By Topic

An ant colony algorithm Hybridized with Iterated Local Search for the QAP

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

1 Author(s)
Mingping Xia ; Beijing Union Univ., Beijing, China

The quadratic assignment problem (QAP) is considered one of the hardest combinatorial optimization problems. Ant colony algorithm (ACA), inspired by the food-searching behavior of ants, is an evolutionary algorithm and performs well in discrete optimization. In this paper, through an analysis of the constructive procedure of the solution in the ACA, a hybrid ant colony system (ACAILS) with iterated local search (ILS), is proposed. In ACAILS, only partial facilities are randomly chosen to compute the designed probability. Experimental results demonstrate that the proposed approach can obtain the better quality of the solutions.

Published in:

Computational Intelligence and Industrial Applications, 2009. PACIIA 2009. Asia-Pacific Conference on  (Volume:2 )

Date of Conference:

28-29 Nov. 2009