Application of artificial intelligence algorithm in mathematical modelling and solving

Yao, Haiyan

doi:10.2478/amns.2021.2.00081

Cited by 4 publications

(2 citation statements)

References 11 publications

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“…In the future, one can exploit/explore the knacks of ANNs-PSO-IPM to solve the singular higher order models (Sabir et al 2020c, d;2021a), fractional order models (I ˙lhan and Kıymaz 2020; Sabir et al 2022b;Sulaiman et al 2019;Touchent et al 2020;Yokus ¸and Gu ¨lbahar 2019;Ziane et al 2019) and many other applications of utmost importance (Kouider and Polat 2020;Xie et al 2020;Xue et al 2021;Yao 2021).…”

Section: Discussionmentioning

confidence: 99%

Neuro-swarm computational heuristic for solving a nonlinear second-order coupled Emden–Fowler model

Sabir

Raja²,

Băleanu

et al. 2022

Soft Comput

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The aim of the current study is to present the numerical solutions of a nonlinear second-order coupled Emden–Fowler equation by developing a neuro-swarming-based computing intelligent solver. The feedforward artificial neural networks (ANNs) are used for modelling, and optimization is carried out by the local/global search competences of particle swarm optimization (PSO) aided with capability of interior-point method (IPM), i.e., ANNs-PSO-IPM. In ANNs-PSO-IPM, a mean square error-based objective function is designed for nonlinear second-order coupled Emden–Fowler (EF) equations and then optimized using the combination of PSO-IPM. The inspiration to present the ANNs-PSO-IPM comes with a motive to depict a viable, detailed and consistent framework to tackle with such stiff/nonlinear second-order coupled EF system. The ANNs-PSO-IP scheme is verified for different examples of the second-order nonlinear-coupled EF equations. The achieved numerical outcomes for single as well as multiple trials of ANNs-PSO-IPM are incorporated to validate the reliability, viability and accuracy.

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Section: Discussionmentioning

confidence: 99%

Neuro-swarm computational heuristic for solving a nonlinear second-order coupled Emden–Fowler model

Sabir

Raja²,

Băleanu

et al. 2022

Soft Comput

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“…An intelligent algorithm is an abstract summary of some structures and laws in nature [17]. The genetic algorithm is an adaptive global intelligent algorithm formed by biological heredity and evolution processes [18].…”

Section: Genetic Algorithmmentioning

confidence: 99%

A Study on the Vehicle Routing Problem Considering Infeasible Routing Based on the Improved Genetic Algorithm

Xiao-Yun Jiang,

Wen-Chao Chen,

Yu-Tong Liu

2023

Int. j. eng. technol. innov.

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The study aims to optimize the vehicle routing problem, considering infeasible routing, to minimize losses for the company. Firstly, a vehicle routing model with hard time windows and infeasible route constraints is established, considering both the minimization of total vehicle travel distance and the maximization of customer satisfaction. Subsequently, a Floyd-based improved genetic algorithm that incorporates local search is designed. Finally, the computational experiment demonstrates that compared with the classic genetic algorithm, the improved genetic algorithm reduced the average travel distance by 20.6% when focusing on travel distance and 18.4% when prioritizing customer satisfaction. In both scenarios, there was also a reduction of one in the average number of vehicles used. The proposed method effectively addresses the model introduced in this study, resulting in a reduction in total distance and an enhancement of customer satisfaction.

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Fractional order Lorenz based physics informed SARFIMA-NARX model to monitor and mitigate megacities air pollution

Bukhari

Raja

Shoaib

et al. 2022

Chaos, Solitons & Fractals

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Application of artificial intelligence algorithm in mathematical modelling and solving

Cited by 4 publications

References 11 publications

Neuro-swarm computational heuristic for solving a nonlinear second-order coupled Emden–Fowler model

Neuro-swarm computational heuristic for solving a nonlinear second-order coupled Emden–Fowler model

A Study on the Vehicle Routing Problem Considering Infeasible Routing Based on the Improved Genetic Algorithm

Fractional order Lorenz based physics informed SARFIMA-NARX model to monitor and mitigate megacities air pollution

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