[1] Cruz, J. B., & Simaan, M. A. (2000). Ordinal games and generalized Nash and Stackelberg solutions. Journal of optimization theory and applications, 107(2), 205-222.
[2] Chankong, V., & Haimes, Y. Y. (2008). Multi-objective decision making: theory and methodology. Courier Dover Publications.
[3] Elshafei, M. M. K. (2007). An interactive approach for solving Nash cooperative continuous static games. International journal of contemporary mathematical sciences, 2(24), 1147- 1162.
[4] Gardiner, L. R., & Steuer, R. E. (1994). Unified interactive multiple objective programming. European journal of operational research, 74(3), 391-406.
[5] Hwang, C. L., & Masud, A. S. M. (2012). Multiple objective decision making—methods and applications: a state-of-the-art survey (Vol. 164). Springer Science & Business Media.
[6] Khalifa, H. A., & Zeineldin, R. A. (2015). An interactive approach for solving fuzzy cooperative continuous static games. International journal of computer applications, 113(1).
[7] Khalifa, H. A. (2016). An interactive approach for solving fuzzy multi-objective non-linear programming problems. The journal of fuzzy mathematics, 3(24), 535- 545.
[8] Molina, C., & Earn, D. J. (2017). Evolutionary stability in continuous nonlinear public goods games. Journal of mathematical biology, 74(1-2), 499-529.
[9] Muhammed, D., Anisi, M., Zareei, M., Vargas-Rosales, C., & Khan, A. (2018). Game theory-based cooperation for underwater acoustic sensor networks: Taxonomy, review, research challenges and directions. Sensors, 18(2), 425.
[10]Narula, S. C., Kirilov, L., & Vassilev, V. (1994). An interactive algorithm for solving multiple objective nonlinear programming problems. In G. H. Tzeng H. F. Wang U. P. Wen & P. L. Yu (Eds.). Multiple criteria decision making (pp. 119-127). Springer, New York, NY.
[11]Navidi, H., Amiri, A., & Kamranrad, R. (2014). Multi-responses optimization through game theory approach. International journal of industrial engineering and production research, 25(3), 215- 224.
[12]Osman, M. S. A. (1977). Qualitative analysis of basic notions in parametric convex programming. I. Parameters in the constraints. Aplikace matematiky, 22(5), 318-332.
[13]Osman, M., & Dauer, J. (1983). Characterization of basic notions in multi-objective convex programming problems. Retrieved from Lincoln, University of Nebraska database.
[14]Osman, M. S., El-Kholy, N. A., & Soliman, E. I. (2015). A recent approach to continuous time open loop stackelberg dynamic game with min- max cooperative and non-cooperative followers. European scientific journal, 11(3), 94-109.
[15]Sadrabadi, M. R., & Sadjadi, S. J. (2009). A new interactive method to solve multiobjective linear programming problems. JSEA, 2(4), 237-247.
[16]Shin, W. S., & Ravindran, A. (1992). A comparative study of interactive tradeoff cutting plane methods for MOMP. European journal of operational research, 56(3), 380-393.
[17]Wang, X. M., Qin, Z. L., & Hu, Y. D. (2001). An interactive algorithm for multicriteria decision making: the attainable reference point method. IEEE transactions on systems, man, and cybernetics-part A: systems and humans, 31(3), 194-198.
[18]Wendell, R. E., & Lee, D. N. (1977). Efficiency in multi-objective optimization problems. Mathematical programming, 12(1), 406- 414.