Paper Info
Title
An Improved PSO and Its Application in Research on Reservoir Operation Function of Long-Term
Abstract
An improved Particle Swarm Optimization called IPSO is presented in this article. By importing mutation operator and adopting self-adapting inertia, the IPSO overcomes the local convergence problem and slow later convergence problem of the basic PSO effectively. The variance analysis is applied to evaluate the optimization effectiveness of key parameters of IPSO. In order to test the performance of IPSO, it is applied to the research on the function of reservoir long-term operation. In the case study, the reservoir operation function achieved by IPSO is tested and the superiority is apparent compared with two other ones that are obtained by PSO and through regression analysis on the simulation operation course gained by the progressive optimization arithmetic respectively. The application results fully show that IPSO is a promising and valuable optimization method, meaning that a novel and effective solution is provided for complex optimization problems.
Year
DOI
Venue
2007
10.1109/ICNC.2007.224
ICNC
Keywords
Field
DocType
improved pso,complex optimization problem,local convergence problem,basic pso,reservoir operation function,progressive optimization arithmetic,optimization effectiveness,reservoir long-term operation,simulation operation course,convergence problem,valuable optimization method,reservoir operation,variance analysis,regression analysis,optimization problem,particle swarm optimization,reservoirs,convergence,local convergence
Convergence (routing),Particle swarm optimization,Mathematical optimization,Reservoir operation,Computer science,Convergence problem,Local convergence,Artificial intelligence,Inertia,Optimization problem,Machine learning,Mutation operator
Conference
Volume
ISSN
ISBN
4
2157-9555
0-7695-2875-9
Citations 
PageRank 
References 
1
0.41
2
Authors
4
Name
Order
Citations
PageRank
Ming Zhang18918.62
Chengjun Li2286.60
Xiao-Hui Yuan353475.44
Yongchuan Zhang418321.99