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 208 articles , 1 standard article )

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  1. Kim, Jaehee; Kim, Sheung-Kown: A CHIM-based interactive Tchebycheff procedure for multiple objective decision making (2006)
  2. Sendín, Oscar H.; Vera, Julio; Torres, Nestor V.; Banga, Julio R.: Model based optimization of biochemical systems using multiple objectives: a comparison of several solution strategies (2006)
  3. Allen, M.; Maute, K.: Reliability-based shape optimization of structures undergoing fluid--structure interaction phenomena (2005)
  4. Camponogara, Eduardo; Talukdar, Sarosh N.: Designing communication networks to decompose network control problems (2005)
  5. Collette, Y.; Siarry, P.: Three new metrics to measure the convergence of metaheuristics towards the Pareto frontier and the aesthetic of a set of solutions in biobjective optimization (2005)
  6. Graña Drummond, L. M.; Svaiter, B. F.: A steepest descent method for vector optimization (2005)
  7. Heseler, Andree: Warm start strategies in parametrized optimization with applications in multi-objective optimization. (2005)
  8. Lin, JiGuan G.: On min-norm and min-max methods of multi-objective optimization (2005)
  9. Li, Wei; Li, Qing; Steven, Grant P.; Xie, Y. M.: An evolutionary shape optimization for elastic contact problems subject to multiple load cases (2005)
  10. Mattson, Christopher A.; Messac, Achille: Pareto frontier based concept selection under uncertainty, with visualization (2005)
  11. Ruetsch, G. R.: An interval algorithm for multi-objective optimization (2005)
  12. Camponogara, Eduardo; Talukdar, Sarosh: Designing communication networks for distributed control agents. (2004)
  13. Fliege, Jörg: Gap-free computation of Pareto-points by quadratic scalarizations (2004)
  14. Marler, R. Timothy; Arora, Jasbir S.: Survey of multi-objective optimization methods for engineering (2004)
  15. Cruz Cortés, Nareli; Coello Coello, Carlos A.: Multiobjective optimization using ideas from the clonal selection principle (2003)
  16. Jin, Yaochu: Advanced fuzzy systems design and applications (2003)
  17. Li, Yusheng; Fadel, Georges M.; Wiecek, Margaret; Blouin, Vincent Y.: Minimum effort approximation of the Pareto space of convex bi-criteria problems (2003)
  18. Medaglia, Andrés L.; Fang, Shu-Cherng: A genetic-based framework for solving (multi-criteria) weighted matching problems. (2003)
  19. Messac, Achille; Ismail-Yahaya, Amir; Mattson, Christopher A.: The normalized normal constraint method for generating the Pareto frontier (2003)
  20. Recchioni, Maria Cristina: A path following method for box-constrained multiobjective optimization with applications to goal programming problems (2003)

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