Sentence examples for solutions selection from inspiring English sources

With regards to metering equipment, significant developments have been made in the recent past on miniaturisation, accuracy, robustness, data storage, ability to connect using multiple communication protocols, and the integration with BEMS and the Cloud – resulting in a range of available solutions, selection of which can be challenging.

These actions were: a) solution development by data analysis (i.e., extract the key features of the problem) and solution selection (i.e., select the most appropriate electrophysical agent), and b) solution implementation by applying a solution to a problem (i.e., parameterizing the selected electrophysical agent) [ 97].

Treatment of infeasibility and solution selection are also presented.

Finally, a new measure is designed to evaluate the effectiveness of clustering solution selection strategies.

In this paper, we focus on the clustering solution selection strategy in the cluster ensemble, and propose to view clustering solutions as features such that suitable feature selection techniques can be used to perform clustering solution selection.

Therefore, proper design of neighborhood structures, base solution selection strategy, and perturbation operators are used to help build a well-balanced set of non-dominated solutions.

Most dynamic geometry systems deal with this problem in such a way that the solution selection method leads to a fixed dynamic behavior of the system.

The aim is to add a new dimensions to solution selection, namely the guarantee of QoS from both application quality and RT issue points of view.

Furthermore, a hybrid clustering solution selection strategy (HCSS) is designed based on a proposed weighting function, which combines several feature selection techniques for the refinement of clustering solutions in the ensemble.

Compared with the G-Jaya algorithm, three main procedures of the GPU-Jaya, the solution update, fitness value computation, and the best/worst solution selection are all computed in parallel on GPU via a compute unified device architecture (CUDA).

The self-adaptive differential evolution with neighbourhood search (SaNSDE) is improved by introducing a new mutation strategy and employing the weighted sum of exploitation and exploration indicators for solution selection.