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Other features of this algorithm are discussed.
Features of this algorithm are its simplicity and robustness.
The main features of this algorithm are described and its encouraging results are presented.
Various features of this algorithm have been designed so that it can be mapped easily to field-programmable gate arrays (FPGAs).
One of the attractive features of this algorithm is that a non-increasing sequence of function values are yielded, implying a quick and guaranteed convergence.
The main features of this algorithm are: fast non-dominated sorting approach, fast crowded distance estimation procedure, simple crowded comparison operator and binary tournament selection operator (Tavakkoli-Moghaddam et al. 2012).
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An important feature of this algorithm is a variable reordering scheme that greatly reduces computation time.
Another important feature of this algorithm is that, it can improve the saturation effect suffered by KRR.
A novel feature of this algorithm is accommodation of time-varying, norm-like cost functions, which enables inclusion of constraints such as asymmetric thruster configurations and time-varying attitude modes on spacecraft in the maneuver planning problem.
The underlying feature of this algorithm is the elimination of the use of reflecting boundaries through the use of novel Green's functions that mimic the boundary conditions of the problem of interest.
The key feature of this algorithm is the wide modulation index (MI) range that can be produced (i.e. 5.40 8.15 p.u) with very small step size (0.01), while maintaining the total harmonic distortion (THD) of the MVSI output voltage below 5%.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com