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  • Open Access


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

    Li Yan1, Zulqurnain Sabir2, Esin Ilhan3, Muhammad Asif Zahoor Raja4, Wei Gao5, Haci Mehmet Baskonus6,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.1, pp. 201-221, 2023, DOI:10.32604/cmes.2023.025094

    Abstract 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 More >

  • Open Access


    Swarming Computational Approach for the Heartbeat Van Der Pol Nonlinear System

    Muhammad Umar1, Fazli Amin1, Soheil Salahshour2, Thongchai Botmart3, Wajaree Weera3, Prem Junswang4,*, Zulqurnain Sabir1

    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 6185-6202, 2022, DOI:10.32604/cmc.2022.027970

    Abstract The present study is related to design a stochastic framework for the numerical treatment of the Van der Pol heartbeat model (VP-HBM) using the feedforward artificial neural networks (ANNs) under the optimization of particle swarm optimization (PSO) hybridized with the active-set algorithm (ASA), i.e., ANNs-PSO-ASA. The global search PSO scheme and local refinement of ASA are used as an optimization procedure in this study. An error-based merit function is defined using the differential VP-HBM form as well as the initial conditions. The optimization of the merit function is accomplished using the hybrid computing performances of More >

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