By Topic

Mixed Using Artificial Fish - Particle Swarm Optimization Algorithm for Hyperspace Basing on Local Searching

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

4 Author(s)
Wei Gao ; Shenyang Inst. of Chem. Technol., Northeastern Univ., Shenyang, China ; Hai Zhao ; Chunhe Song ; Jiuqiang Xu

With many advantages of computing with real number, few parameters to be adjusted, the Particle Swarm Optimizer (PSO) is applied in many fields. The major problem with PSO algorithm is premature convergence. Some optimization strategies were introduced to overcome it. In these former researches, the dimension of benchmarks in experiments was usually set to be a small value. But it can be seen that when the benchmark is with high dimension, the basic PSO and some advantage versions can not converge to a satisfied point. This paper presents a new particle swarm optimizer algorithm-AF-PSO. The AF-PSO uses the adaptive-trying strategy to accelerate the particle swarm convergence speed. To avoid premature convergence of the swarm, adaptive-mutation is also adopted. The HPSO is compared with the BPSO and GCPSO, the experiment result shows that the new algorithm performances better on a four-function test suite with high-dimension.

Published in:

Bioinformatics and Biomedical Engineering , 2009. ICBBE 2009. 3rd International Conference on

Date of Conference:

11-13 June 2009