By Topic

Notice of Violation of IEEE Publication Principles
A Quadratic Particle Swarm Optimization for Weight 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)
He Jing ; Sch. of Comput. & Electron. Eng., Hunan Univ. of Commerce, Changsha, China ; Shi Dejia ; Wang Li

Notice of Violation of IEEE Publication Principles

"A Quadratic Particle Swarm Optimization for Weight Optimization"
by He Jing, Shi Dejia, and Wang Li
in the Proceedings of the 2009 Third International Symposium on Intelligent Information Technology Application (IITA 2009), November 2009, pp. 557-560

After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE's Publication Principles.

This paper contains significant portions of original text from the papers cited below. The original text was copied without attribution (including appropriate references to the original author(s) and/or paper titles) and without permission.

Due to the nature of this violation, reasonable effort should be made to remove all past references to this paper, and future references should be made to the following articles:

"A New Optimization Algorithm for Weight Optimization"
by Hui Li and Xuesong Yan
in Lecture Notes in Computer Science, Volume 5370, Springer, 2008, pp. 723-730

Particle Swarm Optimization algorithm was developed under the inspiration of behavior laws of bird flocks, fish schools and human communities. Aiming at the disadvantages of Particle Swarm Optimization algorithm like being trapped easily into a local optimum, this paper improves the standard PSO and proposes a new algorithm to solve the overcomes of the standard PSO. This paper improved the standard PSO's evolution equation on the foundation of analyzing standard PSO's model and its mechanisms, and then presents a Quadratic PSO. The simulation illustrates the Quadratic PSO improves the performance of the PSO We use the new algorithm for the weight optimization in college student evaluation, and compared with PSO; the results show that the new algorithm is efficient.

Published in:

Intelligent Information Technology Application, 2009. IITA 2009. Third International Symposium on  (Volume:3 )

Date of Conference:

21-22 Nov. 2009