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Optimality conditions for nonlinear optimization problems with interval-valued objective function in admissible orders

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Abstract

This paper addresses the optimization problems with interval-valued objective function. We consider three types of total order relationships on the interval space. For each total order relationship, we introduce interval-valued convex functions and obtain Karush-Kuhn-Tucker (KKT) optimality conditions in an optimization problem with interval-valued objective function. In order to illustrate these conditions, some numerical examples have been considered and solved.

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References

  • Arana-Jiménez, M., & Sánchez-Gil, C. (2020). On generating the set of nondominated solutions of a linear programming problem with parameterized fuzzy numbers. Journal of Global Optimization, 77, 27–52.

    Article  MathSciNet  MATH  Google Scholar 

  • Aubin, J. P., & Cellina, A. (1984). Differential inclusions. New York: Springer.

    Book  MATH  Google Scholar 

  • Bhurjee, A. K., & Panda, G. (2012). Efficient solution of interval optimization problem. Mathematical Methods of Operations Research, 76, 273–288.

    Article  MathSciNet  MATH  Google Scholar 

  • Bustince, H., Fernandez, J., Kolesárová, A., & Mesiar, R. (2013). Generation of linear orders for intervals by means of aggregation functions. Fuzzy Sets and Systems, 220, 69–77.

    Article  MathSciNet  MATH  Google Scholar 

  • Chalco-Cano, Y., Lodwick, W. A., & Rufian-Lizana, A. (2013). Optimality conditions of type KKT for optimization problem with interval-valued objective function via generalized derivative. Fuzzy Optimization and Decision Making, 12, 305–322.

    Article  MathSciNet  MATH  Google Scholar 

  • Cheng, J., Liu, Z., Wu, Z., et al. (2016). Direct optimization of uncertain structures based on degree of interval constraint violation. Computers & Structures, 164, 83–94.

    Article  Google Scholar 

  • Ezzati, R., Khorram, E., & Enayati, R. (2015). A new algorithm to solve fully fuzzy linear programming problems using the MOLP problem. Applied Mathematical Modelling, 39, 3183–3193.

    Article  MathSciNet  MATH  Google Scholar 

  • Fu, C., Liu, Y., & Xiao, Z. (2019). Interval differential evolution with dimension- reduction interval analysis method for uncertain optimization problems. Applied Mathematical Modelling, 69, 441–452.

    Article  MathSciNet  MATH  Google Scholar 

  • Gong, D. W., Ji, X. F., Sun, J., & Sun, X. Y. (2014). Interactive evolutionary algorithms with decision-makers preferences for solving interval multi-objective optimization problems. Neurocomputing, 137, 241–251.

    Article  Google Scholar 

  • Huang, H. B., Huang, X. R., Ding, W. P., et al. (2022). Uncertainty optimization of pure electric vehicle interior tire/road noise comfort based on data-driven. Mechanical Systems and Signal Processing, 165, 108300.

    Article  Google Scholar 

  • Ishibuchi, H., & Tanaka, H. (1990). Multiobjective programming in optimization of the interval objective function. European Journal of Operational Research, 48, 219–225.

    Article  MATH  Google Scholar 

  • Jiang, C., Han, X., Liu, G. R., & Liu, G. P. (2008). A nonlinear interval number programming method for uncertain optimization problems. European Journal of Operational Research, 188, 1–13.

    Article  MathSciNet  MATH  Google Scholar 

  • Jin, T., Xia, H. X., & Chen, H. (2021a). Optimal control problem of the uncertain second-order circuit based on first hitting criteria. Mathematical Methods in the Applied Sciences, 44, 882–900.

    Article  MathSciNet  MATH  Google Scholar 

  • Jin, T., Xia, H. X., Deng, W., et al. (2021b). Uncertain fractional-order multi-objective optimization based on reliability analysis and application to fractional-order circuit with caputo type. Circuits, Systems, and Signal Processing, 40, 5955–5982.

    Article  Google Scholar 

  • Kaveh, A., Dadras, A., & Geran Malek, N. (2019). Robust design optimization of laminated plates under uncertain bounded buckling loads. Structural and Multidisciplinary Optimization, 59, 877–891.

    Article  MathSciNet  Google Scholar 

  • Kumar, A., Kaur, J., & Singh, P. (2011). A new method for solving fully fuzzy linear programming problems. Applied Mathematical Modelling, 35, 817–823.

    Article  MathSciNet  MATH  Google Scholar 

  • Li, D. C., Leung, Y., & Wu, W. Z. (2019). Multiobjective interval linear programming in admissible-order vector space. Information Sciences, 486, 1–19.

    Article  MATH  Google Scholar 

  • Neumaier, A. (1990). Interval methods for systems of equations. Cambridge: Cambridge University Press.

    MATH  Google Scholar 

  • Rahman, M. S., Shaikh, A. A., & Bhunia, A. K. (2020). Necessary and sufficient optimality conditions for non-linear unconstrained and constrained optimization problem with interval valued objective function. Computers & Industrial Engineering, 147, 106634.

    Article  Google Scholar 

  • Santoro, R., Muscolino, G., & Elishakoff, I. (2015). Optimization and anti-optimization solution of combined parameterized and improved interval analyses for structures with uncertainties. Computers & Structures, 149, 31–42.

    Article  Google Scholar 

  • Stefanini, L. (2009). Generalized Hukuhara differentiability of interval-valued functions and interval differential equations. Nonlinear Analysis: Theory, Methods & Applications, 71, 1311–1328.

    Article  MathSciNet  MATH  Google Scholar 

  • Wang, L. Q., Chen, Z. T., Yang, G. L., et al. (2020). An interval uncertain optimization method using back-propagation neural network differentiation. Computer Methods in Applied Mechanics and Engineering, 366, 113065.

    Article  MathSciNet  MATH  Google Scholar 

  • Wu, H. C. (2007). The Karush-Kuhn-Tucker optimality conditions in an optimization problem with interval-valued objective function. European Journal of Operational Research, 176, 46–59.

    Article  MathSciNet  MATH  Google Scholar 

  • Wu, H. C. (2019). Applying the concept of null set to solve the fuzzy optimization problems. Fuzzy Optimization and Decision Making, 18, 279–314.

    Article  MathSciNet  MATH  Google Scholar 

  • Xu, Z. S., & Yager, R. R. (2006). Some geometric aggregation operators based on intuitionistic fuzzy sets. International Journal of General Systems, 35, 417–433.

    Article  MathSciNet  MATH  Google Scholar 

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Acknowledgements

This work was partially supported by the National Natural Science Foundation of China (Grant Nos.11401469,F0118).

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Correspondence to Lifeng Li.

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Li, L. Optimality conditions for nonlinear optimization problems with interval-valued objective function in admissible orders. Fuzzy Optim Decis Making 22, 247–265 (2023). https://doi.org/10.1007/s10700-022-09391-2

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