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  • Training feedforward neural networks using hybrid particle swarm optimization and gravitational search algorithm

    Author(s)
    Mirjalili, SeyedAli
    Hashim, Siti Zaiton Mohd
    Sardroudi, Hossein Moradian
    Griffith University Author(s)
    Mirjalili, Seyedali
    Year published
    2012
    Metadata
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    Abstract
    The Gravitational Search Algorithm (GSA) is a novel heuristic optimization method based on the law of gravity and mass interactions. It has been proven that this algorithm has good ability to search for the global optimum, but it suffers from slow searching speed in the last iterations. This work proposes a hybrid of Particle Swarm Optimization (PSO) and GSA to resolve the aforementioned problem. In this paper, GSA and PSOGSA are employed as new training methods for Feedforward Neural Networks (FNNs) in order to investigate the efficiencies of these algorithms in reducing the problems of trapping in local minima and the slow ...
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    The Gravitational Search Algorithm (GSA) is a novel heuristic optimization method based on the law of gravity and mass interactions. It has been proven that this algorithm has good ability to search for the global optimum, but it suffers from slow searching speed in the last iterations. This work proposes a hybrid of Particle Swarm Optimization (PSO) and GSA to resolve the aforementioned problem. In this paper, GSA and PSOGSA are employed as new training methods for Feedforward Neural Networks (FNNs) in order to investigate the efficiencies of these algorithms in reducing the problems of trapping in local minima and the slow convergence rate of current evolutionary learning algorithms. The results are compared with a standard PSO-based learning algorithm for FNNs. The resulting accuracy of FNNs trained with PSO, GSA, and PSOGSA is also investigated. The experimental results show that PSOGSA outperforms both PSO and GSA for training FNNs in terms of converging speed and avoiding local minima. It is also proven that an FNN trained with PSOGSA has better accuracy than one trained with GSA.
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    Journal Title
    Applied Mathematics and Computation
    Volume
    218
    Issue
    22
    DOI
    https://doi.org/10.1016/j.amc.2012.04.069
    Subject
    Applied mathematics
    Numerical and computational mathematics
    Theory of computation
    Publication URI
    http://hdl.handle.net/10072/48567
    Collection
    • Journal articles

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