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In this study, we propose a modified particle swarm optimization algorithm to determine the thickness of the liquid layer outside a well casing pipe and discuss the performance of the algorithm after testing it in several numerical and physical models.
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This general generation algorithm was structured in order to maximize the performance of the algorithm.
The performance of the algorithm varied according to the pathway from 6%to15%5%.
After applying the proposed scheme to the constrained min max optimization algorithm, the performance of the algorithm could be greatly improved.
The performance of the algorithm was tested on two different datasets totaling to 12,000 images.
This measures the performance of the algorithm.
The time performance of the algorithm is insensitive to the number of states used.
The performance of the algorithm depends on three factors.
Performance of the algorithm is assessed using accuracy-retrieval curves.
Hence, lower SDB score signifies better performance of the algorithm.
First, performance of the algorithm may be database dependent.
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