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The progressive algorithm is proposed to overcome the inefficiency of the heuristic algorithm.
Besides, the progressive algorithm can switch off more PBSs than the heuristic algorithm while slightly affects the network blocking probability, which indicates that the progressive algorithm has a better potential to save energy.
The Progressive algorithm used the idea of this paper [16] of omnidirectional networks for the problem of MDCS.
The simulations demonstrate that the execution time of the progressive algorithm is at most one third of that of the heuristic algorithm, which enables the network to respond to the traffic variation more promptly.
To achieve this target, we propose two centralized algorithms (i.e., the heuristic algorithm and the progressive algorithm) in this paper to dynamically switch off the unnecessary PBSs, both of which can track the traffic variation well.
Genomes were aligned with the open-source MAUVE aligner version 2.3.1 using the progressive algorithm.
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In progressive algorithms, sequences are first aligned two-by-two (using an appropriate pair-wise algorithm) before finding the final alignment.
In this study three optimization algorithms including progressive optimization algorithm (POA), particle swarm optimization (PSO) and genetic algorithm (GA) are applied.
Experiments on the BAliBASE dataset [ 12] show that our approach is competitive with the best progressive methods and significantly outperforms most non-progressive algorithms.
Three alignment algorithms were used: T-COFFEE, which combines information for both global and local homologies [ 34], CLUSTALW, a progressive alignment algorithm [ 35], and DIALIGN [ 36], a local segment alignment algorithm.
We therefore focused on progressive MSA algorithms, as these algorithms generate alignments rapidly.
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CEO of Professional Science Editing for Scientists @ prosciediting.com