Accelerate Incremental TSP Algorithms on Time Evolving Graphs with Partitioning Methods
Round 1
Reviewer 1 Report
Please see the attachment.
Comments for author File: 
Comments.pdf
Reviewer 2 Report
In this work, Travelling Salesman Problem (TSP) have been mapped to three partitioning methods: vertex size attribute, edge attribute and k-means as well as compared TSP tour results. The effect of increasing number of partitions on the total computation time is studied. The authors demonstrate that vertex size attribute performs the best because of balanced number of vertices in each partition.
In conclusion: Some results obtained in this work are novel. Moreover, they are incremental improvements of earlier results. The level of difficulty and originality of these results makes them suitable for publication. The experts in this field will appreciate some technical progress exposed in this work. Furthermore, the work falls in journal Scope. However, some minor comments
- The abstract must be concise and includes only new results, I mean that the first 10 lines must be removed, may be can be written in the introduction section
- The start of introduction is not appropriate, I think that it must be improved
- The translation of figures should be included properly
- A conclusion section should be extended to include more details
In general, the authors have to describe in more detail the purpose of their study and its original contents. A clear explanation, what is the new result in their work, and how it is build up upon previous work in the field.
If the authors submit a modified version according to my suggestions where they also give more details/explanations about the abovementioned criticisms, I could recommend the paper for publication in Algorithms.
Author Response
Thanks for you valuable comments. Our responses are in the attached file.
Author Response File: Author Response.pdf
Reviewer 3 Report
The paper entitled 'Accelerate Incremental TSP Algorithms on Time Evolving Graphs with Partitioning Methods' is an interesting manuscript that shows the continuation of work on incremental TSP algorithms. The authors mapped the TSP problem into three partitioning methods. I find the literature review comprehensive, the work structure good, and the experiments persuasive. However, I have a few comments:
1. Manuscript is well-written, but I think that minor spell check is required.
a) There is a lack of space in some places (like in abstract 'are called time evolving graphs(TEG)' should be replaced by 'are called time evolving graphs (TEG)').
b) check the sentence on page 2 lines 55-56 ('The partitioning methods which are compared are as follows: We have also examined the effect of increasing number of partitions on the total computation time.').
c) Particle swam optimization should be changed by Particle Swarm Optimization.
d) You should standardize your approach to writing the name of the algorithms. Instead of 'Ant colony optimization' you should write 'Ant Colony Optimization' or 'ant colony optimization'.
e) Each variable should be italicized (e.g. u and v are not italicized on lines 211-212).
f) Add dot at the end of the line 351.
g) Table 3 and line 292 -> change 'genetic' to the 'Genetic Algorithm'.
2. Please change 'heuristic' to 'metaheuristic' on line 86.
3. Line 213 -> instead of 'Ig-TSP(line 5)' please write 'Ig-TSP(line 6)'.
4. I understand that you are continuing with previous work, but I think you should clearly write that some figures were published in your previous article [1].
[1] Shalini Sharma, Jerry Chou: Distributed and incremental travelling salesman algorithm on time‑evolving graphs. The Journal of Supercomputing https://doi.org/10.1007/s11227-021-03716-5
Author Response
Thanks for you valuable comments. Our responses are in the attached file.
Author Response File: Author Response.pdf
Reviewer 4 Report
In this paper, the authors investigated distinctive partitioning strategies in order to solve incrementally the TSP on time evolving graphs.
The paper is interesting, the novel solution approach is clearly stated, however in order to accept the paper for publication the following observations should be addressed:
- Nowadays there is an increasing interest for investigating the following extensions of the TSP: the clustered TSP, the generalized TSP because these problems are more complex. I recommend in the introduction to mention these problems and as a future research direction to apply your results on these generalizations in the case of on time evolving graphs. You should introduce to the bibliography the following references:
O. Cosma, P.C. Pop and L. Cosma, An effective hybrid genetic algorithm for solving the generalized traveling salesman problem, Lecture Notes in Computer Science, Vol. 12886, pp. 113-123, 2021.
P.C. Pop, O. Matei and C. Sabo, A New Approach for Solving the Generalized Traveling Salesman Problem, in Proc. of HM 2010, Editors M.J. Blesa et al., Lecture Notes in Computer Science, Springer, Vol. 6373, pp. 62-72, 2010.
Chisman, J.A., 1975. The clustered traveling salesman problem, Computers & Operations Research, 2(2), 115-119.
2. The authors should emphasize on the advantages of the new solution approach against the existing methods from the literature. As well we recommend to compare the achieved results with the existing ones from the literature.
3. I also recommend the authors to professionally get the paper proofread, as I have noticed sentences with typos and inappropriate choice of words, partially listed above and below:
- the sentence "Graph is a commonly used data structure to with relationships" does not have sense.
- "Finally the proposed algorithm are summarized in the Table 4." ->Finally the proposed algorithms are summarized ...
- "Each partitioning strategy divides the graph with
different objective." ->... different objectives.
Author Response
Thanks for you valuable comments. Our responses are in the attached file.
Author Response File: Author Response.pdf
