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Open Access ·Peer-reviewed·ISSN (Online): 2169-0103·ISSN (Print): 0252-2667
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The Journal of Information and Optimization Sciences (JIOS) is a world leading journal publishing high quality, rigorously peer-reviewed original research in all mathematically-oriented theoretical and applied topics in information sciences, optimization sciences and related areas since 1980. Subjects include but are not limited to: • Information Sciences • Optimization Sciences • Control Theory • Operational Research • Decision Sciences • Information Theory • Information Technology • Computer Networks and Communications • Mathematical Programming • Modelling and Simulation • Database Management • Applications to Engineering Sciences • Applications to Technology

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Open Access Research Article

Multi-objective optimization revisited

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pp. 833–848Vol. 47Issue 3March 2026DOI: 10.47974/JIOS-1395XML
Received:
07 Dec 2022
Published Online:
02 Mar 2026
Article type:
Research Article
Language:
EN
Article no.:
JIOS-1395
Pages:
833–848

Abstract

In this paper, the multi-objective (multiple criteria, vector) optimization is considered. Some examples of such optimization problems are presented, Pareto optimality is introduced, and arbitrage schemes are described. The special case of multi-objective linear optimization is studied, and methods for solving such problems are presented. Pareto optimality for the nonlinear optimization problem is also considered.

Keywords

Subject Classifications

90C29

References

[1] Alberto Bemporad and David Munoz de la Pena, “Multiobjective model predictive control”, Automatica, vol. 45, no. 12, pp. 2823-2830 (2009). [2] N. Beume, B. Naujoks, and M. Emmerich, “SMS-EMOA: Multiobjective selection based on dominated hypervolume”, European Journal of Operational Research, vol. 181, no. 3, pp. 1653-1669 (2007). [3] F. H. Clarke, Optimization and Nonsmooth Analysis, Society for Industrial and Applied Mathematics SIAM, Philadelphia, PA, USA, Classics in Applied Mathematics, Vol. 5 (1990). [4] I. Das and J. E. Dennis, “Normal-boundary intersection: A new method for generating the Pareto surface in nonlinear multicriteria optimization problems”, SIAM Journal on Optimization, vol. 8, no. 3, pp. 631-657 (1998). [5] M. Luque, F. Ruiz, and K. Miettinen, “Global formulation for interactive multiobjective optimization”, OR Spectrum, Vol. 33, pp. 27-48 (2008). [6] Sidhartha Panda, “Multi-objective evolutionary algorithm for SSSC-based controller design”, Electric Power Systems Research, vol. 79, no. 6,  pp. 937-944 (2009). [7] Anthony Przybylski and Xavier Gandibleux, “Multi-objective branch and bound”, European Journal of Operational Research, vol. 260, no. 3, no. 6, pp. 856-872 (2017). [8] F. Ruiz, M. Luque, and K. Miettinen, “Improving the computational efficiency in a global formulation (GLIDE) for interactive multiobjective optimization”, Annals of Operations Research, Vol. 197, pp. 47-70 (2011). [9] Hamdy A. Taha, Operations Research: An Introdusction, 10th ed., Pearson Education Ltd. (2017). [10] Thomas Vincent, Florian Seipp, Stefan Ruzika, Anthony Przybylski, and Xavier Gandibleux, “Multiple objective branch and bound for mixed 0-1 linear programming: Corrections and improvements for the biobjective case”, Computers & Operations Research, Vol. 40(1), pp. 498-509 (2013). [11] A. P. Wierzbicki, “A mathematical basis for satisficing decision making”, Mathematical Modelling, vol. 3, no. 5,  pp. 391-405 (1982). [12] N. Wesner, “Multiobjective optimization via visualization”, Economics Bulletin, vol. 37, no. 2, pp. 1226-1233 (2017).
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