Browsing by Author "Shekhawat, S"

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    Laplacian atom search optimization algorithm: development and application for harmonic estimator design
    (2024-04) Saxena, A; Shekhawat, S; Kumar, R; Mehta, A; Jangid, J
    Harmonics, are the major source of contaminations in fundamental voltage and current signals. For consumer satisfaction and for better equipment performance, these contaminations shall be identified and mitigated. Although, for accurate identification of harmonics, several methods have been proposed, yet metaheuristic based approaches have been used and in practice, since last two decades. The work reported here is based on a newly proposed optimization algorithm named as Atom Search Optimisation (ASO). In the first phase, a variant of ASO is proposed, based on Laplacian operator based position update mechanism named as Laplacian-ASO (L-ASO) for enhancing the performance of ASO and in the next phase, application of a newly developed L-ASO is carried out on harmonic estimator design problems. The improvisation of our proposed L-ASO is validated through con ducted analyses and results showcased in the discussion section.
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    Performance Evaluation of Ingenious Crow Search Optimization Algorithm for Protein Structure Prediction
    (2023-05) Alshamrani, A; Saxena, A; Shekhawat, S
    Protein structure prediction is one of the important aspects while dealing with critical diseases. An early prediction of protein folding helps in clinical diagnosis. In recent years, applications of metaheuristic algorithms have been substantially increased due to the fact that this problem is computationally complex and time-consuming. Metaheuristics are proven to be an adequate tool for dealing with complex problems with higher computational efficiency than conventional tools. The work presented in this paper is the development and testing of the Ingenious Crow Search Algorithm (ICSA). First, the algorithm is tested on standard mathematical functions with known properties. Then, the application of newly developed ICSA is explored on protein structure prediction. The efficacy of this algorithm is tested on a bench of artificial proteins and real proteins of medium length. The comparative analysis of the optimization performance is carried out with some of the leading variants of the crow search algorithm (CSA). The statistical comparison of the results shows the supremacy of the ICSA for almost all protein sequences.
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    Performance Evaluation of Ingenious Crow Search Optimization Algorithm for Protein Structure Prediction
    (2023-05) Alshamrani, A; Saxena, A; Shekhawat, S; Zawbaa, H
    Protein structure prediction is one of the important aspects while dealing with critical diseases. An early prediction of protein folding helps in clinical diagnosis. In recent years, applications of metaheuristic algorithms have been substantially increased due to the fact that this problem is computationally complex and time-consuming. Metaheuristics are proven to be an adequate tool for dealing with complex problems with higher computational efficiency than conventional tools. The work presented in this paper is the development and testing of the Ingenious Crow Search Algorithm (ICSA). First, the algorithm is tested on standard mathematical functions with known properties. Then, the application of newly developed ICSA is explored on protein structure prediction. The efficacy of this algorithm is tested on a bench of artificial proteins and real proteins of medium length. The comparative analysis of the optimization performance is carried out with some of the leading variants of the crow search algorithm (CSA). The statistical comparison of the results shows the supremacy of the ICSA for almost all protein sequences.