NBI

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)


References in zbMATH (referenced in 169 articles , 1 standard article )

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  1. Cartee, Elliot; Vladimirsky, Alexander: Control-theoretic models of environmental crime (2020)
  2. 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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  4. Gonçalves, M. L. N.; Prudente, L. F.: On the extension of the Hager-Zhang conjugate gradient method for vector optimization (2020)
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  7. Rojas-Gonzalez, Sebastian; van Nieuwenhuyse, Inneke: A survey on kriging-based infill algorithms for multiobjective simulation optimization (2020)
  8. Ghosh, Debdas: On identifying fuzzy knees in fuzzy multi-criteria optimization problems (2019)
  9. Lin, Wu; Lin, Qiuzhen; Zhu, Zexuan; Li, Jianqiang; Chen, Jianyong; Ming, Zhong: Evolutionary search with multiple utopian reference points in decomposition-based multiobjective optimization (2019)
  10. Mita, Kanako; Fukuda, Ellen H.; Yamashita, Nobuo: Nonmonotone line searches for unconstrained multiobjective optimization problems (2019)
  11. Morovati, Vahid; Pourkarimi, Latif: Extension of Zoutendijk method for solving constrained multiobjective optimization problems (2019)
  12. Pantoja-García, Jesús S.; Villarreal-Cervantes, Miguel G.; García-Mendoza, Consuelo V.; Silva-García, Víctor M.: Synergistic design of the bipedal lower-limb through multiobjective differential evolution algorithm (2019)
  13. Thies, Christian; Kieckhäfer, Karsten; Spengler, Thomas S.; Sodhi, Manbir S.: Operations research for sustainability assessment of products: a review (2019)
  14. Thomann, Jana; Eichfelder, Gabriele: A trust-region algorithm for heterogeneous multiobjective optimization (2019)
  15. Zhou, Yuren; He, Xiaoyu; Xiang, Yi; Cai, Shaowei: A set of new multi- and many-objective test problems for continuous optimization and a comprehensive experimental evaluation (2019)
  16. Akbari, Fereshteh; Ghaznavi, Mehrdad; Khorram, Esmaile: A revised Pascoletti-Serafini scalarization method for multiobjective optimization problems (2018)
  17. Artigues, Christian; Jozefowiez, Nicolas; Sarpong, Boadu M.: Column generation algorithms for bi-objective combinatorial optimization problems with a min-max objective (2018)
  18. 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)
  19. Custódio, A. L.; Madeira, J. F. A.: MultiGLODS: global and local multiobjective optimization using direct search (2018)
  20. Denysiuk, Roman; Silva, Cristiana J.; Torres, Delfim F. M.: Multiobjective optimization to a TB-HIV/AIDS coinfection optimal control problem (2018)

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