A novel computing stochastic algorithm to solve the nonlinear singular periodic boundary value problems
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Date
2022
Authors
Sabir, Zulqurnain
Baleanu, Dumitru
Ali, Mohamed R.
Sadat, R.
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Abstract
In this work, a class of singular periodic nonlinear differential systems (SP-NDS) in nuclear physics is numerically treated by using a novel computing approach based on the Gudermannian neural networks (GNNs) optimized by the mutual strength of global and local search abilities of genetic algorithms (GA) and sequential quadratic programming (SQP), i.e. GNNs-GA-SQP. The stimulation of offering this numerical computing work comes from the aim of introducing a consistent framework that has an effective structure of GNNs optimized with the backgrounds of soft computing to tackle such thought-provoking systems. Two different problems based on the SPNDS in nuclear physics will be examined to check the proficiency, robustness and constancy of the GNNs-GA-SQP. The outcomes obtained through GNNs-GA-SQP are compared with the true results to find the worth of designed procedures based on the multiple trials.
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Genetic Algorithm, Gudermannian Neural Networks, Nuclear Physics, Periodic Singular Systems, Sequential Quadratic Programming
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Citation
Sabir, Zulqurnain;...et.al. (2022). "A novel computing stochastic algorithm to solve the nonlinear singular periodic boundary value problems", International Journal of Computer Mathematics, Vol.99, No.10, pp.2091-2104.
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Source
International Journal of Computer Mathematics
Volume
99
Issue
10
Start Page
2091
End Page
2104