Improved Multi-Strategy Matrix Particle Swarm Optimization for DNA Sequence Design

Round 1

Reviewer 1 Report

This manuscript introduces an improved multi-strategy matrix particle swarm-based optimization algorithm, named IMPSO, to resolve a multi-objective DNA sequence design problem.

1- The investigated problem is multi-objective (7 objectives). However, only one constraint is presented (??=??%).

2- Please highlight the contributions and the research questions on the introduction.

3- Please explain the choice of values for the related parameters of the IMPSO algorithm (Table 3).

4- Please briefly introduce the NCIWO, MO-ABC, CPSO, and DMEA HSWOA (section 4.3.1.)

5- Too old references : most of the references are published before 2016 ! the related works should investigate the recent state of the art, and the authors have to justify the need of innovative optimization paradigms such as the PSO for resolving real world problems such as the DNA sequence design. In this regard, you can refer to the following recent references explaining this need: https://doi.org/10.3390/info9110284  and  https://doi.org/10.1007/s13748-020-00219-x 

6- References need to be formatted (please follow the reference format proposed by the journal).

7- Please correct the funding section: "Funding: Please add: This research received no external funding." >> "Funding: This research received no external funding." and I think there is a funding for this research (indicated as an Acknowledgment).

Author Response

1. The investigated problem is multi-objective (7 objectives). However, only one constraint is presented (??=??%).

Answer:

Thank you very much for your questions and reminders. After careful analysis and consideration, I found that my statement was not accurate enough. The constraints I took include GC=50% and Tm, The objective functions include Continuity，Hairpin，H-measure，Similarity。Among them, controlling Continuity and Hairpin can avoid secondary structure which is not conducive to DNA computation. Controlling H-measure and Similarity can control nonspecific hybridization which obstructs DNA computing. By combining the objective function with the constraints of DNA chain, better DNA chain can be generated, making DNA computing more efficient. Thanks to your preciseness, I was able to correct this error and make my article more accurate in expression. In addition, I also deleted the introduction of free energy of DNA chain constraint conditions in the original text, because the relevant description has not been changed compared with other documents, and the addition will make the article messy, so I deleted this part. At the same time, the final objective function has been modified and marked in the paper because the specific DNA restriction conditions have not been stated in the paper due to negligence and I felt really sorry for that. The modification results are as follows.

2. Please highlight the contributions and the research questions on the introduction.

Answer:

I really appreciate for your reminding. The previous introduction section did not clearly describe the research issues and contributions. In order to highlight the contributions and research issues, I emphasized the innovative methods used by the IMPSO algorithm proposed in this article and its efficiency in DNA computing. These changes are marked in the introduction. The main contributions of this study are as follows:

1) The matrix particle swarm optimization is introduced to improve the efficiency of traditional PSO.

2) On the basis of centroid  opposition-based, the influence of optimal and worst position is considered to make the position update more reasonable.

3) The concept of signal-to-noise ratio distance is led into, and the formula conforming to the internal state of the population is designed.

4) In the DNA sequence optimization design experiment, the rationality and effectiveness of IMPSO are verified by comparing with the variations of various algorithms.

3. Please explain the choice of values for the related parameters of the IMPSO algorithm (Table 3).

Answer:

Thank you very much for your reminder. The parameters Length of the individual=20 and Hamming Distance=11 have uniform provisions. In order to ensure the fairness of the experimental results, I also adopted the same parameter settings. Other parameters are internal parameters, where Maximum number of iterations, The setting of size of the population refers to references [1] and [2]. Since the dynamic boundary is introduced to constrain the population, the dynamic constant has a maximum and a minimum value namely Minimum number of dynamic constant, Maximum number of dynamic constant. The self-learning factor and social learning factor of particles also have initial and minimum constraints, which are respectively Initial factor for self-learning, Minimum factor for self-learning iterations, Initial factor for social learning, Minimum factor for social learning.

Reference Papers：

[1]. Zhan, Z.H.; Li, J.Y.; Wei, F.F. Matrix-Based Evolutionary Computation. IEEE Transactions on Emerging Topics in Computational Intelligence. 2022, 6, 315-328.

[2]. Zhu D, Huang Z, Liao S, Zhou C, Yan S, Chen G. Improved Bare Bones Particle Swarm Optimization for DNA Sequence Design. IEEE Transactions Nanobioscience. 2022, doi: 10.1109/TNB.2022.3220795. PMID: 36350858..

4. Please briefly introduce the NCIWO, MO-ABC, CPSO, and DMEA HSWOA (section 4.3.1.)

Answer:

Thank you very much for your reminding. I apologize for my mistakes. I have added a brief introduction to the introduction and marked it so that readers can have a basic understanding of these optimization algorithms when reading the introduction. In the final analysis, the readers can more clearly compare and understand the highlights of the algorithms proposed in this article.

5. Too old references : most of the references are published before 2016 ! the related works should investigate the recent state of the art, and the authors have to justify the need of innovative optimization paradigms such as the PSO for resolving real world problems such as the DNA sequence design. In this regard, you can refer to the following recent references explaining this need: https://doi.org/10.3390/info9110284  and  https://doi.org/10.1007/s13748-020-00219-x 

Answer:

We are grateful for your comments. After carefully reading these two papers, in order to make readers more clearly understand the specific application of particle swarm to DNA sequence design, I added the second paper to the application introduction of particle swarm and quoted it in the introduction section. The first article cited the introduction of distance application before the matrix particle swarm uses the SNR distance strategy.

6. References need to be formatted (please follow the reference format proposed by the journal).

Answer:

Thank you very much for your reminding. The citation of documents in the original paper is not standardized enough, which has caused trouble to your work. I apologize for my fault. I have made correct corrections to all the citations of the paper.

7. Please correct the funding section: "Funding: Please add: This research received no external funding." >> "Funding: This research received no external funding." and I think there is a funding for this research (indicated as an Acknowledgment).

Answer:

We are extremely grateful for your reminding. There is indeed an error in filling in the fund section. The funding section has been corrected. Finally, thank you again for your willingness to spend so much effort and your precious time to correct the shortcomings of my article, and I’m sincerely hope to get your approval.

Reviewer 2 Report

This paper proposes an improved multi-strategy matrix particle swarm optimization algorithm to solve the problem of DNA sequence optimization design. The algorithm uses an approach based on signal-to-noise ratio distance to dynamically update the optimal and worst positions of individuals within the population and can adequately search for high quality solutions. The neighborhood centroid opposition-based learning strategy is introduced to improve the search range of the algorithm, and exclude the extreme differences brought by the optimal and worst positions when calculating the center of gravity positions. Comparative experiments and analysis were conducted to evaluate this algorithm.

It is recommended to mention the improved/innovative part and the section summary between Section 3.4 and Section 3.4.1. In fact, a summary paragraph is recommended to be added between every section and its first subsection.

It is recommended to reorganize Sections 2 and 3 into a 'Materials and Methods' Section or follow other common paper structures.

Line 414-475, consider replacing some verbiage with mathematical formulas/expressions.

Author Response

This paper proposes an improved multi-strategy matrix particle swarm optimization algorithm to solve the problem of DNA sequence optimization design. The algorithm uses an approach based on signal-to-noise ratio distance to dynamically update the optimal and worst positions of individuals within the population and can adequately search for high quality solutions. The neighborhood centroid opposition-based learning strategy is introduced to improve the search range of the algorithm, and exclude the extreme differences brought by the optimal and worst positions when calculating the center of gravity positions. Comparative experiments and analysis were conducted to evaluate this algorithm.

I have carefully read the comments and carefully revised the paper item by item according to the suggestions:

1.It is recommended to mention the improved/innovative part and the section summary between Section 3.4 and Section 3.4.1. In fact, a summary paragraph is recommended to be added between every section and its first subsection.

Answer:

Thank you for your valuable suggestions, which will play a very important role in improving the quality of my paper. I think your suggestion is very pertinent and effective, so I added the summary before the chapter that does not involve the chapter summary, which makes the article regulations and context clearer, and also helps readers have a brief understanding of each section before in-depth understanding of each chapter.

2. It is recommended to reorganize Sections 2 and 3 into a 'Materials and Methods' Section or follow other common paper structures.

Answer：

Thank you very much for your reminding and suggestions. In the Section 2 and 3, I think that separating them moderately can make the introduction of methods clearer, but the combination of them you mentioned also gives me another good idea. If I use more strategies in my article, I can combine them to make the article look more organized and general, which is a great suggestion for my future writing, I’m really appreciate for your advice.

3. Line 414-475, consider replacing some verbiage with mathematical formulas/expressions.

Answer：

I feel really grateful for your reminding and suggestions. Please allow me to explain the reason for using text description to introduce the IMPSO algorithm. First of all, because the key steps of the IMPSO algorithm are described in more detail in the previous sections (3.1.3 and 3.1.4), the formula is not directly used in the specific introduction of the process, but the key steps are summarized first and then the relevant formula is quoted. I think this description may be more comprehensive. Secondly, the relevant formula is also cited in the subsequent algorithm flow chart. I think combining the algorithm flow chart and the algorithm process description, the algorithm can be described clearly. Finally, thank you again for your willingness to spend so much effort and precious time to correct the shortcomings of my article, and I sincerely hope to get your approval.