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A Hierarchical Particle Swarm Optimization for Solving Bilevel Programming Problems

  • Xiangyong Li
  • Peng Tian
  • Xiaoping Min
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4029)

Abstract

The bilevel programming problem (BLPP) has proved to be a NP-hard problem. In this paper, we propose a hierarchial particle swarm optimization (PSO) for solving general BLPPs. Unlike most traditional algorithms designed for specific versions or based on specific assumptions, the proposed method is a hierarchical algorithm framework, which solves the general bilevel programming problems directly by simulating the decision process of bilevel programming. The solving general BLPPs is transformed to solve the upper-level and lower-level problems iteratively by two variants of PSO. The variants of PSO are built to solve upper-level and lower-level constrained optimization problems. The experimental results compared with those of other methods show that the proposed algorithm is a competitive method for solving general BLPPs.

Keywords

Bilevel Programming Bilevel Programming Problem Standard Particle Swarm Optimization Trust Region Algorithm Linear Bilevel Programming 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Xiangyong Li
    • 1
  • Peng Tian
    • 1
  • Xiaoping Min
    • 2
  1. 1.Antai College of Economics & ManagementShanghai Jiaotong UniversityShanghaiP.R. China
  2. 2.School of FinanceJiangxi University of Finance and EconomicsNanchangP.R. China

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