Review Reports - A Hybrid Preference Interaction Mechanism for Multi-Satellite Imaging Dynamic Mission Planning

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

The authors study a dynamic task planning problem of multiple satellites based on user preference. They explore and enhance the modeling and planning strategies considering numerous dynamic factors and decision-makers' preferences. Furthermore, they propose a hybrid preference interaction mechanism and a knowledge transfer strategy for the multi-objective evolutionary algorithm (HPIM-KTSMOEA). They demonstrate that the algorithm is not only effective and stable but also provides remarkable task benefits, quick response time, and a high task completion rate in handling MSDMPUP.

Generally, this manuscript is well organized and well-written. The HPIM-KTSMOEA demonstrates outstanding performance when dealing with dynamic changes. I appreciate the contribution of the authors to proposing and evaluating a hybrid preference interaction mechanism and knowledge transfer strategy for the multi-objective evolutionary algorithm, which can effectively address the MSDMPUP problem.

In my opinion, this article presents original research and introduces a novel idea. With minor revisions, it could be accepted for publication. Here are some comments:

1) While this article does take into account the preferences of decision-makers, the explanation of the hybrid preference interaction mechanism is not sufficiently clear. There is also room for improvement in terms of how the system acquires and interprets user preferences.

2) In the conclusion section, it is advisable to conduct a more in-depth discussion on the limitations of the research and on the directions for future improvements.

3) The paper could benefit from formal editing. There are also some minor comments. Here are some examples:

-Page 6, Equation (2), why does the last column index of the matrix use 3 instead of 6?

-Page 7, lines 6, The sequence number of the objective functions here starts from 7. It seems that the numerical order of these objective functions are continued from previous challenges and contributions, including the subsequent numbers, they all sequentially follow the previous sections.

-Page 17, line 7-8, “the multi-satellite imaging mission planning model based on cycle-triggered rolling (MSIMP-ET) [32], and the multi-satellite imaging mission planning model based on cycle-triggered rolling (MSIMP-ET) [33]”, this is repetitive.

-Page 22, the significance of the size comparisons of evaluation metrics in the various instances presented under Figure 9 is not clear.

Comments on the Quality of English Language

The English written could be improved, and the formats and details of the references should be checked.

Author Response

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Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

In this paper proposed the hybrid preference interaction mechanism and knowledge transfer strategy for the multi-objective evolutionary algorithm(MSDMPUP). A knowledge transfer strategy for the multi-objective evolutionary algorithm is proposed to accelerate population convergence in new environments based on knowledge transfer according to environmental variability. Simulation experiments verify the effectiveness and stability of the method in processing MSDMPUP. However, the following problems need to be solved：

1.Why is the serial number in the whole article sorted from 1 to 20.

2.There are some mistakes in writing. Such as “time Time T₂” etc.

3.The picture name has some mistakes, such as Figure12(a) and (b).

Comments on the Quality of English Language

Minor editing of English language required.

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Reviewer 3 Report

Comments and Suggestions for Authors

This paper studies the multi-satellite imaging dynamic mission planning problem and develops the HPIM-KTSMOEA to solve the problem. The simulation result looks reasonable, but the mathematical model of MSDMPUP needs to be modified. The comments are as follows:

1. the title of Section 2 should be "Related work" not "Related word".

2. The numbering in the main text needs to be corrected. For example, the contribution summary on page 3, the objectives on page 7, and the constraints on page 8.

3. Figure 1 (a) is confusing to understand. The author should try to make it easier to understand. For example, the author can indicate the corresponding satellites for different task periods. In addition, the order of T5 and T8 is not in line with that in Figure 1 (b) and the text description.

4. The meaning of the event trigger function in Eq. (5) is unclear; the author should provide a clear explanation.

5. The value range and meaning of the decision variable x_{i,j,o} should be given

6. The meaning of the objective function is not clear. The author should indicate whether each objective is maximized or minimized and give a detailed explanation of the meaning of Eq. (6)-(8).

7. The meaning of SA_j in Eq(9) is wrong. It should be the probability that satellite j has at least successfully observed one target.

8. Eq. (12) needs clarification. What is the meaning of "||"? w_{ijk} has not appeared in the Equation.

Comments on the Quality of English Language

N/A

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Reviewer 4 Report

Comments and Suggestions for Authors

The study proposes a hybrid preference interaction mechanism and knowledge transfer strategy for the multi-objective evolutionary algorithm for multi-satellite imaging dynamic mission planning. The proposed algorithm can adapt promptly to dynamic environments to ensure convergence and high task benefits. The research objective is of significance, and the method is technically sounded; however, there are places that warrant further improvements, and the authors should address the following comments.

Major comments:

1. The authors should review the learning-based algorithms in complex time allocation, e.g., reinforcement learning.

2. As stated in the related work, predictions can be leveraged to tackle complex and dynamic environments; the authors should clarify why it is not used in the proposed method.

3. The optimization formulation should be organized in a standard format, with objective function, constraints, and decision variables holistically listed. Additionally, the indices of satellites and targets should stay behind the equations.

4. The authors should discuss the complexity and scalability of the proposed algorithm.

5. The authors should also articulate the scalability and feasibility of the proposed HPIM-KTSMOEA algorithm. How are the parameters selected in the simulation?

Minor comments:

1. The numbering of contributions needs correction.

2. The title of section 2 does not look right.

3. The authors should carefully check the writing of the paper. There are repeated words/numbers in the text.

Comments on the Quality of English Language

The wording is decent in general; however, the authors should carefully check the typos in writing.