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. 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)
  2. Jornada, Daniel; Leon, V. Jorge: Filtering algorithms for biobjective mixed binary linear optimization problems with a multiple-choice constraint (2020)
  3. Liang, Jing; Li, Zhimeng; Qu, Boyang; Yu, Kunjie; Qiao, Kangjia; Ge, Shilei: A knee point based NSGA-II multi-objective evolutionary algorithm (2020)
  4. Li, Wenhua; Wang, Rui; Zhang, Tao; Ming, Mengjun; Li, Kaiwen: Reinvestigation of evolutionary many-objective optimization: focus on the Pareto knee front (2020)
  5. 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)
  6. Mahdavi-Amiri, N.; Salehi Sadaghiani, F.: A superlinearly convergent nonmonotone quasi-Newton method for unconstrained multiobjective optimization (2020)
  7. Nerantzis, Dimitrios; Pecci, Filippo; Stoianov, Ivan: Optimal control of water distribution networks without storage (2020)
  8. Raimundo, Marcos M.; Ferreira, Paulo A. V.; Von Zuben, Fernando J.: An extension of the non-inferior set estimation algorithm for many objectives (2020)
  9. Rojas-Gonzalez, Sebastian; van Nieuwenhuyse, Inneke: A survey on kriging-based infill algorithms for multiobjective simulation optimization (2020)
  10. Salmei, Hossein; Yaghoobi, Mohammad Ali: Improving the min-max method for multiobjective programming (2020)
  11. Stanojević, Bogdana; Glover, Fred: A new approach to generate pattern-efficient sets of non-dominated vectors for multi-objective optimization (2020)
  12. 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)
  13. Yao, Shuangshuang; Dong, Zhiming; Wang, Xianpeng; Ren, Lei: A multiobjective multifactorial optimization algorithm based on decomposition and dynamic resource allocation strategy (2020)
  14. Ying, Weiqin; Huang, Junjie; Wu, Yu; Deng, Yali; Xie, Yuehong; Wang, Zhenyu; Lin, Zhiyi: Multi-dimensional tree guided efficient global association for decomposition-based evolutionary many-objective optimization (2020)
  15. Bao, Chunteng; Xu, Lihong; Goodman, Erik D.: A novel two-archive matching-based algorithm for multi- and many-objective optimization (2019)
  16. Chen, Min-Rong; Zeng, Guo-Qiang; Lu, Kang-Di: A many-objective population extremal optimization algorithm with an adaptive hybrid mutation operation (2019)
  17. Ghosh, Debdas: On identifying fuzzy knees in fuzzy multi-criteria optimization problems (2019)
  18. Han, Dong; Du, Wenli; Du, Wei; Jin, Yaochu; Wu, Chunping: An adaptive decomposition-based evolutionary algorithm for many-objective optimization (2019)
  19. Khan, Burhan; Hanoun, Samer; Johnstone, Michael; Lim, Chee Peng; Creighton, Douglas; Nahavandi, Saeid: A scalarization-based dominance evolutionary algorithm for many-objective optimization (2019)
  20. 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)

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