By Topic

Genetic Algorithms for Solving Linear Bilevel Programming

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Wang Guang-Min ; Wuhan University, Wuhan, China ; Wan Zhong-Ping ; Wang Xian-Jia ; Chen Ya-lin

Bilevel programming, a tool for modelling decentralized decisions, consists of the objectives of the upper level and lower level. And numerous methods are proposed for solving this problem. In this paper, we provide a genetic algorithm method for solving the linear bilevel programming. In our algorithm, we adopted some techniques to guarantee the not only the initial chromosomes but also the chromosomes generated by genetic operators are all feasible, which greatly reduces the searching space and avoiding the difficulty to deal with the infeasible points. Furthermore, it also enhances the efficiency of the algorithm that the best offsprings are selected to replace the parents in operator procedures. Some examples are illustrative to show the feasibility and efficiency of the algorithm proposed in this paper.

Published in:

Parallel and Distributed Computing, Applications and Technologies, 2005. PDCAT 2005. Sixth International Conference on

Date of Conference:

05-08 Dec. 2005