A Novel and Unified Full-Chip CMP Model Aware Dummy Fill Insertion Framework With SQP-Based Optimization Method

Cai, Junzhe; Yan, Changhao; Tao, Yudong; Lin, Yibo; Wang, Sheng-Guo; Pan, David Z.; Zeng, Xuan

doi:10.1109/tcad.2020.3001380

Cited by 4 publications

(1 citation statement)

References 19 publications

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“…Therefore, an efficient SQPS, i.e., a local search technique is implemented in the process of hybridization. SQPS is used recently in unified fullchip CMP model aware dummy fill insertion framework [40], quasilinear parabolic optimal control problems [41], convex/nonconvex economic dispatch models [42], fractional order control design [43] and economic dispatch of micro-grid [44] and some related other applications [45][46][47]. The designed structure using the ANN procedures along with GA-SQPS for the NLM is presented in Fig.…”

Section: Mathematical Ann Modelingmentioning

confidence: 99%

Design of a Computational Heuristic to Solve the Nonlinear Li閚ard Differential Model

Li¹,

Sabir²,

İlhan³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

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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. KEYWORDSNonlinear Liénard model; numerical computing; sequential quadratic programming scheme; genetic algorithm; statistical analysis; artificial neural networks Optimization Procedure: GA-SQPSThe optimization processes to solve the nonlinear Liénard differential model using the designed ANN along with GA-SQPS measures are provided in this section.

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