The modified objective-constraint scalarization approach for multiobjective optimization problems

In this article, a novel scalarization methodology, called the modified objective-constraint technique, is proposed for determining efficient solutions a given multiobjective programming problem. The suggested scalarized problem extends some existing problems. It is shown that how adding slack variables to the constraints, can help us to find easily checked conditions concerning (weak, proper) Pareto optimality. By applying the suggested problem, we generate an almost even approximation of the efficient front. The performance and capability of the developed approach are demonstrated in test problems containing disconnected or nonconvex fronts and feasible points. In particular, we apply the suggested approach in an engineering design problem with two objective functions.

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  • [1] S. Adali, J.C. Bruch, J.M. Sloss and I.S. Sadek, Structural control of a variable crosssection beam by distributed forces, Mech. Based Des. Struct. Mach. 16(3), 313-333, 1988.
  • [2] F. Akbari, M. Ghaznavi and E. Khorram, A revised Pascoletti-Serafini scalarization method form multiobjective optimization problems, J. Optim. Theory Appl. 178(2), 560-590, 2018.
  • [3] M. Alavi Hejazi, On approximate Karush-Kuhn-Tucker conditions for multiobjective optimization problems, Iran. J. Sci. Technol. Trans. A Sci. 42, 873-879, 2018.
  • [4] K.K. Annamdas and S.S. Rao, Multi-objective optimization of engineering systems using game theory and particle swarm optimization, Eng. Optim. 41(8), 737-752, 2009.
  • [5] J. Branke, K. Deb, K. Miettinen and R. Sowiski, Multiobjective Optimization: Interactive and Evolutionary Approaches, Springer, Berlin, 2008.
  • [6] R.S. Burachik, C.Y. Kaya and M.M. Rizvi, A new scalarization technique to approximate Pareto fronts of problems with disconnected feasible sets, J. Optim. Theory Appl. 162(2), 428-446, 2014.
  • [7] R.S. Burachik, C.Y. Kaya and M.M. Rizvi, A New Scalarization technique and new algorithms to generate Pareto fronts, SIAM J. Optim. 27(2), 1010-1034, 2017.
  • [8] V. Chankong and Y.Y. Haimes, Multiobjective Decision Making: Theory and Methodology, North Holland, Amsterdam, 1983.
  • [9] C.A.C. Coello, G.B. Lamont and D.A. Van Veldhuizen, Evolutionary Algorithms for Solving Multiobjective Problems, Springer, New York, 2007.
  • [10] K. Deb, Multi-Objective Optimization Using Evolutionary Algorithms, Wiley- Interscience Series in Systems and Optimization, Wiley, Chichester, 2001.
  • [11] K. Doerner, W.J. Gutjahr, R.F. Hartl, C. Strauss and C. Stummer, Pareto ant colony optimization: A metaheuristic approach to multiobjective portfolio selection, Ann. Oper. Res. 131, 79-99, 2004.
  • [12] M. Ehrgott, Multicriteria Optimization, Springer, Berlin, 2005
  • [13] M. Ehrgott and M. Burjony, Radiation therapy planning by multicriteria optimization, in: Proceedings of the 36th Annual Conference of the Operational Research Society of New Zealand, 244-253, Auckland, 2001.
  • [14] M. Ehrgott, K. Klamroth and C. Schwehm An mcdm approach to portfolio optimization, Eur. J. Oper. Res. 155(3), 752-770, 2004.
  • [15] M. Ehrgott and S. Ruzika, Improved ϵ-constraint method for multiobjective programming, J. Optim. Theory Appl. 138, 375-396, 2008.
  • [16] G. Eichfelder, Adaptive Scalarization Methods in Multiobjective Optimization, Springer, Berlin, 2008.
  • [17] G. Eichfelder, An adaptive scalarization method in multi-objective optimization, SIAM J. Optim. 19, 1694-1718, 2009.
  • [18] G. Eichfelder, Scalarizations for adaptively solving multi-objective optimization problems, Comput. Optim. Appl. 44, 249-273, 2009
  • [19] A. Engau and M.M. Wiecek, Generating ϵ-efficient solutions in multiobjective programming, European J. Oper. Res. 177(3), 1566-1579, 2007
  • [20] S. Gass and T. Saaty, The computational algorithm for the parametric objective function, Nav. Res. Logist. Q. 2, 39-45, 1955.
  • [21] A. Geoffrion, Proper efficiency and the theory of vector maximization, J. Math. Anal. Appl. 22(3), 618-630, 1968.
  • [22] M. Ghaznavi, F. Akbari and E. Khorram, Optimality conditions via a unified direction approach for (approximate) efficiency in multiobjective optimization, Optim. Methods Softw. 36(2-3), 627-652, 2021.
  • [23] M. Ghaznavi and Z. Azizi, An algorithm for approximating nondominated points of convex multiobjective optimization problems, Bull. Iranian Math. Soc. 43(5), 1399- 1415, 2017.
  • [24] S. Habibi, N. Kanzi and A. Ebadian, Weak Slater qualification for nonconvex multiobjective semi-infinite programming, Iran. J. Sci. Technol. Trans. A Sci. 44, 417-424, 2020.
  • [25] C. Hillermeier and J. Jahn, Multiobjective optimization: survey of methods and industrial applications, Surv. Math. Ind. 11, 1-42, 2005.
  • [26] S. Huband, P. Hingston, L. Barone and L. While, A review of multi-objective test problems and a scalable test problem toolkit, IEEE Trans. Evol. Comput., 10(5), 477- 506, 2006.
  • [27] J. Jahn, A. Kirsch and C. Wagner, Optimization of rod antennas of mobile phones, Math. Methods Oper. Res. 59(1), 37-51, 2004.
  • [28] H. Kuhn and A. Tucker, Nonlinear programming, in: Proceedings of the second Berkeley symposium on mathematical statistics and probability, edited by J. Neyman, 481- 492, University of California Press, Berkeley, California, 1951.
  • [29] R.T. Marler and J.S. Arora, Survey of multi-objective optimization methods for engineering, Struct. Multidiscip. Optim. 26(6), 369-395, 2004.
  • [30] A. Pascoletti and P. Serafini, Scalarizing vector optimization problems, J. Optim. Theory Appl. 42, 499-524, 1984.
  • [31] A. Rezaee, Characterization of isolated efficient solutions in nonsmooth multiobjective semi-infinite programming, Iran. J. Sci. Technol. Trans. A Sci. 43, 1835-1839, 2019.
  • [32] M.M. Rizvi, New optimality conditions for non-linear multiobjective optimization problems and new scalarization techniques for constructing pathological Pareto fronts, PhD Thesis, University of South Australia, 2013.
  • [33] S. Schäffler, R. Schultz and K. Weinzierl, Stochastic method for the solution of unconstrained vector optimization problems, J. Optim. Theory Appl. 114(1), 209-222, 2002.
  • [34] R.E. Steuer and P. Na, Multiple criteria decision making combined with finance: a categorized bibliographic study, European J. Oper. Res. 150(3), 496-515, 2003.