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Quantized Orthogonal Experimentation SSA (QOX-SSA): A Hybrid Technique for Feature Selection (FS) and Neural Network Training

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Abstract

The standard metaheuristics are commonly based on a single metaphorical model miming a particular animal group's food-searching behaviour. As a result, the contributions of such approaches are becoming inadequate in dealing with the evolving complexities of optimizing objective functions. Salp Swarm Algorithm (SSA) is one of them, a new swarm intelligence-based technique relying on a lone metaphor introduced for tackling global optimization issues. Nonetheless, SSA has garnered substantial acknowledgement and attraction among the research community because it is easy to implement and requires few control parameters to fine-tune. However, the typical SSA encounters confinement issues in local optima and an insufficient convergence pace when confronted with more intricate scenarios due to deficient population diversity, local exploitation, and global exploration. Therefore, this research integrates a Quantized Orthogonal Experimentation (QOX) operator to enhance population variety and intensify SSA's local exploitation and global explorative potential. The resulting hybrid approach is named QOX-SSA. QOX-SSA's optimization skill is demonstrated using 14 fundamental and 30 advanced benchmark problems of IEEE-CEC-2014 and comparing its effectiveness to some contemporary metaheuristics. Three nonparametric tests are conducted to verify the statistical importance of QOX-SSA's results. Furthermore, the applicability of QOX-SSA is examined by implementing it to train the Radial Basis Function Neural Network to classify data and resolve problems related to optimal feature selection. Experimental outcomes of QOX-SSA over different optimization issues confirm its superior performance compared to SSA and the alternate metaheuristics used for comparative analysis.

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Abbreviations

RBFNN:

Radial basis function neural network

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 Schaffer's F6 function

Iono:

Ionosphere

WBC:

Wisconsin breast cancer

Pima:

Pima Indian diabetes

Heart:

Heart

E. coli:

E. coli

liver:

Liver

Hepa:

Hepatitis

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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

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  1. Ajit Kumar Mahapatra
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Correspondence to Nibedan Panda.

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Mahapatra, A.K., Panda, N. & Pattanayak, B.K. Quantized Orthogonal Experimentation SSA (QOX-SSA): A Hybrid Technique for Feature Selection (FS) and Neural Network Training. Arab J Sci Eng (2024). https://doi.org/10.1007/s13369-024-09113-3

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