PONE-D-20-10803

Hybrid Algorithms for Generating Optimal  Designs for Discriminating Multiple Nonlinear Models under Various Error Distributional Assumptions

PLOS ONE

Dear Dr. Wong,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Please submit your revised manuscript by Jul 11 2020 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

• A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
• A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
• An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: http://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols

We look forward to receiving your revised manuscript.

Kind regards,

Ping He, Ph.D., Full Prof., 

Academic Editor

PLOS ONE

Journal Requirements:

When submitting your revision, we need you to address these additional requirements.

1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at

https://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and

https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf

2. Please note that PLOS ONE has specific guidelines on software sharing (http://journals.plos.org/plosone/s/materials-and-software-sharing#loc-sharing-software) for manuscripts whose main purpose is the description of a new software or software package. In this case, new software must conform to the Open Source Definition (https://opensource.org/docs/osd) and be deposited in an open software archive. Please see http://journals.plos.org/plosone/s/materials-and-software-sharing#loc-depositing-software for more information on depositing your software.

Additional Editor Comments (if provided):

Please polish your manuscript carefully according to the reviewer's opinion.

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Partly

Reviewer #3: Yes

Reviewer #4: Yes

Reviewer #5: Yes

Reviewer #6: Yes

Reviewer #7: Yes

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: I Don't Know

Reviewer #2: Yes

Reviewer #3: Yes

Reviewer #4: Yes

Reviewer #5: Yes

Reviewer #6: Yes

Reviewer #7: I Don't Know

**********

3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: No

Reviewer #2: Yes

Reviewer #3: Yes

Reviewer #4: Yes

Reviewer #5: Yes

Reviewer #6: Yes

Reviewer #7: No

**********

4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: No

Reviewer #3: No

Reviewer #4: No

Reviewer #5: Yes

Reviewer #6: Yes

Reviewer #7: Yes

**********

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: Subject: Review of Manuscript Number: PONE-D-20-10803

Manuscript Title: Hybrid Algorithms for Generating Optimal Designs for Discriminating Multiple Nonlinear Models under Various Error Distributional Assumptions

After carefully reading the article in detail, I came to the conclusion that it was not my field of research, so I cannot review this article accurate completely.

I apologies for this decision.

Reviewer #2: This paper proposes hybrid algorithms based on particle swarm optimization (PSO) to solve such optimization problems when there are multiple models to discriminate, including cases when the optimal design is singular, the mean response of one or more models are not fully specified and problems that involve four layers of nested optimization.The content of the study is interesting.I have several comments about the paper.

1. In this paper, the authors proposed he basic particle swarm optimization algorithm for Generating Optimal Designs for Discriminating Multiple Nonlinear Models.The method used is not novel enough. This algorithm has been out for a long time. The author had better find a new algorithm to solve it.

2.The writing and format of the paper should be improved. It is not clear in several sections. There are some grammatical errors in the article. The author checks them carefully.

3.The introduction is well written and gives an insight of the state of the art. However, to me it fails on giving the reader a realistic scenario in which the proposed routing algorithm could be applied. Introduction is not clear. There are long sentences and they are difficult to understand. Moreover, some ideas are repeated several times. The writing should be also reviewed. There are some typos.

4. The simulation environment parameters are not detailed and the simulation comparison is not deep. It is necessary to add comparative simulation to illustrate the superiority of the proposed method.

Reviewer #3: The authors address the topic of optimal designs for discriminating multiple nonlinear models. The aim of the paper is to design effective algorithms to solve optimal discrimination design problems when there are two or more nonlinear models and errors may or may not be normally distributed. The authors proposed two algorithms based on particle swarm optimization (PSO) to find the optimal discrimination design. Then they did comprehensive experiments to evaluate their proposed algorithms and compared their algorithms with other related algorithms such as Remes algorithm. Based on the analysis and the experiments, the authors asserted that their algorithms are effective and outperform other related algorithms. Overall, the results in the paper are new to the best of my knowledge. However, the manuscript is not very well presented. Thus, I would recommend its publication only if the following issues are addressed.

Main issues:

1) I suggest the authors add “Inputs” in Algorithm 1 to improve readability.

2) The manuscript presents many grammatical errors. Some errors are presented as follows:

In line 73 of page 3, paraameter -> parameter

In line 129 of page 5, propose in Section an algorithm... (add section number)

In line 148 of page 6, propose in Section an algorithm... (add section number)

In line 175 of page 7, cpu time -> CPU time

In line 347 of page 15, For example, we observe in Section ... (add section number)

I recommend the authors to have the manuscript proofread by a colleague.

Reviewer #4: This is an interesting report that addresses optimal discrimination design problems, but I have a number of concerns which I would like to see addressed.

Importantly, please have the manuscript edited by a native English speaker as it contains numerous grammatical and spelling errors

Reviewer #5: Respected Editor

I did considered the manuscript "PONE-D-20-10803"

The manuscript is sound good and contains enough novelty and deserve to be published in Plos one.

Therefore, I recommend to publish it in the current form.

Reviewer #6: A very good effort in optimal solution field. This research will be helpful to select explicit model in variety of solutions where all solutions are seemed correct. The technique used in this article is also helpful for the beginners.

Best of luck

Reviewer #7: Title: Hybrid Algorithms for Generating Optimal Designs for

Discriminating Multiple Nonlinear Models under Various

Error Distributional Assumptions

• Authors investigate different algorithms being used for searching optimal design to discriminate models under various assumptions on error.

• Authors provide a detailed review on T- and KL-optimal Design Criteria and Maximin T- and KL-optimal Design Criteria

• They indicate that run time of the earlier algorithms is not endurable

• They propose hybrid algorithms based on the concept of particle swarm optimization.

Minor Comments

• Page 1 line 3, “apart from the unknown model parameters” should be deleted.

• Page 2 line 3, algorithm should be replaced with algorithms.

• Page 3 line 75, define Өi and Rmi

• Page 9 line 233, homoscedastic should be replaced with homoscedastic error

• Page 11 line 277, “in that they” should be replaced with “and”.

Final Comments

• Overall idea of the paper is good

• Authors claim to have significantly less run time as compared to earlier algorithms

• The R-code or Mathematica code used to develop algorithms should be provided.

Decision

A minor revision of paper is required in the light of following points:

• Authors should correct typos errors

• Paper may be accepted after minor revision and providing R code to verify results.

**********

6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: No

Reviewer #3: No

Reviewer #4: No

Reviewer #5: No

Reviewer #6: No

Reviewer #7: Yes: Tanvir Ahmad

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

Attachments

Attachment

Submitted filename: comment.pdf

Attachment

Submitted filename: PONE-D-20-10803 Reviewer comment.docx

Attachment

Submitted filename: Review Report of PONE.docx

Attachment

Submitted filename: Final report-PLOS One.docx

Hybrid Algorithms for Generating Optimal  Designs for Discriminating Multiple Nonlinear Models under Various Error Distributional Assumptions

PONE-D-20-10803R1

Dear Dr. Wong,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.

If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

Kind regards,

Ping He, Ph.D.

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

The paper can be accepted.

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #2: All comments have been addressed

Reviewer #4: All comments have been addressed

Reviewer #6: All comments have been addressed

Reviewer #7: (No Response)

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #2: Yes

Reviewer #4: Yes

Reviewer #6: Yes

Reviewer #7: (No Response)

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #2: Yes

Reviewer #4: Yes

Reviewer #6: (No Response)

Reviewer #7: (No Response)

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #2: Yes

Reviewer #4: Yes

Reviewer #6: Yes

Reviewer #7: (No Response)

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #2: Yes

Reviewer #4: Yes

Reviewer #6: Yes

Reviewer #7: (No Response)

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #2: The author has revised the paper according to the questions raised by the reviewers. The paper is innovative and suggested to be accepted.

Reviewer #4: Dear authors,

I feel the manuscript is drastically improved.

However, I'm still not sure about the the use of vancouver citation in the body of the text as either a subject or an object of a sentence. I am willing to let this pass.

Reviewer #6: (No Response)

Reviewer #7: (No Response)

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #2: No

Reviewer #4: No

Reviewer #6: No

Reviewer #7: Yes: Tanvir Ahmad