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Topology optimization of neural networks based on a coupled genetic algorithm and particle swarm optimization techniques (c-GA–PSO-NN)

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Abstract

In this short paper, a coupled genetic algorithm and particle swarm optimization technique was used to supervise neural networks where the applied operators and connections of layers were tracked by genetic algorithm and numeric values of biases and weights of layers were examined by particle swarm optimization to modify the optimal network topology. The method was applied for a previously studied case, and results were analyzed. The convergence to the optimal topology was highly fast and efficient, and the obtained weights and biases revealed great reliability in reproduction of data. The optimal topology of neural networks was obtained only after seven iterations, and an average square of the correlation (R 2) of 0.9989 was obtained for the studied cases. The proposed method can be used for fast and reliable topology optimization of neural networks.

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Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

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Authors and Affiliations

  1. Department of Chemistry, Arak Branch, Islamic Azad University, Arak, Iran

    Azam Marjani

  2. Department of Chemical and Environmental Sciences, Bernal Institute, University of Limerick, Limerick, Ireland

    Saeed Shirazian

  3. Young Researchers and Elite Club, South Tehran Branch, Islamic Azad University, Tehran, Iran

    Mehdi Asadollahzadeh

Authors
  1. Azam Marjani
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  2. Saeed Shirazian
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  3. Mehdi Asadollahzadeh
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Correspondence to Mehdi Asadollahzadeh.

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Marjani, A., Shirazian, S. & Asadollahzadeh, M. Topology optimization of neural networks based on a coupled genetic algorithm and particle swarm optimization techniques (c-GA–PSO-NN). Neural Comput & Applic 29, 1073–1076 (2018). https://doi.org/10.1007/s00521-016-2619-7

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  • DOI: https://doi.org/10.1007/s00521-016-2619-7

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