Evolving Radial Basis Function Networks Using Moth-Flame Optimizer

Hossam Faris; Ibrahim Aljarah; Seyedali Mirjalili

doi:10.1016/B978-0-12-811318-9.00028-4

Evolving Radial Basis Function Networks Using Moth-Flame Optimizer

Hossam Faris, Ibrahim Aljarah, Seyedali Mirjalili

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

18 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 language	English
Title of host publication	Handbook of Neural Computation
Publisher	Elsevier Inc.
Pages	537-550
Number of pages	14
ISBN (Electronic)	9780128113196
ISBN (Print)	9780128113189
DOIs	https://doi.org/10.1016/B978-0-12-811318-9.00028-4
Publication status	Published - 1 Jan 2017
Externally published	Yes

Keywords

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

Access to Document

10.1016/B978-0-12-811318-9.00028-4

Link to publication in Scopus

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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.",

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