Target Assignment Algorithm for Joint Air Defense Operation Based on Spatial Crowdsourcing Mode

Round 1

Reviewer 1 Report

Review Report

Journal Name: Electronics (MDPI)

Title: Target assignment algorithm for joint air defense operation based on spatial crowdsourcing mode

Manuscript ID: electronics-1728591

Decision: Minor Revision

At the outset, I appreciate the authors for their contribution towards the spatial crowdsourcing mode. Although the work is novel, some possible corrections make the article more informative and easily understandable to a researcher.

1. There are significant concerns about the grammar, usage, and overall readability of the manuscript. Therefore, request is to revise the text to fix the grammatical errors and improve the overall readability of the text before this work is considered for publication.
2. Would you explicitly specify the novelty of your work? What progress against the most recent state-of-the-art similar studies was made?
3. The literature review section should be improved. It should be dedicated to present critical analysis of state-of-the-art related work to justify the objective of the study. Also, critical comments should be made on the results of the cited works.
4. Add some details about Weapon-target distribution in the novelty paragraph.
5. Give the nomenclature for the betterment of the manuscript and understanding of the reader.
6. The introduction section should be made more concise to show previous work in the field. For instance:

• https://doi.org/10.1002/er.7134.

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Reviewer 2 Report

This paper proposed a modified PSO for the target allocation model of joint air defence operation in spatial crowd-sourcing mode, based on variable weight nonlinear learning factor after tree decomposition algorithm split solution space. The experimental results show a modification in this problem compared with other algorithms; however, the article should be revised as follows:

1. English writing needs consideration and can be improved by a native. 
2. The abstract should be re-written, and principal research gaps and contributions are unclear. 
3. Although the introduction is well-organized, the existing research gaps were not adequately discussed and listed in the introduction section.
4. The introduction section would be great to include this work's main contributions and novelty. Please list them one by one.
5. The formulas need to be numbered in the whole paper.
6. In the Tables, the best-found results can be bold.
7. In the end, adding a conclusion section can be helpful.
8. Testing the performance of the proposed method in more extensive (larger) search space is recommended. 
9. Considering some relevant references for the application of PSO and other meta-heuristics can be beneficial, such as a) FAIPSO: fuzzy adaptive informed particle swarm optimization. Neural Computing and Applications, 23(1), 95-116. b) MPSO: Modified particle swarm optimization and its applications. Swarm and evolutionary computation, 41, 49-68. c) Adaptive particle swarm optimization. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 39(6), 1362-1381.

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Round 2

Reviewer 2 Report

The authors have sufficiently addressed the reviewed issues in the manuscript.