By Topic

Improved ant colony algorithm for continuous function optimization

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)
Xue Xue ; Sch. of Inf. & Electr. Eng., China Univ. of Min. & Technol., Xuzhou, China ; Wei Sun ; Chengshi Peng

As a new model of intelligent computing, ant colony optimization (ACO) is a great success on combinatorial optimization problems, however, but research is relatively less in solving problems on continuous space optimization. Based on the mechanism and mathematical model of ant colony algorithm, mutation operation is introduced. The global and local updating rules of ant colony algorithm are improved. The possibility of halting the ant system becomes much lower than the ever in the time arriving at local minimum. At last, this algorithm was tested by several benchmark functions. The simulation results indicate that improved ant colony algorithm can rapidly find superior global solution and the algorithm presents a new effective way for solving this kind of problem.

Published in:

Control and Decision Conference (CCDC), 2010 Chinese

Date of Conference:

26-28 May 2010