Hybrid Whale Optimization Algorithm with simulated annealing for feature selection

Majdi M. Mafarja, Seyedali Mirjalili

Research output: Contribution to journalArticlepeer-review

693 Citations (Scopus)


Hybrid metaheuristics are of the most interesting recent trends in optimization and memetic algorithms. In this paper, two hybridization models are used to design different feature selection techniques based on Whale Optimization Algorithm (WOA). In the first model, Simulated Annealing (SA) algorithm is embedded in WOA algorithm, while it is used to improve the best solution found after each iteration of WOA algorithm in the second model. The goal of using SA here is to enhance the exploitation by searching the most promising regions located by WOA algorithm. The performance of the proposed approaches is evaluated on 18 standard benchmark datasets from UCI repository and compared with three well-known wrapper feature selection methods in the literature. The experimental results confirm the efficiency of the proposed approaches in improving the classification accuracy compared to other wrapper-based algorithms, which insures the ability of WOA algorithm in searching the feature space and selecting the most informative attributes for classification tasks.

Original languageEnglish
Pages (from-to)302-312
Number of pages11
Publication statusPublished - 18 Oct 2017
Externally publishedYes


  • Classification
  • Feature selection
  • Hybrid optimization
  • Optimization
  • Simulated annealing
  • Whale Optimization Algorithm
  • WOA


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