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 189 articles , 1 standard article )

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  1. Cartee, Elliot; Vladimirsky, Alexander: Control-theoretic models of environmental crime (2020)
  2. Dell’Aere, Alessandro: An image set-oriented method for the numerical treatment of bi-level multi-objective optimization problems (2020)
  3. 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)
  4. Filatovas, E.; Kurasova, O.; Redondo, J. L.; Fernández, J.: A reference point-based evolutionary algorithm for approximating regions of interest in multiobjective problems (2020)
  5. Gilles, Marc Aurèle; Vladimirsky, Alexander: Evasive path planning under surveillance uncertainty (2020)
  6. Gonçalves, M. L. N.; Prudente, L. F.: On the extension of the Hager-Zhang conjugate gradient method for vector optimization (2020)
  7. 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)
  8. Jornada, Daniel; Leon, V. Jorge: Filtering algorithms for biobjective mixed binary linear optimization problems with a multiple-choice constraint (2020)
  9. Liang, Jing; Li, Zhimeng; Qu, Boyang; Yu, Kunjie; Qiao, Kangjia; Ge, Shilei: A knee point based NSGA-II multi-objective evolutionary algorithm (2020)
  10. Luo, Jianping; Huang, Xiongwen; Yang, Yun; Li, Xia; Wang, Zhenkun; Feng, Jiqiang: A many-objective particle swarm optimizer based on indicator and direction vectors for many-objective optimization (2020)
  11. Nerantzis, Dimitrios; Pecci, Filippo; Stoianov, Ivan: Optimal control of water distribution networks without storage (2020)
  12. Raimundo, Marcos M.; Ferreira, Paulo A. V.; Von Zuben, Fernando J.: An extension of the non-inferior set estimation algorithm for many objectives (2020)
  13. Rojas-Gonzalez, Sebastian; van Nieuwenhuyse, Inneke: A survey on kriging-based infill algorithms for multiobjective simulation optimization (2020)
  14. Witting, Katrin; Molo, Mirko Hessel-Von; Dellnitz, Michael: Structural properties of Pareto fronts: the occurrence of dents in classical and parametric multiobjective optimization problems (2020)
  15. Yao, Shuangshuang; Dong, Zhiming; Wang, Xianpeng; Ren, Lei: A multiobjective multifactorial optimization algorithm based on decomposition and dynamic resource allocation strategy (2020)
  16. Bao, Chunteng; Xu, Lihong; Goodman, Erik D.: A novel two-archive matching-based algorithm for multi- and many-objective optimization (2019)
  17. Chen, Min-Rong; Zeng, Guo-Qiang; Lu, Kang-Di: A many-objective population extremal optimization algorithm with an adaptive hybrid mutation operation (2019)
  18. Ghosh, Debdas: On identifying fuzzy knees in fuzzy multi-criteria optimization problems (2019)
  19. Han, Dong; Du, Wenli; Du, Wei; Jin, Yaochu; Wu, Chunping: An adaptive decomposition-based evolutionary algorithm for many-objective optimization (2019)
  20. Khan, Burhan; Hanoun, Samer; Johnstone, Michael; Lim, Chee Peng; Creighton, Douglas; Nahavandi, Saeid: A scalarization-based dominance evolutionary algorithm for many-objective optimization (2019)

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