Normal-boundary intersection: A new method for generating the Pareto surface in nonlinear multicriteria optimization problems This paper proposes an alternate method for finding several Pareto optimal points for a general nonlinear multicriteria optimization problem. Such points collectively capture the trade-off among the various conflicting objectives. It is proved that this method is independent of the relative scales of the functions and is successful in producing an evenly distributed set of points in the Pareto set given an evenly distributed set of parameters, a property which the popular method of minimizing weighted combinations of objective functions lacks. Further, this method can handle more than two objectives while retaining the computational efficiency of continuation-type algorithms. This is an improvement over continuation techniques for tracing the trade-off curve since continuation strategies cannot easily be extended to handle more than two objectives. (Source: http://plato.asu.edu)

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  1. Bento, G. C.; Cruz Neto, J. X.; López, G.; Soubeyran, Antoine; Souza, J. C. O.: The proximal point method for locally Lipschitz functions in multiobjective optimization with application to the compromise problem (2018)
  2. Kirlik, Gokhan; Sayın, Serpil: Bilevel programming for generating discrete representations in multiobjective optimization (2018)
  3. Banasik, Aleksander; Kanellopoulos, Argyris; Claassen, G. D. H.; Bloemhof-Ruwaard, Jacqueline M.; van der Vorst, Jack G. A. J.: Assessing alternative production options for eco-efficient food supply chains using multi-objective optimization (2017)
  4. Burachik, R. S.; Kaya, C. Y.; Rizvi, M. M.: A new scalarization technique and new algorithms to generate Pareto fronts (2017)
  5. Cao, Yongtao; Smucker, Byran J.; Robinson, Timothy J.: A hybrid elitist Pareto-based coordinate exchange algorithm for constructing multi-criteria optimal experimental designs (2017)
  6. Lin, Kuan-Min; Ehrgott, Matthias; Raith, Andrea: Integrating column generation in a method to compute a discrete representation of the non-dominated set of multi-objective linear programmes (2017)
  7. Liu, Chongyang; Gong, Zhaohua; Teo, Kok Lay; Sun, Jie; Caccetta, Louis: Robust multi-objective optimal switching control arising in 1,3-propanediol microbial fed-batch process (2017)
  8. Liu, Xin; Reynolds, Albert C.: Robust gradient-based multiobjective optimization for the generation of well controls to maximize the net-present-value of production under geological uncertainty (2017)
  9. Martin, Benjamin; Goldsztejn, Alexandre; Granvilliers, Laurent; Jermann, Christophe: Constraint propagation using dominance in interval branch & bound for nonlinear biobjective optimization (2017)
  10. Sánchez, Helem Sabina; Visioli, Antonio; Vilanova, Ramon: Optimal Nash tuning rules for robust PID controllers (2017)
  11. Wang, Honggang: Multi-objective retrospective optimization using stochastic zigzag search (2017)
  12. Cheng, Junheng; Chu, Feng; Chu, Chengbin; Xia, Weili: Bi-objective optimization of single-machine batch scheduling under time-of-use electricity prices (2016)
  13. Fliege, Jörg; Vaz, A. Ismael F.: A method for constrained multiobjective optimization based on SQP techniques (2016)
  14. Jornada, Daniel; Leon, V. Jorge: Biobjective robust optimization over the efficient set for Pareto set reduction (2016)
  15. Liu, Xin; Reynolds, Albert C.: A multiobjective steepest descent method with applications to optimal well control (2016)
  16. Martin, Benjamin; Goldsztejn, Alexandre; Granvilliers, Laurent; Jermann, Christophe: On continuation methods for non-linear bi-objective optimization: towards a certified interval-based approach (2016)
  17. Nowé, Ann; Brys, Tim: A gentle introduction to reinforcement learning (2016)
  18. Schütze, Oliver; Martín, Adanay; Lara, Adriana; Alvarado, Sergio; Salinas, Eduardo; Coello Coello, Carlos A.: The directed search method for multi-objective memetic algorithms (2016)
  19. Shao, Lizhen; Ehrgott, Matthias: Discrete representation of non-dominated sets in multi-objective linear programming (2016)
  20. Cao, Yongtao; Smucker, Byran J.; Robinson, Timothy J.: On using the hypervolume indicator to compare Pareto fronts: applications to multi-criteria optimal experimental design (2015)

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