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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Updated 6 Jul 2020

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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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Cite As

Sajad Ahmad Rather (2023). CPSOGSA for MLP Training (https://github.com/SajadAHMAD1/CPSOGSA-for-MLP-Training/releases/tag/1.1.1), GitHub. Retrieved .

MATLAB Release Compatibility
Created with R2013b
Compatible with any release
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Version Published Release Notes
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

To view or report issues in this GitHub add-on, visit the GitHub Repository.
To view or report issues in this GitHub add-on, visit the GitHub Repository.