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Quantized Salp Swarm Algorithm (QSSA) for optimal feature selection

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Abstract

Metaheuristic algorithms are well-known and widely used strategies for tackling optimization issues. Each has advantages and limitations and is frequently combined with other algorithms to compensate for flaws. The basic Salp Swarm Algorithm (SSA) is simple to use and often effective when solving real-world optimization problems, but it can sometimes get stuck at local optima, leading to premature convergence. The main reasons for this are the poor population diversity, the lack of exploitable resources, and the exploration capabilities being insufficient. A modified SSA algorithm called quantized SSA (QSSA) is suggested to improve performance. The proposed method has incorporated a mathematical operator called the quantization operator into the basic SSA. The main goal of incorporating quantization operator is to improve population diversity and local usage, which can help in finding the solution space more effectively, thereby enabling faster convergence. The suggested QSSA approach is validated through IEEE-CEC-2014 Basic functions. Further, as an application, the same methodology is used to select the finest features from benchmark datasets while retaining accuracy and reducing neural network complexity.

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Data availability

The datasets analysed during the current study are available in the KAGGLE repository. https://www.kaggle.com/datasets

Abbreviations

HCEF:

High-conditioned elliptic function

BCF:

Bent-cigar function

DF:

Discuss function

RBF:

Rosenbrock’s function

AF:

Ackley’s function

WF:

Weierstrass function

GF:

Griewank’s function

RF:

Rastrigin’s function

SF:

Schwefel’s function

KF:

Katsuura function

HCF:

HappyCat function

HGBF:

HGBat function

EGRF:

Expanded Griewank’s plus Rosenbrock’s function

ESF6:

Expanded Scaffer’s F6 function

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Authors and Affiliations

  1. Department of CSIT, Institute of Technical Education and Research, Siksha ‘O’ Anusandhan (Deemed to be) University, Bhubaneswar, India

    Ajit Kumar Mahapatra

  2. School of Computer Engineering, KIIT Deemed to be University, Bhubaneswar, Odisha, 751024, India

    Nibedan Panda

  3. Department of CSE, Institute of Technical Education and Research, Siksha ‘O’ Anusandhan (Deemed to be) University, Bhubaneswar, India

    Binod Kumar Pattanayak

Authors
  1. Ajit Kumar Mahapatra
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  2. Nibedan Panda
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  3. Binod Kumar Pattanayak
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Correspondence to Nibedan Panda.

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Mahapatra, A.K., Panda, N. & Pattanayak, B.K. Quantized Salp Swarm Algorithm (QSSA) for optimal feature selection. Int. j. inf. tecnol. 15, 725–734 (2023). https://doi.org/10.1007/s41870-023-01161-6

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