A hierarchical particle swarm optimization for solving bilevel programming problems

Authors:
Xiangyong Li;Peng Tian;Xiaoping Min
Affiliations:
Antai College of Economics & Management, Shanghai Jiaotong University, Shanghai, P.R. China;Antai College of Economics & Management, Shanghai Jiaotong University, Shanghai, P.R. China;School of Finance, Jiangxi University of Finance and Economics, Nanchang, P.R. China
Venue:
ICAISC'06 Proceedings of the 8th international conference on Artificial Intelligence and Soft Computing
Year:
2006

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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.