CPSOGSA for MLP Training

The constriction coefficient based particle swarm optimization and gravitational search algorithm has been utilized for MLP trainiing.
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Aggiornato 6 lug 2020

In this paper, a newly proposed hybridization algorithm namely constriction coefficient-based particle swarm optimization and gravitational search algorithm (CPSOGSA) has been employed for training MLP to overcome sensitivity to initialization, premature convergence, and stagnation in local optima problems of MLP.

This is the source code of the paper:

Rather, S.A. and Bala, P.S. (2020), "A hybrid constriction coefficient-based particle swarm optimization and gravitational search algorithm for training multi-layer perceptron", International Journal of Intelligent Computing and Cybernetics, Vol. 13 No. 2, pp. 129-165. https://doi.org/10.1108/IJICC-09-2019-0105

If you have no access to the paper, please drop me an email at sajad.win8@gmail.com and I will obviously send you the paper. All of the source codes and extra information as well as more optimization techniques can be found in my Github page at https://github.com/SajadAHMAD1.

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Cita come

Sajad Ahmad Rather (2024). CPSOGSA for MLP Training (https://github.com/SajadAHMAD1/CPSOGSA-for-MLP-Training/releases/tag/1.1.1), GitHub. Recuperato .

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Creato con R2013b
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Versione Pubblicato Note della release
1.1.1

See release notes for this release on GitHub: https://github.com/SajadAHMAD1/CPSOGSA-for-MLP-Training/releases/tag/1.1.1

1.1.0

See release notes for this release on GitHub: https://github.com/SajadAHMAD1/CPSOGSA-for-MLP-Training/releases/tag/1.1.0

1.0.0

Per visualizzare o segnalare problemi su questo componente aggiuntivo di GitHub, visita GitHub Repository.
Per visualizzare o segnalare problemi su questo componente aggiuntivo di GitHub, visita GitHub Repository.