Design of a Computational Heuristic to Solve the Nonlinear Liénard Differential Model

In this study, the design of a computational heuristic based on the nonlinear Liénard model is presented using the efficiency of artificial neural networks (ANNs) along with the hybridization procedures of global and local search approaches. The global search genetic algorithm (GA) and local search...

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Veröffentlicht in:Computer modeling in engineering & sciences Jg. 136; H. 1; S. 201
Hauptverfasser: Li, Yan, Sabir, Zulqurnain, Ilhan, Esin, Asif, Muhammad, Gao, Wei, Baskonus, Haci
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Henderson Tech Science Press 01.01.2023
Schlagworte:
Artificial neural networks
Boundary conditions
Design
Genetic algorithms
Heuristic
Quadratic programming
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ISSN:1526-1492, 1526-1506
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Zusammenfassung:In this study, the design of a computational heuristic based on the nonlinear Liénard model is presented using the efficiency of artificial neural networks (ANNs) along with the hybridization procedures of global and local search approaches. The global search genetic algorithm (GA) and local search sequential quadratic programming scheme (SQPS) are implemented to solve the nonlinear Liénard model. An objective function using the differential model and boundary conditions is designed and optimized by the hybrid computing strength of the GA-SQPS. The motivation of the ANN procedures along with GA-SQPS comes to present reliable, feasible and precise frameworks to tackle stiff and highly nonlinear differential models. The designed procedures of ANNs along with GA-SQPS are applied for three highly nonlinear differential models. The achieved numerical outcomes on multiple trials using the designed procedures are compared to authenticate the correctness, viability and efficacy. Moreover, statistical performances based on different measures are also provided to check the reliability of the ANN along with GA-SQPS.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:1526-1492
1526-1506
DOI:10.32604/cmes.2023.025094