By Topic

A New Particle Swarm Optimization Algorithm with Random Inertia Weight and Evolution Strategy

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)
Gao Yue-lin ; North Nat. Univ., Yinchuan ; Duan Yu-hong

The paper gives a new particle swarm optimization algorithm with random inertia weight and evolution strategy (REPSO). The proposed random inertia weight is using simulated annealing idea and the given evolution strategy is using the fitness variance of particles to improve the global search ability of PSO. The experiments with six benchmark functions show that the convergent speed and accuracy of REPSO is significantly superior to the one of The PSO with linearly decreasing inertia weight LDW-PSO.

Published in:

Computational Intelligence and Security Workshops, 2007. CISW 2007. International Conference on

Date of Conference:

15-19 Dec. 2007