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Dear Authors,
Thank you for addressing all the reviewers' comments in a satisfactory manner. I can confirm that the quality of your paper has been improved to the extent that it is now ready for publication, following this revision.
Best wishes,
[# PeerJ Staff Note - this decision was reviewed and approved by Carlos Fernandez-Lozano, a 'PeerJ Computer Science' Section Editor covering this Section #]
According to the response letter, the paper has been revised well according to the previous reviewers, and the current version of the manuscript is acceptable for publication.
According to the response letter, the paper has been revised well according to the previous reviewers, and the current version of the manuscript is acceptable for publication.
According to the response letter, the paper has been revised well according to the previous reviewers, and the current version of the manuscript is acceptable for publication.
According to the response letter, the paper has been revised well according to the previous reviewers, and the current version of the manuscript is acceptable for publication.
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Dear authors,
The reviews for your manuscript are included at the bottom of this letter. According to this reviews, your article has not been recommended for publication in its current form. However, we do encourage you to address the concerns and criticisms of the reviewers and resubmit your article once you have updated it accordingly.
Best wishes,
This manuscript offers a quantum-based improved seagull optimization algorithm to optimize the convergence and distribution of solutions in multi-objective optimization problems. Adequate revisions to the following points should be undertaken to justify the recommendation for publication.
Proofread the manuscript carefully to eliminate any grammatical errors or typos and ensure clarity and coherence in writing. Additionally, adhere to the formatting and style guidelines specified by the target journal or publication venue to enhance the professionalism of the manuscript.
The abstract section is fragile. Please rewrite it, explain the result obtained and contribution, improve a proposed method, and delete unnecessary information.
The authors should clearly state the limitations of the proposed method in other real applications.
I suggest the authors add a table at the end of the literature review and compare the reviewed papers to clarify the research gap better.
Please re-write your contribution to this paper in the Introduction section.
How did the authors set parameters for their proposed algorithm? Please make sensitivities of these parameters to the performance of their proposed algorithm!
Incorporating relevant and recent academic sources could strengthen your paper's validity and give readers more context and background. Quantum-inspired metaheuristic algorithms: comprehensive survey and classification, advances in manta ray foraging optimization: a comprehensive survey, an improved heterogeneous comprehensive learning symbiotic organism search for optimization problems, chaotic rime optimization algorithm with adaptive mutualism for feature selection problems, , chaotic-based divide-and-conquer feature selection method and its application in cardiac arrhythmia classification, an improved african vultures optimization algorithm using different fitness functions for multi-level thresholding image segmentation.
Expand the critical results in the conclusion. Focus on the main developments in the finale. Also, write the main contributions in the conclusion.
Numerical results are good enough, but more explanations are required to analyze each figure presented.
The simulation section needs to be more detailed. The authors should provide more information about the data they employed and the simulation process.
Please Change the “conclusion” section to “ Conclusion and Future Work” and write future work.
All figures are of low quality, so please improve all of them.
Good luck
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The authors should clarify the phrase "This technique allows the DBN to descend into local minima" in the abstract. Does this mean the proposed method got trapped in local minima?
The authors should also clarify how equations 10 and 11 were implemented in relation to the operators of the traditional Seagull optimizer.
Was there any validation of the introduced quantum operators into the Seagull optimizer before applying them to the classification problem?
The authors should compare QISOA with recent state-of-the-art optimizers on recent CEC problems before applying it to the classification problem.
Figures 2, 3, and 4 are of low resolution; the authors should improve the quality of all the images in the manuscript.
The "Experiment and Discussion" section is unclear. The results of the proposed QISOA-DBN are not compared with any machine learning methods in the experiment.
The table reporting the results for Figure 7 is missing.
For Figure 7, why was QISOA-DBN not included in the comparison, since it is also a nature-inspired optimizer-improved machine learning model?
Based on Figure 7, the quantity of data used is significantly low.
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