Paper Info
Title
Using opposition-based learning to improve the performance of particle swarm optimization
Abstract
Particle swarm optimization (PSO) is a stochastic, population-based optimization method, which has been applied successfully to a wide range of problems. However, PSO is computationally expensive and suffers from premature convergence. In this paper, opposition-based learning is used to improve the performance of PSO. The performance of the proposed approaches is investigated and compared with PSO when applied to eight benchmark functions. The experiments conducted show that opposition-based learning improves the performance of PSO.
Year
DOI
Venue
2008
10.1109/SIS.2008.4668288
St. Louis, MO
Keywords
Field
DocType
learning (artificial intelligence),particle swarm optimisation,stochastic processes,opposition-based learning,particle swarm optimization,population-based optimization method,stochastic optimization method
Particle swarm optimization,Convergence (routing),Population,Mathematical optimization,Premature convergence,Computer science,Opposition based learning,Evolutionary computation,Stochastic process,Multi-swarm optimization,Artificial intelligence
Conference
ISBN
Citations 
PageRank 
978-1-4244-2705-5
11
0.56
References 
Authors
8
2
Name
Order
Citations
PageRank
Mahamed G. H. Omran164835.28
Salah al-Sharhan210613.21