An improved particle swarm optimizer with momentum

Tao Xiang, Jun Wang, Xiaofeng Liao

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

13 Citations (Scopus)

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.

Original languageEnglish
Title of host publication2007 IEEE Congress on Evolutionary Computation, CEC 2007
Pages3341-3345
Number of pages5
DOIs
Publication statusPublished - 2007
Externally publishedYes
Event2007 IEEE Congress on Evolutionary Computation, CEC 2007 - , Singapore
Duration: 25 Sep 200728 Sep 2007

Publication series

Name2007 IEEE Congress on Evolutionary Computation, CEC 2007

Conference

Conference2007 IEEE Congress on Evolutionary Computation, CEC 2007
Country/TerritorySingapore
Period25/09/0728/09/07

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