionistic fuzzy preference relations and their application to multi-criteria decision making. Inf. Fusion 46, 77–90 (2019)

    Article  Google Scholar 

  16. Sirbiladze, G., Khutsishvili, I., Midodashvili, B.: Associated immediate probability intuitionistic fuzzy aggregations in MCDM. Comput. Ind. Eng. 123, 1–8 (2018)

    Article  Google Scholar 

  17. Liu, H.W., Wang, G.J.: Multi-criteria decision-making methods based on intuitionistic fuzzy sets [J]. Eur. J. Perational Res. 179, 220–233 (2007)

    Article  MATH  Google Scholar 

  18. Jafarian, E., Razmi, J., Baki, M.F.: A flexible programming approach based on intuitionistic fuzzy optimization and geometric programming for solving multi-objective nonlinear programming problems. Expert Syst. Appl. 93, 245–256 (2018)

    Article  Google Scholar 

  19. Mahapatra, G.S.: Intuitionistic fuzzy multi-objective mathematical programming on reliability optimization Model. Int. J. Fuzzy Syst. 12, 259–266 (2010)

    MathSciNet  Google Scholar 

  20. Mirzaei, N., Mahmoodirad, A., Niroomand, S.: An uncertain multi-objective assembly line balancing problem: a credibility-based fuzzy modeling approach. Int. J. Fuzzy Syst. 21(8), 2392–2404 (2019)

    Article  MathSciNet  Google Scholar 

  21. Sakawa, M., Yano, H., Sawada, K.: Primal decomposition method for multiobjective structured nonlinear programs with fuzzy goals. Cybern. Syst. 26(4), 413–426 (1995)

    Article  MATH  Google Scholar 

  22. Lachhwani, K.: Fuzzy goal programming approach to multi objective quadratic programming problem. Proc. Natl. Acad. Sci., India, Sect. A 82(4), 317–322 (2012)

    Article  MathSciNet  Google Scholar 

  23. Abo-Sinna, M.A., Amer, A.H.: Extensions of TOPSIS for multi-objective large-scale nonlinear programming problems. Appl. Math. Comput. 162(1), 243–256 (2005)

    MathSciNet  MATH  Google Scholar 

  24. Abo-Sinna, M.A., Amer, A.H., Ibrahim, A.S.: Extensions of TOPSIS for large scale multi-objective non-linear programming problems with block angular structure. Appl. Math. Model. 32(3), 292–302 (2008)

    Article  MATH  Google Scholar 

  25. Kumar, P.S.: Algorithms for solving the optimization problems using fuzzy and intuitionistic fuzzy set. Int. J. Syst. Assur. Eng. Manag 11(1), 189–222 (2020)

    Article  Google Scholar 

  26. Atan, O., Kutlu, F., Castillo, O.: Intuitionistic fuzzy sliding controller for uncertain hyperchaotic synchronization. Int. J. Fuzzy Syst. 22(5), 1430–1443 (2020)

    Article  MathSciNet  Google Scholar 

  27. Singh, S.K., Yadav, S.P.: Fuzzy programming approach for solving intuitionistic fuzzy linear fractional programming problem. Int. J. Fuzzy Syst. 18(2), 263–269 (2016)

    Article  MathSciNet  Google Scholar 

  28. Vidhya, R., Hepzibah, R.I.: A comparative study on interval arithmetic operations with intuitionistic fuzzy numbers for solving an intuitionistic fuzzy multi–objective linear programming problem. Int. J. Appl. Math. Comput. Sci. 27(3), 563–573 (2017)

    Article  MathSciNet  MATH  Google Scholar 

  29. Bharati, S.K., Singh, S.R.: Solution of multiobjective linear programming problems in interval-valued intuitionistic fuzzy environment. Soft. Comput. 23(1), 77–84 (2019)

    Article  MATH  Google Scholar 

  30. Bharati, S.K., Nishad, A.K., Singh S.R.: Solution of multi-objective linear programming problems in intuitionistic fuzzy environment. In: Proceedings of the Second International Conference on Soft Computing for Problem Solving (SocProS 2012), December 28–30, 2012 (pp. 161–171), (2014). Springer, New Delhi

  31. Nishad, A.K., Singh, S.R.: Solving multi-objective decision making problem in intuitionistic fuzzy environment. Int. J. Syst. Assur. Eng. Manag. 6(2), 206–215 (2015)

    Article  Google Scholar 

  32. Niroomand, S.: A multi-objective based direct solution approach for linear programming with intuitionistic fuzzy parameters. J. Intell. Fuzzy Syst. 35(2), 1923–1934 (2018)

    Article  Google Scholar 

  33. Rouhbakhsh, F.F., Ranjbar, M., Effati, S.: Multi objective programming problem in the hesitant fuzzy environment. Appl Intell. (2020). https://doi.org/10.1007/s10489-020-01682-8

    Article  Google Scholar 

  34. Razmi, J., Jafarian, E., Amin, S.H.: An intuitionistic fuzzy goal programming approach for finding pareto-optimal solutions to multi-objective programming problems. Expert Syst. Appl. 65, 181–193 (2016)

    Article  Google Scholar 

  35. Singh, S.K., Yadav, S.P.: Intuitionistic fuzzy multi-objective linear programming problem with various membership functions. Ann. Oper. Res. 269(1–2), 693–707 (2018)

    Article  MathSciNet  MATH  Google Scholar 

  36. Stanojević, B., Stanojević, M.: On fuzzy solutions to a class of fuzzy multi-objective linear optimization problems. In: Advances in Operational Research in the Balkans (pp. 63–76) Springer, Cham (2020)

  37. Tsao, Y.C., Thanh, V.V.: A multi-objective fuzzy robust optimization approach for designing sustainable and reliable power systems under uncertainty. Applied Soft Computing, 106317 (2020)

  38. Malik, M., Gupta, S., K.: Goal programming technique for solving fully interval-valued intuitionistic fuzzy multiple objective transportation problems. Soft Computing, 1–23 (2020)

  39. Mahajan, S., Gupta, S., K.: On fully intuitionistic fuzzy multiobjective transportation problems using different membership functions. Annals of Operations Research, 1–31(2019)

  40. Rizk-Allah, R.M., Abo-Sinna, M.: Integrating reference point, Kuhn-Tucker conditions and neural network approach for multi-objective and multi-level programming problems. OPSEARCH 54, 663–683 (2017)

    Article  MathSciNet  MATH  Google Scholar 

  41. Mine, H., Fukushima, M.: Decomposition of multiple criteria mathematical programming by dynamic programming. Int. J. Syst. Sci. 10, 557–566 (1979)

    Article  MathSciNet  MATH  Google Scholar 

  42. Yalian, Y., Huanxin, P., Xiaosong, H., Yonggang, L., Cong, H., Dongpu, C.: Fuel economy optimization of power split hybrid vehicles: a rapid dynamic programming approach. Energy 166, 929–938 (2019)

    Article  Google Scholar 

  43. Yager, R.R.: Some aspects of intuitionistic fuzzy sets. Fuzzy Optim. Decis. Making 8, 67–90 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  44. Abo-Sinna, M.A., Hussein, M.L.: An algorithm for decomposing the parametric space in multiobjective dynamic programming problems. Eur. J. Oper. Res. 73(3), 532–538 (1994)

    Article  MATH  Google Scholar 

  45. Binh, T., Korn, U.: MOBES: a multiobjective evolution strategy for constrained optimization problems. In: Proceedings of the third international conference on genetic algorithms, Czech Republic 176–182 (1997)

  46. Maghawry, A., Hodhod, R., Omar, Y., Kholief, M.: An approach for optimizing multi-objective problems using hybrid genetic algorithms. Soft Computing, 1–17 (2020)

  47. Hu, C.F., Teng, C.J., Li, S.Y.: A fuzzy goal programming approach to multi-objective optimization problem with priorities. Eur. J. Oper. Res. 176(3), 1319–1333 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  48. Abido, M.A.: A novel multiobjective evolutionary algorithm for environmental/economic power dispatch. Electr. Power Syst. Res. 65(1), 71–81 (2003)

    Article  Google Scholar 

  49. Rizk-Allah, R.M., El-Sehiemy, R.A., Wang, G.G.: A novel parallel hurricane optimization algorithm for secure emission/economic load dispatch solution. Appl. Soft Comput. 63, 206–222 (2018)

    Article  Google Scholar 

  50. El-Sehiemy, R.A., Rizk-Allah, R.M., Attia, A.F.: Assessment of hurricane versus sine-cosine optimization algorithms for economic/ecological emissions load dispatch problem. Int. Trans. Electr. Energ Syst. 29, e2716 (2019). https://doi.org/10.1002/etep.2716

    Article  Google Scholar 

  51. Rizk-Allah, R., M., El-Sehiemy, R., A.: A Novel Sine Cosine Approach for Single and Multiobjective Emission/Economic Load Dispatch Problem. International Conference on Innovative Trends in Computer Engineering (ITCE 2018) Aswan University, Egypt, 271–277 (2018)

  52. Chakraborti, D., Biswas, P., Pal, B. B.: Modelling Multiobjective Bilevel Programming for Environmental-Economic Power Generation and Dispatch Using Genetic Algorithm. In: International Conference on Computational Intelligence, Communications, and Business Analytics, Springer, Singapore, 423–439 (2017)

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Correspondence to Aboul Ella Hassanien.

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Rizk-Allah, R.M., Abo-Sinna, M.A. & Hassanien, A.E. Intuitionistic Fuzzy Sets and Dynamic Programming for Multi-objective Non-linear Programming Problems. Int. J. Fuzzy Syst. 23, 334–352 (2021). https://doi.org/10.1007/s40815-020-00973-z

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