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Improved salp swarm algorithm based on the levy flight for feature selection

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Abstract

The fields of data science and data mining are enduring high-dimensionality issues because of a high volume of data. Conventional machine learning techniques give disgruntled responses to high-dimensional datasets. Feature selection is used to get the appropriate information from the dataset to reduce the dimensionality of the data. The recently proposed Salp Swarm Algorithm (SSA) is a population-based meta-heuristic optimization technique inspired by the Sea Salps Swarming technique. SSA failed to converge initial random solutions to the global optimum owing to its complete dependency on the number of iterations for the process of exploration and exploitation. The proposed improved SSA (iSSA) aims to enhance the ability of Salps to explore divergent areas by randomly updating its location. Randomizing the Salps location via Levy flight enriches the exploitation potential of SSA resulting in it converging the model toward the global optima. The performance of the proposed iSSA is investigated using six different high-dimensional microarray datasets. While comparing the ability to converge, it is understood that the proposed model outperforms SSA providing 0.1033% more confidence in the selected features. The results of the simulation revealed that the iSSA can provide better competitive and significant results compared to SSA.

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Acknowledgements

The authors sincerely thank the Department of Science and Technology (DST), Government of India for funding this research project work under the Interdisciplinary Cyber Physical Systems (ICPS) scheme (Grant No. T-54).

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Indian Institute of Information Technology Tiruchirappalli, Tiruchirappalli, Tamil Nadu, 620009, India

    K. Balakrishnan & R. Dhanalakshmi

  2. Department of Data Science and Intelligent Systems, Indian Institute of Information Technology Dharwad, Sattur, Karnataka, 580009, India

    Utkarsh Mahadeo Khaire

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Balakrishnan, K., Dhanalakshmi, R. & Khaire, U.M. Improved salp swarm algorithm based on the levy flight for feature selection. J Supercomput 77, 12399–12419 (2021). https://doi.org/10.1007/s11227-021-03773-w

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