Evolving Radial Basis Function Networks Using Moth-Flame Optimizer

Hossam Faris, Ibrahim Aljarah, Seyedali Mirjalili

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

44 Citations (Scopus)


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.
Number of pages14
ISBN (Electronic)9780128113196
ISBN (Print)9780128113189
Publication statusPublished - 1 Jan 2017
Externally publishedYes


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


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