Scheduled System Maintenance on December 17th, 2014:
IEEE Xplore will be upgraded between 2:00 and 5:00 PM EST (18:00 - 21:00) UTC. During this time there may be intermittent impact on performance. We apologize for any inconvenience.
By Topic

A Hybrid Method for Solving Global Optimization 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.

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

2 Author(s)
Jinhua Li ; Sch. of Archit. & Civil Eng., Xi''an Univ. of Sci. & Technol., Xi''an, China ; Jie Liu

In this paper, a hybrid descent method, consisting of a genetic algorithm and the filled function method, is proposed. The genetic algorithm is used to locate descent points for previously converged local minima. The combined method has the decent property and the convergence is monotonic. To demonstrate the effectiveness of the proposed hybrid method, several multi-dimensional or non-convex optimization problems are solved. Numerical experiments on benchmark functions with different dimansions denmonstrate that the new algorithm has a more rapid convergence and a higher success rate, and can fine the solutions with higher quality, compared with some other existing similar algorithms, which is consistent with the analysis in theory.

Published in:

Computational Intelligence and Security (CIS), 2011 Seventh International Conference on

Date of Conference:

3-4 Dec. 2011