Browsing by Author "Saxena, A"

Now showing 1 - 8 of 8
  • Thumbnail Image
    Item
    An Amended Crow Search Algorithm for Hybrid Active Power Filter Design
    (2023-08) Ali, S; Bhargava, A; Saxena, A
    Hybrid Active Power Filter (HAPF) imbibes the advantages of both passive and active power filters. These filters are considered one of the important technologies for mitigating harmonic pollution in electrical systems. Accurate estimation of filter parameters is a key component to reduce harmonic pollution effectively. In recent years, several optimization approaches have been reported to solve this estimation problem; still, this area is worthy of further investigation. This paper is a proposal for an estimator that can estimate the parameter of HAPF configuration accurately. For evolving this estimator, first, an objective function that mathematically embeds filter parameters and harmonic pollution is presented. For handling the optimization process, an Amended Crow Search Algorithm (ACSA) is proposed. ACSA employs a local search algorithm (in the form of a pattern search) for obtaining optimal results. The analysis of the estimation process is carried out on two HAPF configurations. Various analyses that include harmonic pollution statistical analysis along with fitness function value analysis reveal that the proposed algorithm acquires optimal results as compared with other recently published and reported algorithms. Further, the proposed filter configurations are tested with the existing filter. The results prove that the proposed filter shows promising results.
  • Thumbnail Image
    Item
    An Amended Crow Search Algorithm for Hybrid Active Power Filter Design
    (2023-08) Ali, S; Bhargava, A; Saxena, A
    Hybrid Active Power Filter (HAPF) imbibes the advantages of both passive and active power filters. These filters are considered one of the important technologies for mitigating harmonic pollution in electrical systems. Accurate estimation of filter parameters is a key component to reduce harmonic pollution effectively. In recent years, several optimization approaches have been reported to solve this estimation problem; still, this area is worthy of further investigation. This paper is a proposal for an estimator that can estimate the parameter of HAPF configuration accurately. For evolving this estimator, first, an objective function that mathematically embeds filter parameters and harmonic pollution is presented. For handling the optimization process, an Amended Crow Search Algorithm (ACSA) is proposed. ACSA employs a local search algorithm (in the form of a pattern search) for obtaining optimal results. The analysis of the estimation process is carried out on two HAPF configurations. Various analyses that include harmonic pollution statistical analysis along with fitness function value analysis reveal that the proposed algorithm acquires optimal results as compared with other recently published and reported algorithms. Further, the proposed filter configurations are tested with the existing filter. The results prove that the proposed filter shows promising results.
  • Thumbnail Image
    Item
    Development of Grey Machine Learning Models for Forecasting of Energy Consumption, Carbon Emission and Energy Generation for the Sustainable Development of Society
    (2023-03) Saxena, A; Zeineldin, RA
    Energy is an important denominator for evaluating the development of any country. Energy consumption, energy production and steps towards obtaining green energy are important factors for sustainable development. With the advent of forecasting technologies, these factors can be accessed earlier, and the planning path for sustainable development can be chalked out. Forecasting technologies pertaining to grey systems are in the spotlight due to the fact that they do not require many data points. In this work, an optimized model with grey machine learning architecture of a polynomial realization was employed to predict power generation, power consumption and CO2 emissions. A nonlinear kernel was taken and optimized with a recently published algorithm, the augmented crow search algorithm (ACSA), for prediction. It was found that as compared to conventional grey models, the proposed framework yields better results in terms of accuracy.
  • Thumbnail Image
    Item
    Development of Grey Machine Learning Models for Forecasting of Energy Consumption, Carbon Emission and Energy Generation for the Sustainable Development of Society
    (2023-03) Saxena, A; Zeineldin, R
    Energy is an important denominator for evaluating the development of any country. Energy consumption, energy production and steps towards obtaining green energy are important factors for sustainable development. With the advent of forecasting technologies, these factors can be accessed earlier, and the planning path for sustainable development can be chalked out. Forecasting technologies pertaining to grey systems are in the spotlight due to the fact that they do not require many data points. In this work, an optimized model with grey machine learning architecture of a polynomial realization was employed to predict power generation, power consumption and CO2 emissions. A nonlinear kernel was taken and optimized with a recently published algorithm, the augmented crow search algorithm (ACSA), for prediction. It was found that as compared to conventional grey models, the proposed framework yields better results in terms of accuracy.
  • Thumbnail Image
    Item
    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.
  • No Thumbnail Available
    Item
    Microbial World: Recent Developments in Health, Agriculture and Environmental Sciences
    (2021-03) Dhingra, G; Saxena, A; Nigam, A; Hira, P; Singhvi, N; Anand, S; Kaur, J; Kaur, J; Dua, A; Negi, N; Gupta, V; Sood, U; Kumar, R; Lal, S; Verma, H; Verma, M; Singh, P; Rawat, C; Tripathi, C; Talwar, C; Nagar, S; Mahato, N; Om Prakash; Singh, M; Kuhad, R.C.
    An Annual Conference Organized by Association of Microbiologists of India and Indian Network for Soil Contamination Research.
  • Thumbnail Image
    Item
    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.
  • Thumbnail Image
    Item
    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.