A novel computing stochastic algorithm to solve the nonlinear singular periodic boundary value problems

• Published in: International journal of computer mathematics Vol. 99; no. 10; pp. 2091 - 2104
• Main Authors: Sabir, Zulqurnain, Baleanu, Dumitru, Ali, Mohamed R., Sadat, R.
• Format: Journal Article
• Language: English
• Published: Abingdon Taylor & Francis 03.10.2022; Taylor & Francis Ltd
• Subjects: Boundary value problems; genetic algorithm; Genetic algorithms; Gudermannian neural networks; Neural networks; Nonlinear systems; Nuclear physics; Periodic singular systems; Quadratic programming; Robustness (mathematics); sequential quadratic programming; Soft computing
• ISSN: 0020-7160; 1029-0265
• DOI: 10.1080/00207160.2022.2037132

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.