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Reviewers' comments:

Editor:We manage to find just one expert available for reviewing the authors manuscript. In order to speed up the revision process, we decided to take a decision. The authors must follow all the comments of the reviewer and improve the overall quality of the manuscript
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Reviewer #1: The authors propose a new metaheuristic (called Car Tracking Optimization Algorithm) that finds inspiration from the behavior of other algorithms.
The subject addressed  is coherent with the aims of the journal but the way of presenting the work needs several improvements.  
It is in general very hard to follow the work. Therefore, I do not recommend the publication of the work in this form in a high level journal like SOFT COMPUTING.
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In the following, some major/minor comments are given to the authors.

Major comments:

Originality and significance:
The topic of the paper has to be better clarified in the introduction. In this form, it is very hard to follow the concepts, to understand the real contribution etc. Originality and significance have to be highlighted. For example, through a series of bullets, the contributions can be listed and described in the introduction.


Presentation:
1) the paper contains several grammatical errors and it is strongly suggested to proofreader it very carefully.

2)It seems to me that the proposed metaheuristic is very similar to the Ant Colony Optimization. Although the authors mention it, they should spend more time to better clarify the differences and the shared aspects.

3) It is not clear the setting of the input parameters.
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4) from the results shown in the tables, it is not highlighted the behavior, on average,  of each of the algorithms compared.
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For example, it seems to me that the proposed algorithm doesn't perform better than the others for what concerns the CPU time required. Is it right?


Some Minor comments:
Since the performance of these algorithms PRESENTS
Car Tracking optimization Algorithm (CTA)

Rephrase:
We can put an end to the law of these algorithms by studying these optimization algorithms

In these swarm intelligence optimization algorithms, particle swarm algorithm

For the basic parameters of all algorithms, including population size, the maximum number of function evaluations are same.
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lovehyojoo

Òø³æ (СÓÐÃûÆø)

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lovehyojoo

Òø³æ (СÓÐÃûÆø)

ÒýÓûØÌû:
2Â¥: Originally posted by Chem. Albert at 2017-05-12 15:55:43
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