By Topic

Particle Swarm Optimization Based on Self-organizing Topology Driven by Fitness

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)
Simin Mo ; Coll. of Electr. & Inf. Eng., Lanzhou Univ. of Technol., Lanzhou, China ; Jianchao Zeng ; Ying Tan

To explore the relation between topologic characteristics of dynamic network and performance of the particle swarm optimization (PSO) algorithm, the population of PSO is viewed as a network where each particle is represented as a node and the network structure changes dynamically as the fitness of particles varies. Moreover, in this paper, the structural changes involve adding and removing the links but the network size remains the same. Then, two kinds of simulations are conducted. The results from one kind focusing on PSO show that the dynamic network is capable of balancing exploration and exploitation so that the performance of PSO can be improved as long as the weight θ is selected properly. In addition, the results from other kind concerning on topologic characteristics of dynamical network indicate the impact of network structure on algorithm behavior and the law of network evolution.

Published in:

Computational Aspects of Social Networks (CASoN), 2010 International Conference on

Date of Conference:

26-28 Sept. 2010