Neural Computing and Applications

, Volume 29, Issue 7, pp 529–553 | Cite as

Training radial basis function networks using biogeography-based optimizer

  • Ibrahim Aljarah
  • Hossam Faris
  • Seyedali Mirjalili
  • Nailah Al-Madi
Original Article


Training artificial neural networks is considered as one of the most challenging machine learning problems. This is mainly due to the presence of a large number of solutions and changes in the search space for different datasets. Conventional training techniques mostly suffer from local optima stagnation and degraded convergence, which make them impractical for datasets with many features. The literature shows that stochastic population-based optimization techniques suit this problem better and are reliably alternative because of high local optima avoidance and flexibility. For the first time, this work proposes a new learning mechanism for radial basis function networks based on biogeography-based optimizer as one of the most well-regarded optimizers in the literature. To prove the efficacy of the proposed methodology, it is employed to solve 12 well-known datasets and compared to 11 current training algorithms including gradient-based and stochastic approaches. The paper considers changing the number of neurons and investigating the performance of algorithms on radial basis function networks with different number of parameters as well. A statistical test is also conducted to judge about the significance of the results. The results show that the biogeography-based optimizer trainer is able to substantially outperform the current training algorithms on all datasets in terms of classification accuracy, speed of convergence, and entrapment in local optima. In addition, the comparison of trainers on radial basis function networks with different neurons size reveal that the biogeography-based optimizer trainer is able to train radial basis function networks with different number of structural parameters effectively.


Optimization Biogeography-based optimizer BBO Radial basis function network Training neural network Evolutionary algorithm 


Compliance with ethical standards

Conflict of interest

The authors declare that there is no conflict of interest regarding the publication of this paper.


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Copyright information

© The Natural Computing Applications Forum 2016

Authors and Affiliations

  1. 1.Business Information Technology Department, King Abdullah II School for Information TechnologyThe University of JordanAmmanJordan
  2. 2.School of Information and Communication TechnologyGriffith UniversityNathanAustralia
  3. 3.The King Hussein Faculty of Computing SciencesPrincess Sumaya University for TechnologyAmmanJordan

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