Improved salp swarm algorithm based on particle swarm optimization for feature selection

Rehab Ali Ibrahim, Ahmed A. Ewees, Diego Oliva, Mohamed Abd Elaziz, Songfeng Lu

Research output: Contribution to journalArticlepeer-review

68 Citations (Scopus)


Feature selection (FS) is a machine learning process commonly used to reduce the high dimensionality problems of datasets. This task permits to extract the most representative information of high sized pools of data, reducing the computational effort in other tasks as classification. This article presents a hybrid optimization method for the FS problem; it combines the slap swarm algorithm (SSA) with the particle swarm optimization. The hybridization between both approaches creates an algorithm called SSAPSO, in which the efficacy of the exploration and the exploitation steps is improved. To verify the performance of the proposed algorithm, it is tested over two experimental series, in the first one, it is compared with other similar approaches using benchmark functions. Meanwhile, in the second set of experiments, the SSAPSO is used to determine the best set of features using different UCI datasets. Where the redundant or the confusing features are removed from the original dataset while keeping or yielding a better accuracy. The experimental results provide the evidence of the enhancement in the SSAPSO regarding the performance and the accuracy without affecting the computational effort.

Original languageEnglish
Pages (from-to)3155-3169
Number of pages15
JournalJournal of Ambient Intelligence and Humanized Computing
Issue number8
Publication statusPublished - 1 Aug 2019
Externally publishedYes


  • Feature selection
  • Global optimization
  • Particle swarm optimization
  • Salp swarm algorithm
  • Swarm techniques

ASJC Scopus subject areas

  • Computer Science(all)

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