DocumentCode
518687
Title
Notice of Retraction
Particle swarm optimization algorithm based on variable metric method and its application of non-linear equations
Author
Gao Lei-fu ; Qi Wei ; Liu Xu-wang
Author_Institution
Inst. of Math. & Syst. Sci., Liaoning Tech. Univ., Fuxin, China
Volume
3
fYear
2010
fDate
27-29 March 2010
Firstpage
514
Lastpage
518
Abstract
Notice of Retraction
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
In this paper, particle swarm optimization algorithm based on variable metric method is proposed for the defects of elementary particle swarm optimization algorithm “premature” and the parameter setting. The algorithm uses fast local convergence characteristics of the variable metric method, so that the improved algorithm can jump out of local optimal solution effectively, and can also search the global optimal solution quickly. Simulation results show that the new algorithm improves the accuracy of the optimal solution and optimization efficiency; also demonstrate that the new algorithm has better robustness, and then the improved algorithm is successfully applied to solve the problem of nonlinear equations.
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
In this paper, particle swarm optimization algorithm based on variable metric method is proposed for the defects of elementary particle swarm optimization algorithm “premature” and the parameter setting. The algorithm uses fast local convergence characteristics of the variable metric method, so that the improved algorithm can jump out of local optimal solution effectively, and can also search the global optimal solution quickly. Simulation results show that the new algorithm improves the accuracy of the optimal solution and optimization efficiency; also demonstrate that the new algorithm has better robustness, and then the improved algorithm is successfully applied to solve the problem of nonlinear equations.
Keywords
convergence; nonlinear equations; particle swarm optimisation; elementary particle swarm optimization algorithm; global optimal solution; local convergence characteristics; nonlinear equations; robustness; variable metric method; Algorithm design and analysis; Clustering algorithms; Constraint optimization; Elementary particles; Load flow; Mathematics; Nonlinear equations; Particle swarm optimization; Robustness; Vehicles; Global Optimization; Particle swarm optimization algorithm; nonlinear equations; variable metric algorithm;
fLanguage
English
Publisher
ieee
Conference_Titel
Advanced Computer Control (ICACC), 2010 2nd International Conference on
Conference_Location
Shenyang
Print_ISBN
978-1-4244-5845-5
Type
conf
DOI
10.1109/ICACC.2010.5486803
Filename
5486803
Link To Document