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The experimental results prove good performance of hybrid algorithm in terms of both the solution quality and computation time.
The results show that both the solution quality and the convergence speed of the ATPPSO algorithm precede the other two recently proposed algorithms.
Our extensive computational analysis shows that the proposed variable neighborhood search based heuristics outperforms the best-known algorithms in terms of both the solution quality and computational efforts.
Comparisons show that the proposed TS out-performs GA solutions, in terms of both the solution quality and the execution time.
The results confirmed satisfying performance of the heuristics in terms of both the solution quality and the computational time.
According to the results, the proposed hybrid algorithm has better performance than the SA algorithm from both the solution quality and computation time standpoints.
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The obtained results of the proposed approach are compared with other methods, and simulation and comparison results clearly show that DRQEA is able to provide better solution than other reported methods, both in the solution quality and the convergence speed.
In this section, the performance of the proposed hybrid algorithm is investigated from both of the solution quality and computational time points of view in a qualitative manner.
In other words, it will be explained that how the good initial solutions generated by CS lead to improve both of the solution quality and the computational time of SA algorithm.
The suggested approaches outperformed the previous study in terms of both computational time and the solution quality by reducing the overall system cost.
The computational experiment results reveal that the new method outperforms the state-of-the-art methods, both in terms of the solution quality and computation time, especially for medium and large-scale problems.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com