• DocumentCode
    510280
  • Title

    A Genetic Algorithm for Multiobjective Bilevel Convex Optimization Problems

  • Author

    Jia, Liping ; Wang, Yuping

  • Author_Institution
    Sch. of Comput. Sci. & Technol., Xidian Univ., Xi´´an, China
  • Volume
    1
  • fYear
    2009
  • fDate
    11-14 Dec. 2009
  • Firstpage
    98
  • Lastpage
    102
  • Abstract
    Multiobjective bilevel programming problem (MBPP) has a wide field of applications and has been proven to be an NP-hard problem. In this paper, a special multiobjective bilevel convex programming problem (MBCPP) is studied, and it is first transformed into an equivalent single objective bilevel convex programming problem by weighted sum of objectives. Then, for the equivalent problem, we design a scheme for generating weight vectors. Thereafter, a crossover operator and mutation operator are designed. Based on all these, a genetic algorithm called Ga-BCPP is proposed for MBCPP. At last, the simulation is made and the performance of the proposed algorithm is compared with one peer genetic algorithm. The results show that the proposed algorithm is effective.
  • Keywords
    convex programming; genetic algorithms; optimisation; NP hard problem; crossover operator; genetic algorithm; multiobjective bilevel convex optimization problems; multiobjective bilevel programming problem; mutation operator; objectives weighted sum; single objective bilevel convex programming problem; weight vectors; Application software; Cities and towns; Computational intelligence; Computer science; Computer security; Genetic algorithms; Genetic mutations; NP-hard problem; Search methods; Transportation; genetic algorithm; multiobjective bilevel convex programming; numerical experiment;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computational Intelligence and Security, 2009. CIS '09. International Conference on
  • Conference_Location
    Beijing
  • Print_ISBN
    978-1-4244-5411-2
  • Type

    conf

  • DOI
    10.1109/CIS.2009.37
  • Filename
    5376707