DocumentCode
2694388
Title
An improved particle swarm optimizer with momentum
Author
Xiang, Tao ; Wang, Jun ; Liao, Xiaofeng
Author_Institution
Chongqing Univ., Chongqing
fYear
2007
fDate
25-28 Sept. 2007
Firstpage
3341
Lastpage
3345
Abstract
In this paper, an improved particle swarm optimization algorithm with momentum (mPSO) is proposed based on inspiration from the back propagation (BP) learning algorithm with momentum in neural networks. The momentum acts as a lowpass filter to relieve excessive oscillation and also extends the PSO velocity updating equation to a second-order difference equation. Experimental results are shown to verify its superiority both in robustness and efficiency.
Keywords
backpropagation; difference equations; neural nets; particle swarm optimisation; back propagation learning algorithm; lowpass filter; momentum; neural networks; particle swarm optimization; second-order difference equation; Acceleration; Birds; Cultural differences; Difference equations; Educational institutions; Filters; Marine animals; Neural networks; Particle swarm optimization; Robustness;
fLanguage
English
Publisher
ieee
Conference_Titel
Evolutionary Computation, 2007. CEC 2007. IEEE Congress on
Conference_Location
Singapore
Print_ISBN
978-1-4244-1339-3
Electronic_ISBN
978-1-4244-1340-9
Type
conf
DOI
10.1109/CEC.2007.4424903
Filename
4424903
Link To Document