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The goal of our work is to bridge the gap by progressive optimization after communication measure.
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We propose to use progressive optimization for UWB indoor localization by ELPSO.
We make the following three contributions in this work: We propose to use progressive optimization for UWB indoor localization by ELPSO.
The design problem is solved using what we term progressive optimization, whereby a sequence of a partially converged flow solution, followed by a partially converged adjoint solution followed by an optimization step is performed.
In this paper we focus on graded interface abruptness in the progressive optimization.
In this study three optimization algorithms including progressive optimization algorithm (POA), particle swarm optimization (PSO) and genetic algorithm (GA) are applied.
According to the discussion above, we can get the progressive optimization of relay selection, subcarrier and power allocation mechanism as Figure 2. Figure 2 Demonstration of the progressive optimization on relay selection, subcarrier and power allocation.
As a progressive optimization, the semi-distributed scheme combines the merits of centralized and distributed framework in a low complexity.
This algorithm can be effectively used with a progressive optimization strategy, which is very beneficial for ranking a large number of chemical structures by their similarities to query structures.
A proper order of the search space also benefits progressive optimization.
This can improve the effectiveness of the branch and bound strategy, and it also helps when progressive optimization steps are used.
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