Evolving Radial Basis Function Networks Using Moth-Flame Optimizer

Hossam Faris, Ibrahim Aljarah, Seyedali Mirjalili

Research output: Chapter in Book/Report/Conference proceedingChapter

13 Citations (Scopus)

Abstract

This book chapter proposes a new training algorithms for Radial Basis Function (RBF) using a recently proposed optimization algorithm called Moth-Flame Optimizer (MFO). After formulating MFO as RBFN trainer, seven standard binary classifications are employed as case studies. The MFO-based trainer is compared with Particle Swarm Algorithm (PSO), Genetic Algorithm (GA), Bat Algorithm (BA), and newrb. The results show that the proposed trainer is able to show superior results on the majority of case studies. The observation of convergence behavior proves that this new trainer benefits from accelerating convergence speed as well.

Original languageEnglish
Title of host publicationHandbook of Neural Computation
PublisherElsevier Inc.
Pages537-550
Number of pages14
ISBN (Electronic)9780128113196
ISBN (Print)9780128113189
DOIs
Publication statusPublished - 1 Jan 2017
Externally publishedYes

Keywords

  • Metaheuristics
  • MFO
  • Moth Flame Optimizer
  • Neural Networks
  • Optimization
  • Radial Basis Function
  • RBF
  • Training

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  • Cite this

    Faris, H., Aljarah, I., & Mirjalili, S. (2017). Evolving Radial Basis Function Networks Using Moth-Flame Optimizer. In Handbook of Neural Computation (pp. 537-550). Elsevier Inc.. https://doi.org/10.1016/B978-0-12-811318-9.00028-4