Design of artificial neural networks optimized through genetic algorithms and sequential quadratic programming for tuberculosis model

Shoaib, Mohammed; Kainat, Saba; Raja, Muhammad Asif Zahoor; Nisar, Kottakkaran Sooppy

doi:10.1080/17455030.2022.2094028

Cited by 4 publications

(3 citation statements)

References 49 publications

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“…GAs is a well‐known global solver used in the current research for optimization of MHD Prandtl–Eyring fluid flow problem through the most appropriate selection of generation, mutation, crossover, population and elitism. The applications [59, 67–69] of GAs proves its effectiveness.…”

Section: Problem Methodologymentioning

confidence: 99%

“…ANNs-based stochastic numerical techniques are used quite successfully to solve nonlinear systems of ODEs involved in real-world problems to a large extent for rapid convergence in the fields of biomedical, engineering, welding, finance, material science, medicine and geology. Some recent applications of ANNs in the fields of applied science are including the SEIR Ebola model [48], COVID-19 [49,50], squeezing flow paradigm [51], delay model [52], computer virus handling [53], spline model [54], corneal model [55], Lassa fever model [56], Falkner-Skan model [57], VPSA model [58], tuberculosis model [59], hydrogen purification model [60], nanofluids [61], nanoparticles [62], hybrid nanofluids [63], and induction motor [64]. The above-mentioned literature interprets the successful application of ANNs but as per the author's knowledge, ANNs-IMQ algorithm is never applied to scrutinize the MHD Prandtl-Eyring fluid flow model with radiation and convecting heating impacts over a stretched sheet.…”

Section: Introductionmentioning

confidence: 99%

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Integrated intelligence of inverse multiquadric radial base neuro‐evolution for radiative MHD Prandtl–Eyring fluid flow model with convective heating

Butt,

Ahmad,

Shoaib

et al. 2024

Z Angew Math Mech

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In this communication, a new stochastic numerical paradigm is introduced for thorough scrutinization of the magnetohydrodynamic (MHD) boundary layer flow of Prandtl–Eyring fluidic model along a stretched sheet impact on thermal radiation as well as convective heating scenarios by exploiting the accurate approximation knacks of ANNs (artificial‐neural‐networks) modeling by using the inverse multiquadric (IMQ) function optimized/trained with well‐reputed global search with genetic algorithms (GAs) and local search efficacy of sequential quadratic programming (SQP), that is, ANNs‐IMQ‐GA‐SQP. The mathematical model of the Prandtl–Eyring fluid flow is portrayed in the form of PDEs and then converted in the form of a nonlinear system of ODEs by utilizing suitable similarity transformation to calculate/analyze the solution dynamics. The kinematic and thermodynamic properties of MHD Prandtl–Eyring fluid flow are examined/observed by varying the sundry physical parameters. The obtained intelligent computing designed solver‐based numerical results are consistently found in good agreement with reference results obtained through the Adams numerical technique. First and second‐order cumulant‐based statistical assessments are extensively exploited to endorse trustily the efficacy of ANNs‐IMQ‐GA‐SQP solver based on exhaustive simulations for the Prandtl–Eyring fluidic system.

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Section: Problem Methodologymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Integrated intelligence of inverse multiquadric radial base neuro‐evolution for radiative MHD Prandtl–Eyring fluid flow model with convective heating

Butt,

Ahmad,

Shoaib

et al. 2024

Z Angew Math Mech

Self Cite

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“…Multi-objective GA and an artificial neural network model produce global objective function results. Process variable optimization improves quality and reduces machining time and expense [8].…”

Section: Introductionmentioning

confidence: 99%

Genetic Algorithms for Quadratic Equations

Abbas

2023

JECNAM

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A common technique for finding accurate solutions to quadratic equations is to employ genetic algorithms. The authors propose using a genetic algorithm to find the complex roots of a quadratic problem. The technique begins by generating a collection of viable solutions, then proceeds to assess the suitability of each solution, choose parents for the next generation, and apply crossover and mutation to the offspring. For a predetermined number of generations, the process is repeated. Comparing the evolutionary algorithm's output to the quadratic formula proves its validity and uniqueness. Furthermore, the utility of the evolutionary algorithm has been demonstrated by programming it in Python code and comparing the outcomes to conventional intuitions.

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MHD-hybrid nanofluidic model on rotating plate with impact of thermal slip and heat absorption: a solution predictive neuro-computing approach

Ilyas

Ahmad

Raja

et al. 2022

Waves in Random and Complex Media

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Design of artificial neural networks optimized through genetic algorithms and sequential quadratic programming for tuberculosis model

Cited by 4 publications

References 49 publications

Integrated intelligence of inverse multiquadric radial base neuro‐evolution for radiative MHD Prandtl–Eyring fluid flow model with convective heating

Integrated intelligence of inverse multiquadric radial base neuro‐evolution for radiative MHD Prandtl–Eyring fluid flow model with convective heating

Genetic Algorithms for Quadratic Equations

MHD-hybrid nanofluidic model on rotating plate with impact of thermal slip and heat absorption: a solution predictive neuro-computing approach

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