Designing evolutionary feedforward neural networks using social spider optimization algorithm

Seyedeh Zahra Mirjalili, Shahrzad Saremi, Seyed Mohammad Mirjalili

Research output: Contribution to journalArticlepeer-review

48 Citations (Scopus)

Abstract

Training feedforward neural networks (FNNs) is considered as a challenging task due to the nonlinear nature of this problem and the presence of large number of local solutions. The literature shows that heuristic optimization algorithms are able to tackle these problems much better than the mathematical and deterministic methods. In this paper, we propose a new trainer using the recently proposed heuristic algorithm called social spider optimization (SSO) algorithm. The trained FNN by SSO (FNNSSO) is benchmarked on five standard classification data sets: XOR, balloon, Iris, breast cancer, and heart. The results are verified by the comparison with five other well-known heuristics. The results prove that the proposed FNNSSO is able to provide very promising results compared with other algorithms.

Original languageEnglish
Pages (from-to)1919-1928
Number of pages10
JournalNeural Computing and Applications
Volume26
Issue number8
DOIs
Publication statusPublished - 22 Nov 2015
Externally publishedYes

Keywords

  • Feedforward neural networks
  • Learning
  • Optimization
  • Social spider optimization
  • Training neural network

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