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It means that the memory is hardly a limitation for MKL with the large scale problem.
The large scale problem exhibits qualitatively similar behavior to the laboratory scale problem with some differences: slightly larger intensification of the boundary flow and somewhat higher non-dimensional values for the energy fluxed away by the internal wave field.
The seven algorithms are again employed to solve the large scale problem for comparative study where the specific parameter settings for these algorithms are the same with those in case 2. The results provided in Table 12 illustrate that the SL-TLBO method again achieved the optimal solutions, outperforming with all the other six algorithms in all the four load scenarios.
New models that can appropriately balance the biological and structural similarities and algorithms that can efficiently and effectively solve the large scale problem are extremely demanded in the fields of systems biology.
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Besides, according the nature of the problem, this study proposes a simple nested approach named as tri-level metaheuristic for the first time in order to solve the large scale problems.
To solve the large scale problems in a reasonable time, we also develop a two-stage algorithm, which decomposes the problem into planning stages such as aircraft-path assignment and robust schedule generation, and then solves them sequentially.
Moreover, the results from the genetic algorithm are good enough (up to 82 %) that can be later used as a baseline for the larger scale problem.
Many algorithms have been presented for solving the large-scale problem (1).
A gradient-based algorithm was employed in the DMO approach to tackle the large-scale problem.
Second, some solution algorithms can be applied to gain the optimal value of the model in the large-scale problem.
The resulting algorithm improves the results obtained for the large-scale problem described in the paper by other algorithms.
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