Neural networks learning using vbest model particle swarm optimisation

Hong B.O. Liu; Y. I.Yuan Tang; Jun Meng; Ye Ji

Neural networks learning using vbest model particle swarm optimisation

Hong B.O. Liu, Y. I.Yuan Tang, Jun Meng, Ye Ji

Psychological Sciences

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

15 Scopus citations

Abstract

The two most commonly used methods are known as gbest model and Ibest model in particle swarm optimization (PSO). The gbest model converges quickly on problem solutions but has a weakness of becoming trapped in local optima, while the Ibest model is able to "flow around" local optima, as the individuals explore different regions. In this paper, we investigated a variable neighborhood model in particle swarm search method for neural network learning, and the experimental results illustrated its efficiency.

Original language	English
Title of host publication	Proceedings of 2004 International Conference on Machine Learning and Cybernetics
Pages	3157-3159
Number of pages	3
State	Published - 2004
Event	Proceedings of 2004 International Conference on Machine Learning and Cybernetics - Shanghai, China Duration: Aug 26 2004 → Aug 29 2004

Publication series

Name	Proceedings of 2004 International Conference on Machine Learning and Cybernetics
Volume	5

Conference

Conference	Proceedings of 2004 International Conference on Machine Learning and Cybernetics
Country/Territory	China
City	Shanghai
Period	08/26/04 → 08/29/04

Keywords

Learning Algorithm
Neural Network
Particle Swarm Optimization (PSO)

Cite this

@inproceedings{3ec70197a3e94150acdb83ef0b1dda20,

title = "Neural networks learning using vbest model particle swarm optimisation",

abstract = "The two most commonly used methods are known as gbest model and Ibest model in particle swarm optimization (PSO). The gbest model converges quickly on problem solutions but has a weakness of becoming trapped in local optima, while the Ibest model is able to {"}flow around{"} local optima, as the individuals explore different regions. In this paper, we investigated a variable neighborhood model in particle swarm search method for neural network learning, and the experimental results illustrated its efficiency.",

keywords = "Learning Algorithm, Neural Network, Particle Swarm Optimization (PSO)",

author = "Liu, {Hong B.O.} and Tang, {Y. I.Yuan} and Jun Meng and Ye Ji",

year = "2004",

language = "English",

isbn = "0780384032",

series = "Proceedings of 2004 International Conference on Machine Learning and Cybernetics",

pages = "3157--3159",

booktitle = "Proceedings of 2004 International Conference on Machine Learning and Cybernetics",

note = "Proceedings of 2004 International Conference on Machine Learning and Cybernetics ; Conference date: 26-08-2004 Through 29-08-2004",

}

Liu, HBO, Tang, YIY, Meng, J & Ji, Y 2004, Neural networks learning using vbest model particle swarm optimisation. in Proceedings of 2004 International Conference on Machine Learning and Cybernetics. Proceedings of 2004 International Conference on Machine Learning and Cybernetics, vol. 5, pp. 3157-3159, Proceedings of 2004 International Conference on Machine Learning and Cybernetics, Shanghai, China, 08/26/04.

Neural networks learning using vbest model particle swarm optimisation. / Liu, Hong B.O.; Tang, Y. I.Yuan; Meng, Jun et al.
Proceedings of 2004 International Conference on Machine Learning and Cybernetics. 2004. p. 3157-3159 (Proceedings of 2004 International Conference on Machine Learning and Cybernetics; Vol. 5).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Neural networks learning using vbest model particle swarm optimisation

AU - Liu, Hong B.O.

AU - Tang, Y. I.Yuan

AU - Meng, Jun

AU - Ji, Ye

PY - 2004

Y1 - 2004

N2 - The two most commonly used methods are known as gbest model and Ibest model in particle swarm optimization (PSO). The gbest model converges quickly on problem solutions but has a weakness of becoming trapped in local optima, while the Ibest model is able to "flow around" local optima, as the individuals explore different regions. In this paper, we investigated a variable neighborhood model in particle swarm search method for neural network learning, and the experimental results illustrated its efficiency.

AB - The two most commonly used methods are known as gbest model and Ibest model in particle swarm optimization (PSO). The gbest model converges quickly on problem solutions but has a weakness of becoming trapped in local optima, while the Ibest model is able to "flow around" local optima, as the individuals explore different regions. In this paper, we investigated a variable neighborhood model in particle swarm search method for neural network learning, and the experimental results illustrated its efficiency.

KW - Learning Algorithm

KW - Neural Network

KW - Particle Swarm Optimization (PSO)

UR - http://www.scopus.com/inward/record.url?scp=6344265577&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:6344265577

SN - 0780384032

T3 - Proceedings of 2004 International Conference on Machine Learning and Cybernetics

SP - 3157

EP - 3159

BT - Proceedings of 2004 International Conference on Machine Learning and Cybernetics

T2 - Proceedings of 2004 International Conference on Machine Learning and Cybernetics

Y2 - 26 August 2004 through 29 August 2004

ER -

Neural networks learning using vbest model particle swarm optimisation

Abstract

Publication series

Conference

Keywords

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