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

Seyedali Mirjalili, Siti Zaiton Mohd Hashim, Hossein Moradian Sardroudi

Research output: Contribution to journalArticle

309 Citations (Scopus)

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 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. Crown

Original languageEnglish
Pages (from-to)11125-11137
Number of pages13
JournalApplied Mathematics and Computation
Volume218
Issue number22
DOIs
Publication statusPublished - 15 Jul 2012
Externally publishedYes

Keywords

  • Evolutionary algorithm
  • FNN
  • Gravitational search algorithm
  • Learning neural network
  • Multilayer perceptron
  • Neural network
  • Particle swarm optimization
  • PSO

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