Adaptive Particle Swarm Optimization for Continuous Domain

Abstract:

Article Preview

An Adaptive particle swarm optimization algorithm is proposed. Algorithm combines with pareto local search (PLS) method and adaptively adjusts flying time. During the search process, our algorithm can enhance the local search ability of particle swarm optimization (PSO) thought adding random perturbation to local search. The flying time of every particle in our algorithm can adaptively adjust with the evolutionary generations. Some optimization tests of the standard benchmark function confirm that our method has a stronger ability of global optimization.

Info:

Periodical:

Advanced Materials Research (Volumes 204-210)

Edited by:

Helen Zhang, Gang Shen and David Jin

Pages:

1139-1142

DOI:

10.4028/www.scientific.net/AMR.204-210.1139

Citation:

C. M. Qi "Adaptive Particle Swarm Optimization for Continuous Domain", Advanced Materials Research, Vols. 204-210, pp. 1139-1142, 2011

Online since:

February 2011

Authors:

Export:

Price:

$35.00

In order to see related information, you need to Login.

In order to see related information, you need to Login.