By Topic

Using stochastic dynamic step length particle swarm optimization to direct orbits of chaotic systems

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
$33 $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)
Xingjuan Cai ; Complex System and Computational Intelligence Laboratory Taiyuan University of Science and Technology, Shanxi, P.R. China, 030024 ; Zhihua Cui

Stochastic dynamic step length particle swarm optimization (SDSLPSO) is a new novel variant of particle swarm optimization that incorporating the dynamic step length for each particle in each iteration. This strategy simulates the phenomenon that each bird adjusts its velocity automatically in the process to finding the prey. In this paper, SD-SLPSO is employed to solve the directing orbits of chaotic systems, simulation results show this new variant increases the performance significantly when compared with the standard version of particle swarm optimization.

Published in:

Cognitive Informatics (ICCI), 2010 9th IEEE International Conference on

Date of Conference:

7-9 July 2010