Training Artificial Neural Networks Using Lévy Group Search Optimizer

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14 Citations (Scopus)


Inspired by animal behavior, especially animal searching behavior, a novel swarm intelligence algorithm Group Search Optimizer (GSO) has been proposed recently [20]. In this paper, we propose a new artificial neural network (ANN) training algorithm based on an improve GSO algorithm. We replace the gaussian random walk in the standard GSO with Levy flight, which is a random search patterns adopted by many organisms to maximize the efficiency of resource searches in uncertain environments. We firstly evaluate the improved GSO with Levy flight (LGSO) on a set of 5 optimization benchmark functions. We then apply the LGSO algorithm to tune the parameters of a 3-layer feed-forward ANN, including connection weights and bias. Two real-world problems, namely Cleveland heart disease classification problem and sunspot number forecasting problem, have been employed to assess the performance of our LGSO-trained ANN (LGSOANN). In comparison with other sophisticated machine learning techniques proposed in recent years such as ANN ensembles, LGSOANN has better convergence and generalization performance on the two real-world problems.
Original languageEnglish
Pages (from-to)527-545
Number of pages19
JournalJournal of Multiple-Valued Logic and Soft Computing
Issue number6
Publication statusPublished - 1 Jan 2010


  • Group Search Optimizer
  • Levy Flight
  • Evolutionary Algorithm
  • Animal Behavior
  • Optimization
  • Swarm Intelligence


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