Neuro-Swarm heuristic using interior-point algorithm to solve a third kind of multi-singular nonlinear system

The purpose of the present work is to solve a third kind of multi-singular nonlinear system using the neuro-swarm computing solver based on the artificial neural networks (ANNs) optimized with the effectiveness of particle swarm optimization (PSO) maintained by a local search proficiency of interior...

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Autores principales: Zulqurnain Sabir, Muhammad Asif Zahoor Raja, Aldawoud Kamal, Juan L.G. Guirao, Dac-Nhuong Le, Tareq Saeed, Mohamad Salama
Formato: article
Lenguaje:EN
Publicado: AIMS Press 2021
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Acceso en línea:https://doaj.org/article/1e1f9f37eea14b2d93216283adc5ac02
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Sumario:The purpose of the present work is to solve a third kind of multi-singular nonlinear system using the neuro-swarm computing solver based on the artificial neural networks (ANNs) optimized with the effectiveness of particle swarm optimization (PSO) maintained by a local search proficiency of interior-point algorithm (IPA), i.e., ANN-PSO-IPA. An objective function is designed using the continuous mapping of ANN for nonlinear multi-singular third order system of Emden-Fowler equations and optimization of fitness function carried out with the integrated strength of PSO-IPA. The motivation to design the ANN-PSO-IPA is to present a feasible, reliable and feasible framework to handle with such complicated nonlinear multi-singular third order system of Emden-Fowler model. The designed ANN-PSO-IPA is tested for three different nonlinear variants of the multi-singular third kind of Emden-Fowler system. The obtained numerical results on single/multiple executions of the designed ANN-PSO-IPA are used to endorse the precision, viability and reliability.