Scheduled System Maintenance:
On May 6th, single article purchases and IEEE account management will be unavailable from 8:00 AM - 5:00 PM ET (12:00 - 21:00 UTC). We apologize for the inconvenience.
By Topic

Global Optimization Method Based on the Statistical Genetic Algorithm for Solving Nonlinear Bilevel Programming Problems

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.

The purchase and pricing options are temporarily unavailable. Please try again later.
4 Author(s)

This paper presents a global optimization method based on the statistical genetic algorithm for solving nonlinear bilevel programming problems. The bilevel programming problem is firstly transformed into a single level problem by applying Karush-Kuhn-Tucker conditions, and then an efficient method based on the statistical genetic algorithm has been proposed for solving the single level problem with the complementarity constraints. By certain handling tech- nology, the simplified problem without the complementarity constraints can be gotten. If it is solvable then its optimal solution is a feasible solution of the original bilevel pro- gramming problem. At last, a global optimal solution of the original problem can be found among its feasible solutions. Numerical experiments on some benchmark problems show that the new algorithm can find global optimal solutions of the bilevel programming problems in a small number of fit- ness evaluations.

Published in:

Computational Intelligence and Security, 2007 International Conference on

Date of Conference:

15-19 Dec. 2007