Advanced Path Planning for UAV Swarms in Smart City Disaster Scenarios Using Hybrid Metaheuristic Algorithms

Sr.

Reviewer 1’s Comments

Our Response

Changes in the Manuscript

1.      

In Abstract, authors indicate that the study aims at optimizing the coverage, QoS compliance, and energy consumption. Quantitative information about the energy consumption should be provided in the abstract.

We sincerely thank the reviewer for highlighting the importance of including quantitative information about energy consumption. However, we would like to clarify that while energy efficiency is a critical aspect of UAV swarm operations, it is not within the immediate scope of this study. Our primary focus is on optimizing communication coverage and QoS compliance in disaster response scenarios.

We have revised the Abstract to reflect the scope of the current study more accurately.

2.      

“Seamless user allocation” is one key challenge of the proposed approach (lines 65–67, page 2). What does the “seamless” stand for, and how is it achieved?

We thank the reviewer for raising this important question. The term "seamless" in this context refers to the uninterrupted allocation and reallocation of users to UAV swarms as users move across service areas in a disaster scenario. This is achieved through the dynamic adaptability of the proposed APC-GA hybrid algorithm, which integrates user mobility patterns into the clustering and optimization process. By leveraging the real-time mobility models described in Section 3, such as the RWPM and RPGM, the framework ensures that UAV swarms can adjust their coverage areas and paths dynamically to maintain consistent connectivity.

Details for achieving this seamless allocation are elaborated in Section 4 from lines 898 to 903

3.      

In Sections 4.1 and 4.2, two algorithms with different fitness functions are given, however, the notations of these two functions (given in lines 700 and 750, respectively) are the same. In Section 1.2, a QoS-aware fitness function is indicated as one of the key contributions of this paper (line 76, page 3). Which of the two functions mentioned above does it correspond to?

We appreciate the reviewer’s observation regarding the two notations and their potential for confusion. We have revived it and removed the duplicate function. It corresponds to equation (33)

Revised in Section 4.2, line 796 is coloured red.

4.      

Figure 2 gives an environmental example, and the red circular dash line indicates a UAV swarm path. This illustration makes it easy to think that all the UAV swarms follow the same path. It is suggested to improve Figure 2 so as to better show the innovations of this paper. For example, the areas with different environmental characteristics are indicated.

We thank the reviewer for the insightful comment regarding Figure 2. To address the concern and enhance clarity, we have revised the figure in section 3.

The update figure can be found on page 10.

5.      

The simulation parameters and their values are given in Section 3 (page 11) describing the environment modelling. In my opinion, the modelling should be somewhat common, and the specific simulation parameter settings will be more appropriate in the experimental part, i.e. Section 5.

We thank the reviewer for the insightful comment regarding simulation table 2. To address the concern and enhance clarity, we have revised the manuscript by moving the detailed discussion of environmental parameters and their impact on UAV swarm path optimization to Section 5.

Revised in Table 2 in Section 3 page 20 and Section 5-page 30 coloured red.

6.      

 All the findings of the current work need to be compared and discussed with the results of other researchers finding instead of having a general comparison with other researchers' works. The authors should perform a comparison between the forecasting results. Currently, there are many studies using improved GA/PSO/ACO for UAV path planning (including the research work referenced in this paper), some of which also consider the users’ dynamic movement, UAV energy consumption, communication requirements, and other factors. This paper does not compare with these works, but only compares the performance with the GA, PSO, and ACO benchmarks, which makes it difficult to demonstrate the validity and effectiveness of the work proposed in this paper.

Thank you for your insightful comment. The justification for using GA, PSO, and ACO as benchmarks is provided in Section 2, from lines 315 to 339. This section explains that these metaheuristic algorithms are widely recognized and serve as standard benchmarks in UAV path planning studies. They were selected to ensure a fair and consistent comparison under identical simulation settings, including user mobility models, communication constraints, and base station loss scenarios.

This approach enables a robust evaluation of the proposed APC-GA framework, highlighting its unique contributions and effectiveness compared to established methods.

The justification for using GA, PSO, and ACO as benchmarks is provided in Section 2, from lines 315 to 339 are coloured red.

7.      

About communication QoS requirement. In Section 3 (lines 374 and 375), we can know the QoS requirements are the minimum data rate needs of individual users. The authors say that some challenges are to be addressed in this study (line 308), and the proposed approach adapts to varying communication demands considering real-time QoS metrics (e.g., latency, throughput, and reliability). The latency is also involved in modelling the environment (Eq. 14). It would be desirable to provide more results in the experiment section to demonstrate the basis of these specific communication demand metrics have been met, rather than just the values of the fitness function and QoS compliance based on the computational equations proposed by the authors.

Thank you for your insightful comment regarding the communication QoS requirements and the need to demonstrate specific metrics like latency, throughput, and reliability in the experimental results. To address this concern, additional results focusing on latency have been produced and included in Section 5.6 of the manuscript, spanning pages 38 to 42.

The new result of latency is provided in Section 5.6, from lines 1163 to 1251 are coloured red.

8.      

 In this manuscript, an equation number corresponds to multiple equations, and the citations and explanations for them are confusing. For example, there are two Eq. (1) (lines 400 and 428). The equation numbers cited in lines 467 and 471 also need to be checked.

We thank the reviewer for bringing this issue to our attention. We have carefully reviewed the manuscript and corrected all the equation's numbering to ensure that each equation is uniquely identified.

From page 12 to page equations (1) to page 26 equations (37).

9.      

The first sentence of the 3rd paragraph on page 14 (line 482) cites Ref. [42], which is not relevant to the content of this sentence.

We appreciate the reviewer for identifying this inconsistency. Upon review, we found that Ref. [42] is not relevant to the content of the cited sentence in the 3rd paragraph on page 14 (line 482). We have replaced Ref. [42] with the appropriate reference that aligns with the content of the sentence.

The ref has been updated and coloured red in line 498

10.    

The max number of iterations for the experiments is 17,000. Please explain why this value is set. In Ref. [6], the max number of iterations is 500. In Table 20 of the literature, the GA is adopted, and the worst value of average iteration times is not more than 120. The work in Ref. [14] compares PSO, GA, ACO, etc. with experimental settings and obtained the maximum numbers of iterations less than 50. In this paper, authors also point out that the improved algorithm should reduce the computational overhead (line 242). However, in the simulation experiments, the metaheuristic algorithms process and iterate so many times, which may also result in a waste of computing resources.

We thank the reviewer for raising this important point regarding the maximum number of iterations (17,000) used in our experiments.

The justifications for the 17,000 iterations are given in Section 3.3 in lines 628 to 680 and in Table 2 references given [35] are coloured red.

11.    

The energy consumption of a UAV-BS is mentioned in Section 3.2 (line 563, page 16). How much is the value of energy consumption set in the simulations and according to what.

We appreciate the reviewer for pointing out the need for clarity regarding the energy consumption of UAV-BS in Section 3.2 (line 563). In the current study, energy consumption is mentioned conceptually to highlight its importance in UAV operations. However, this work does not explicitly simulate or evaluate UAV energy consumption values as a parameter.

The information regarding energy consumption is in Section 3 between lines 388 to 397.

12.    

On page 22, the two equations provided in lines 700 and 727 are the same.

We thank the reviewer for pointing out the redundancy in the two equations provided on page 22 (lines 700 and 727). Upon review, we confirm that the equations are indeed the same, which was unintentional and may lead to confusion.

Updated equations (28) is on page 22

13.    

In the last paragraph of page 25 (lines 782–784), we know that the algorithms stop processing when reaching the maximum number of iterations or achieving a stable fitness value across generations. That is to say, the convergence criteria will be satisfied by fewer iterations.  Therefore, it is also important to compare the max and min iteration times of the metaheuristic algorithms in the evaluation section.

The algorithms are designed to stop processing when either the maximum number of iterations (17,000) is reached, or a stable fitness value is achieved across generations. While the focus of this study is on the robustness and performance of the proposed GA+APC algorithm, future work will include a detailed comparison of the maximum and minimum iteration times required for convergence. This will provide additional insights into the computational efficiency of the metaheuristic algorithms under varying scenarios. In the current manuscript, we have added a brief discussion in the evaluation section highlighting the need for such comparisons and noting that the maximum number of iterations (17,000) was set to evaluate long-term stability under extended operation.

14.    

Fitness scoring is done using Eq. (16), where seven weights balance the importance of each metric to make the equation adapt to specific scenarios. How are the values of these weights set and on what basis?

We appreciate the reviewer’s inquiry regarding the determination of the weights in the fitness function (Eq. 28). The weights were assigned based on a combination of heuristic knowledge, problem-specific priorities, and empirical validation during preliminary experiments.

The weights were fine-tuned through iterative simulations to achieve an optimal balance across various environmental scenarios, including urban, suburban, dense urban, and high-rise urban settings. This process involved testing different weight configurations and analyzing their impact on the overall fitness score and performance metrics, such as coverage, and QoS compliance.

15.    

In Section 5.2, the coverage ratios vs. the number of UAV swarms in four different environments are shown in Figures 5–8. There are two mistakes in the sentence “Figures 5, 6, 7, and 8 further illustrate the relationship between UAV swarm size and coverage in high-rise urban settings.” (lines 964 and 965). Only Figure 8 is in high-rise urban settings. In addition, “UAV swarm size” is confused with “number of UAV swarms”.

We appreciate the reviewer’s observation regarding the inaccuracies in the description of Figures 5–8 in Section 5.2.

The modification and corrections were done in lines 1019 to 1021 are coloured red.

16.    

The information of some references, such as Ref. [33] and Ref. [38], needs to be supplemented.

We thank the reviewer for identifying the need to supplement the information for certain references, such as Ref. [33] and Ref. [38] it became Ref. [37] in the new version. We agree that all references should provide comprehensive details for the benefit of readers and researchers. However, we would like to clarify that Ref. [38], titled Aerial Base Station Deployment for Post-Disaster Public Safety Applications, is a thesis and not a published paper. As such, it does not have additional publication details such as a journal name, volume, or DOI.

Despite this, the thesis is highly relevant to our work as it provides foundational insights into UAV-enabled communication systems in post-disaster scenarios. We have cited it appropriately to acknowledge its significance and contribution to this study.

To ensure clarity, we have reviewed the reference formatting to confirm it adheres to the required citation style for theses and dissertations. If further clarification or adjustments are needed, we are happy to incorporate them.

Page 43 lines 1264 and 1273.

17.    

Some comments about acronyms.

1) Define acronyms the first time they appear in the abstract as well as the first time they appear in the body of the paper. For example, UE in line 318, RWPM and RPGM in line 360, etc.

2) A term should only correspond to one acronym. Please check the text. For example, NLOS (in line 466) and NLoS (line 625), and LOS (in line 32) and LoS (line 463).

We thank the reviewer for the helpful comments regarding the use of acronyms. Below are the specific actions taken to address these issues:

1.     Defining Acronyms at First Mention:

We reviewed the manuscript to ensure that all acronyms are defined the first time they appear in both the abstract and the main body of the text. For example:

Non-line-of-sight (NLOS): Defined in the Abstract and first used in the main text (line 19).

RWPM (Random Waypoint Model) and RPGM (Reference Point Group Mobility): Defined in the main text (line 368).

2.     Consistency in Acronym Usage:

We ensured that each term corresponds to only one consistent acronym throughout the manuscript. Specifically:

NLOS and LOS: Standardized to uppercase (e.g., NLOS and LOS) across the manuscript to maintain consistency.

All variations such as "NLoS" (line 625) and "LoS" (line 463) were corrected to their standardized forms.

A final check of all acronyms was performed to identify and resolve any similar inconsistencies are coloured red.

18.    

Go through the paper, correct any typographical errors/typos. For example,

1) Line 34: Figure 1 --> Figure 2

2) On page 14, the title of Figure 3 indicates that 80% BSs outage, and the text on the top of the figure tells us that 30% BSs are failed. It is contradictory.

3) In Figure 3, the black inverted triangle denotes the failed BS. However, none of the black inverted triangles appear in the figure.

4) The commas at the end of two equations in lines 679 and 682 should be deleted.

We appreciate the reviewer for identifying these typographical errors and contradictions. We have carefully reviewed the manuscript to address all the issues mentioned and conducted an additional thorough review for any other typographical inconsistencies.

1)     It has been replaced

2) BS outages has been updated

3) Figure 3: UAV swarm path and coverage visualization under 30% BS outage scenario. The black inverted triangles denote failed base stations (out-of-service BS). Note: The UAV swarm is positioned directly over these failed BSs to restore connectivity, which may obscure the black inverted triangles in the figure.

4) the commas has been updated

 Page 11 at line 350 coloured red.

At Page 14 line 505 and 15 line 511

All commas at the end of all equations have been removed.

Sr. No.

Reviewer 2’s Comments

Our Response

Changes in the Manuscript

1.      

 in the abstract, I suggest removing the notation (line 13): “The solution exhibited significant stability, maintaining an average fitness score of 0.95 over 17,000 iterations.” Replace this notation with a general statistical statement (it is not important to the reader that there were 17,000 iterations),

We revised the abstract to remove the specific reference to "17,000 iterations" and replaced it with a general statement: “The solution exhibited significant stability, maintaining consistently high performance, highlighting its robustness under dynamic disaster scenarios.”

Updated in the abstract, lines 13 and 14 are coloured purple.

2.      

 in Chapter 3 (line 338), I suggest adding a justification for the choice of the test subject. In addition to justifying that it is a real area, it should be indicated what types of objects are located there and why it can be treated as a model - representative;

We elaborated on the justification for the

25 km × 25 km

25 km×25 km test area. This size was chosen as representative of real-world medium-scale disaster zones. The description now highlights its urban features, population clusters, and dynamic mobility.

Revised in Section 3, lines 338 to 348 are coloured purple.

3.      

in Chapter 3 (line 368) I suggest justifying why the parameters 5Mbps, 2Mbps, 1 Mbps were adopted for the purpose of the article. Isn't it worth referring here to the parameters obtained in the study area,

We justified these data rate parameters by referencing real-world and prior studies. Citations have been added to support these thresholds.

Revised in Section 3, lines 376 to 378 are coloured purple.

4.      

 in Chapter 3 (line 474), I suggest justifying why 50km/h parameters were adopted for the purposes of the article. In particular, justify why the analysis was not carried out for the parameters 10km/h, 20km/h, 30km/h, 40km/h .....

We provided justification for 50 km/h 50 km/h as a balance between rapid deployment. We also acknowledged that future studies could explore other speeds.

Revised in Section 3, lines 485 to 490 are coloured purple.

5.      

 In Section 3.3 Parameter Settings Table 2 Simulation Parameters and Settings for UAV Swarms, justify why the following parameters were adopted:  UE Distribution (UED), UE Mobility Speed, UE Speed, Path Variation, Group Deviation , UAV Swarm, Communication Range, BS Failures, Failure Distribution. These parameters are crucial to the results obtained. These parameters should be justified in detail.

Detailed justifications for all parameters were added in the discussion of  Table~\ref{tab2}. References to relevant studies were provided to validate these choices.

Justifications added in Section 3.3 from lines 632 to 684 are coloured purple.

Sr.

Reviewer 3’s Comments

Our Response

Changes in the Manuscript

1.      

In the Abstract section the following sentences are irrelevant as the abstract should be  precise  and should focus on your work only.

The Reference Point Group Mobility (RPGM) model consistently achieved higher coverage rates (95%) compared to Random Waypoint Model (RWPM) (90%), demonstrating the importance of group-based mobility  patterns in enhancing UAV deployment efficiency.

We appreciate the reviewer's suggestion regarding the Abstract. The sentence in question highlights the importance of group-based mobility patterns, as demonstrated by the evaluation of the Reference Point Group Mobility (RPGM) model and the Random Waypoint Model (RWPM). This comparison is a critical part of the evaluation of our results, as discussed in detail in Section 5.5 Figure 11: Impact of Mobility Models on Coverage Provided by Swarm UAVs, where the analyses of the mobility models' impact on UAV deployment efficiency and coverage rates.

Section 5.5

2.      

  In line 91 you forgot to mention about  Section 3?

Section 3 describes the environment modelling, path loss modelling, problem definition, and parameter settings.

Revised in Section 1, line 91 is coloured blue.

3.      

The github repository doesn’t show the following

·       `experiment/`: Scripts for setting up experiments, processing results, and analyzing performance.

·       `network/`: Core modules for base station assignment, capacity calculation, and QoS evaluation.

·       `mobility/`: Mobility models for simulating dynamic user movements.

·       `utilities/`: Helper functions for clustering, visualization, and algorithm execution.

·       `results/`: Plots and data from simulations across various scenarios.

The directory has been updated

4.      

The authors should provide more detailed information about the software, hardware platforms and the environment  used to implement the algorithm. This is necessary for future researchers to replicate and extend the study.

The implementation of the proposed GA+APC algorithm was conducted using Python. The simulations were performed on a system equipped with a 2.4 GHz Intel Core i7-7600U CPU, 64 GB of RAM, and an NVIDIA T1000 (4 GB) graphics card. This hardware configuration was selected to ensure computational efficiency for managing the complex optimization tasks and large-scale disaster scenarios simulated in this study. The operating environment was configured to execute multiple iterations of the algorithm under varying conditions, facilitating a comprehensive evaluation of its performance

Revised in Section 3, lines 707 to 714 are coloured blue.

5.      

 How will your proposed algorithm respond to the following?

a) Terrain variations

b) Weather conditions

c) Real-time network demands i.e. to respond quickly to an event

“Although this study addresses numerous critical challenges, certain aspects remain beyond the scope and represent valuable avenues for future research. For instance, the incorporation of terrain variations and meteorological conditions into the optimization framework will enhance the algorithm's adaptability to real-world scenarios. Subsequent investigations will also explore real-time network demand management utilizing predictive algorithms to facilitate rapid response to dynamic events.”

Revised in Section 6, lines 1271 to 1283 are coloured blue.

6.      

 How will you address connectivity disruptions (fault tolerance) to ensure uninterrupted connectivity?

“ Furthermore, fault tolerance mechanisms such as rerouting strategies and resource redundancy should be developed to ensure uninterrupted connectivity in disaster-affected areas.”

Revised in Section 6, lines 1271 to 1283 are coloured blue.

7.      

Given that battery life is a critical issue for UAVs, how will your algorithms address energy constraints in future deployments involving multiple UAVs?

I acknowledge the importance of energy constraints for UAVs. In future work, is considered “Given the significance of UAV energy constraints, subsequent research will incorporate energy-efficient trajectory optimization and renewable energy solutions to extend operational endurance. ”

Revised in Section 6, lines 1271 to 1283 are coloured blue.

8.      

How do you ensure user privacy as multiple UAVs collect and relay user data?

User privacy is a critical concern, and future work will integrate privacy-preserving mechanisms. “Furthermore, ensuring user privacy during data collection and relay operations is a critical focus, with plans to implement encryption protocols, privacy-preserving data aggregation, and adherence to privacy standards.”

Revised in Section 6, lines 1271 to 1283 are coloured blue.

Sr.

Reviewer 1’s Comments

Our Response

Changes in the Manuscript

1.      

1. In the cover letter, the authors said “The justifications for the 17,000 iterations are given in Section 3.3 in lines 628 to 680 and in Table 2 references given [35] are coloured red.” However, I cannot find the justifications in lines 628 to 680 and Table 2.

We apologize for the oversight and confusion caused. The justification for the 17,000 iterations in our study is based on a reference methodology presented in the paper ref [30]. We selected this iteration count to ensure comparability and consistency with validated methodologies. To clarify this, we have revised Section 3.3, explicitly adding this explanation and citing the reference for transparency.

Additionally, we have reviewed the Table 2.

The justifications for the 17,000 iterations are given in Section 3.3 in lines 698 to 702 and in Table 2 are coloured red.

2.      

The information of some references, such as Ref. [27] (previous Ref. [33]), needs to be supplemented.

Thank you for pointing this out. We have revisited Ref. [27] and similar references to ensure they include complete and accurate bibliographic information. This has been updated in the revised manuscript.

Page 9 ref [27] coloured red.