Formation Control with Connectivity Assurance for Missile Swarms by a Natural Co-Evolutionary Strategy

Round 1

Reviewer 1 Report

This paper investigates on proposing a  metaheuristic approach that leverages a natural co-evolutionary strategy to solve the formation control problem for a swarm of missiles. Also, a model-based policy constraint and a population adaptation strategy has been presented to alleviate the performance degradation. The paper is well-organized and the idea is interesting. However, some modifications are required before publication. The abstract should be rewritten to better present the research gap and the achieved results of this study. Contributions of the paper are not clear, please provide the difference between this work and existing studies clearly. A section should be added to address the comparative analysis. Figure 2 should be elaborated more and please addressed the reference of the figure that is not from the authors' side. Please update the references to address more works published in recent years. Add more references in data analyticts applications and data modeling for training. For instance, the study in dynamic characteristics preserving data compressing algorithm for transactive energy management frameworks can be useful. 

Author Response

We thank the reviewer for the comments provided, and the time and the efforts he/she has spent in the review. We hope that our revisions adequately address your concerns. Attached herewith are the detailed responses to the reviewer.

Author Response File: Author Response.pdf

Reviewer 2 Report

Dear Authors,

the article "Formation control with connectivity assurance for missile swarm by a natural co-evolutionary strategy" is very interesting from the point of view of a person dealing with optimization for the needs of the armed forces.

The article describes the method of controlling rocket trajectories in such a way that they follow in a swarm in a specific formation.

Your work contains editing gaps, which I have indicated below. Some elements are difficult to point precisely due to the fact that the line numbering is discontinuous.

The drawings were placed in the work in a careless manner.

I have a lot of comments on how to define parameters and variables, including the lack of a definition of the sets to which the indexes belong. It seems to me that some indexes are redundant. Please see the comments below.

After making corrections, the article will be interesting for people dealing with optimization.

 

My remarks:

1. "Consider a total of N missiles, for missile i denoted by Mi," - what is M, V, etc. If "i" is the missile index, then "m" stands for what?

2. "xi = [xmi, ymi, αmi]" - the same question about index m

3. "two independent input, avi, ali," - All indexes belong to sets. Please formally define these sets.

4. "λ = {λ_pi = [λ_xi, λ_yi]T : i = 1, 2, ..., N}," - and what is p?

5. "error vector as e_ri" - e is a vector of values for the error of i-th missile, but r stands for what?

6. clip() function - As I understand it, the function ensures that the number of elements in the population is within a given range?

7. equation (30)(31) - It would be very useful for the reader to describe the meaning of parameters or variables under the formulas. This makes reading the work much easier.

8. After equation (31) there is a dot in a new line.

9. Algorithm 1:

"Input: agent number N ∈ N+," so N is a number. So i ∈ {1, ..., N} is not possible. This is not clear for a reader.

 

Author Response

We thank the reviewer for the comments provided, and the time and the efforts he/she has spent in the review. We hope that our revisions adequately address your concerns. Attached herewith are the detailed responses to the reviewer.

Author Response File: Author Response.pdf