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. Aliano Filho, Angelo; Moretti, Antonio Carlos; Pato, Margarida Vaz; de Oliveira, Washington Alves: An exact scalarization method with multiple reference points for bi-objective integer linear optimization problems (2021)
  2. Assunção, P. B.; Ferreira, O. P.; Prudente, L. F.: Conditional gradient method for multiobjective optimization (2021)
  3. Audet, Charles; Bigeon, Jean; Cartier, Dominique; Le Digabel, Sébastien; Salomon, Ludovic: Performance indicators in multiobjective optimization (2021)
  4. Ghaznavi, M.; Akbari, F.; Khorram, E.: Optimality conditions via a unified direction approach for (approximate) efficiency in multiobjective optimization (2021)
  5. Gkaragkounis, K. T.; Papoutsis-Kiachagias, E. M.; Giannakoglou, K. C.: Adjoint-assisted Pareto front tracing in aerodynamic and conjugate heat transfer shape optimization (2021)
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  10. Doganay, O. T.; Gottschalk, H.; Hahn, C.; Klamroth, K.; Schultes, J.; Stiglmayr, M.: Gradient based biobjective shape optimization to improve reliability and cost of ceramic components (2020)
  11. Dong, Zhiming; Wang, Xianpeng; Tang, Lixin: MOEA/D with a self-adaptive weight vector adjustment strategy based on chain segmentation (2020)
  12. Filatovas, E.; Kurasova, O.; Redondo, J. L.; Fernández, J.: A reference point-based evolutionary algorithm for approximating regions of interest in multiobjective problems (2020)
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  14. Gonçalves, M. L. N.; Prudente, L. F.: On the extension of the Hager-Zhang conjugate gradient method for vector optimization (2020)
  15. Jiang, Shouyong; Li, Hongru; Guo, Jinglei; Zhong, Mingjun; Yang, Shengxiang; Kaiser, Marcus; Krasnogor, Natalio: AREA: an adaptive reference-set based evolutionary algorithm for multiobjective optimisation (2020)
  16. Jornada, Daniel; Leon, V. Jorge: Filtering algorithms for biobjective mixed binary linear optimization problems with a multiple-choice constraint (2020)
  17. Liang, Jing; Li, Zhimeng; Qu, Boyang; Yu, Kunjie; Qiao, Kangjia; Ge, Shilei: A knee point based NSGA-II multi-objective evolutionary algorithm (2020)
  18. Li, Wenhua; Wang, Rui; Zhang, Tao; Ming, Mengjun; Li, Kaiwen: Reinvestigation of evolutionary many-objective optimization: focus on the Pareto knee front (2020)
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