Sentence examples for expensive optimisation from inspiring English sources

In this study, a computational framework, known as DE-kNN, is presented for solving computationally expensive optimisation problems.

This method has the major advantage of depending on just n decision variables – the number of compared elements – and so is less computationally expensive than other optimisation methods, and can be easily implemented in virtually any existing computer environment.

These difficulties make direct simulation methods such as Monte Carlo, expensive to implement in optimisation applications.

Advanced knowledge from the fields of simulation and optimisation of expensive functions can be very useful when systematically applied to GP.

As kinetic modelling, using non-linear least squares optimisation, is computationally expensive and sensitive to noise, a comparison will be made between kinetic modelling and graphical analysis to quantify [18F]FCho uptake in tissues.

This paper introduces a grid-enabled framework for the multi-objective optimisation of computationally expensive problems, before using the framework in the multi-objective evolutionary design of a robust lateral stability controller for a real-world aircraft using H∞ loop shaping.

This paper introduces a grid-enabled framework for the multi-objective optimisation of computationally expensive problems which will then be demonstrated using and example of the multi-objective evolutionary design of a robust lateral stability controller for a real-world aircraft using H∞ loop shaping.

Here, computationally the most expensive part is the full geometry optimisation using the PBE0 functional.

The motivation behind the improvement is the fact that when these algorithms are based on a computationally expensive set of model equations, real time optimisation is seldom possible.

This optimisation technique can replace the expensive and traditional trial and error procedures during the design and prototyping phase, and it significantly decreases the time to build the final component.

The optimisation itself is computationally non-expensive, but the optimisation might become time consuming if the dynamic model is large, since the optimisation has to be run many times with different starting values to provide reliable results.