Evolving neural networks using bird swarm algorithm for data classification and regression applications

Ibrahim Aljarah, Hossam Faris, Seyedali Mirjalili, Nailah Al-Madi, Alaa Sheta, Majdi Mafarja

Research output: Contribution to journalArticlepeer-review

20 Citations (Scopus)


This work proposes a new evolutionary multilayer perceptron neural networks using the recently proposed Bird Swarm Algorithm. The problem of finding the optimal connection weights and neuron biases is first formulated as a minimization problem with mean square error as the objective function. The BSA is then used to estimate the global optimum for this problem. A comprehensive comparative study is conducted using 13 classification datasets, three function approximation datasets, and one real-world case study (Tennessee Eastman chemical reactor problem) to benchmark the performance of the proposed evolutionary neural network. The results are compared with well-regarded conventional and evolutionary trainers and show that the proposed method provides very competitive results. The paper also considers a deep analysis of the results, revealing the flexibility, robustness, and reliability of the proposed trainer when applied to different datasets.

Original languageEnglish
JournalCluster Computing
Publication statusPublished - 1 Jan 2019
Externally publishedYes


  • Bird Swarm Algorithm
  • Classification
  • Multilayer perceptron
  • Neural networks
  • Optimization
  • Regression


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