By Topic

Improvement of the Fusing Genetic Algorithm and Ant Colony Algorithm in Virtual Enterprise Partner Selection Problem

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

3 Author(s)
Zhong Yao ; Sch. of Econ. & Manage., BeiHang Univ., Beijing, China ; Ranran Pan ; Fujun Lai

This paper extends the previous research in which it integrates the genetic algorithm (GA) into ant colony algorithm (ACA) to optimize the partner selection problems. New improvement mainly uses a max-min algorithm instead of the ant colony algorithm in ACA. We first briefly presents the benefits and necessity of applying the integrated algorithm based on GA and ACA approach to resolve the partner selection, and then proposes an improved model of ACA for virtual enterprise partner selection. Finally, experiments demonstrate significant quality improvement of partner selection for our new method and significant efficiency improvement with new GA and ACA fusion methods in partner selection. The conclusions in this paper can be useful for the similar problems in virtual enterprises.

Published in:

Computer Science and Information Engineering, 2009 WRI World Congress on  (Volume:1 )

Date of Conference:

March 31 2009-April 2 2009