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The proposed method, solved by a multi-objective extension of the particle swarm optimization method, is based on strain measurements under controlled loading.
Compared with the experimental data, the maximum relative error of the model simulation is 9.38% while the corresponding results of Euler-Eulerian method solved by FLUENT is 33.46%, which identifies that the model is better coincident with the field production.
MCR-ALS is a bilinear factor decomposition method solved by means of ALS optimization [19, 20].
MCR-ALS is a bilinear factor decomposition method solved by means of alternating least squares optimization [22].
To evaluate the performance of the proposed algorithms, the results of produced small-size test problems with the results of ε-constraint method solved by the GAMS software.
To demonstrate the efficiency of the proposed algorithm, the performance of AMOSA is compared with an exact algorithm (i.e., ∈-constraint method) solved by the GAMS software and a well-known evolutionary algorithm, namely NSGA-II for some randomly generated problems based on some comparison metrics.
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Dynamic equations of this model have been derived by the Lagrange method and solved by the Galerkin method using five modes.
For the numerical treatment of (7), systems of non-linear equations are set up via a Runge-Kutta collocation method and solved by a Newton method.
For the boundary value problem of the stationary Cosserat rod (5), systems of nonlinear equations are set up via a Runge-Kutta collocation method and solved by a Newton method in MATLAB 7.4.
The time integration of the RBC schemes uses a fully implicit method of 2nd-order accuracy (Gear method) usually solved by a dual-time approach.
The results of the homotopy analysis method are compared with those of the exact solution and Adomian's decomposition method (ADM) solved by Cihat Arslanturk.
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