A New Migration and Reproduction Intelligence Algorithm: Case Study in Cloud-Based Microgrid

Round 1

Reviewer 1 Report (Previous Reviewer 2)

Inspired by the migration and reproduction of species in nature to exploreamoresuitable habitat, this paper proposed a new swarm intelligence algorithmcalled the Migrationa nd Reproduction Algorithm (MARA).  It is discussed the organism’s behavior and its mathematics and how it can be used to solve optimization problems. It can see that MARA has features in common with other optimization methods such as particle swarmoptimization(PSO)andfireworks algorithm (FWA). This makes MARA can be also applicable to many sametypesofproblems that PSO and FWA are used to, namely, high-dimensional optimizationproblems.However, MARA also has some unique features among biology-based optimizationmethods. It is made the following contributions in this paper: Initially, It isexpounded the structure of MARA by correlating it with natural biogeography; Furthermore, we demonstrated the performance of MARA on sets of 12 benchmark functions. Eventually, to test the applicability of MARA, we applied it to optimize a practical problem of power dispatching in a multi-microgrid system.

 

 

This manuscript suffers from a number of weak points, it should be further improved before consideration for publication. Let's elaborate on some of them:

 

1.      The abstract should mention the contribution of the paper clearly.  Starting few lines of abstract can be improved.

2.      Language of the paper needs to be improved.

 

3.    The literature review  needs to be extended. The following paper should be considered to give an opportunity to readers about recent real-life applications of recent Mhs.

  

After revisions, I would like to see the manuscript again.

Robust design of electric vehicle components using a new hybrid salp swarm algorithm and radial basis function-based approach." International Journal of Vehicle Design 83.1 (2020): 38-53.

Multi-surrogate-assisted metaheuristics for crashworthiness optimisation. International journal of vehicle design, 80(2-4), 223-240.

Inspired by the migration and reproduction of species in nature to exploreamoresuitable habitat, this paper proposed a new swarm intelligence algorithmcalled the Migrationa nd Reproduction Algorithm (MARA).  It is discussed the organism’s behavior and its mathematics and how it can be used to solve optimization problems. It can see that MARA has features in common with other optimization methods such as particle swarmoptimization(PSO)andfireworks algorithm (FWA). This makes MARA can be also applicable to many sametypesofproblems that PSO and FWA are used to, namely, high-dimensional optimizationproblems.However, MARA also has some unique features among biology-based optimizationmethods. It is made the following contributions in this paper: Initially, It isexpounded the structure of MARA by correlating it with natural biogeography; Furthermore, we demonstrated the performance of MARA on sets of 12 benchmark functions. Eventually, to test the applicability of MARA, we applied it to optimize a practical problem of power dispatching in a multi-microgrid system.

 

 

This manuscript suffers from a number of weak points, it should be further improved before consideration for publication. Let's elaborate on some of them:

 

1.      The abstract should mention the contribution of the paper clearly.  Starting few lines of abstract can be improved.

2.      Language of the paper needs to be improved.

 

3.    The literature review  needs to be extended. The following paper should be considered to give an opportunity to readers about recent real-life applications of recent Mhs.

  

After revisions, I would like to see the manuscript again.

 

Comparison of the arithmetic optimization algorithm, the slime mold optimization algorithm, the marine predators algorithm, the salp swarm algorithm for real-world engineering applications." Materials Testing 63.5 (2021): 448-452.

Comparision of the political optimization algorithm, the Archimedes optimization algorithm and the Levy flight algorithm for design optimization in industry" Materials Testing, vol. 63, no. 4, 2021, pp. 356-359. https://doi.org/10.1515/mt-2020-0053

Conceptual comparison of the ecogeography-based algorithm, equilibrium algorithm, marine predators algorithm and slime mold algorithm for optimal product design" Materials Testing, vol. 63, no. 4, 2021, pp. 336-340. https://doi.org/10.1515/mt-2020-0049

Slime mould algorithm and kriging surrogate model-based approach for enhanced crashworthiness of electric vehicles." International Journal of Vehicle Design 83.1 (2020): 54-68.

Robust design of electric vehicle components using a new hybrid salp swarm algorithm and radial basis function-based approach." International Journal of Vehicle Design 83.1 (2020): 38-53.

Multi-surrogate-assisted metaheuristics for crashworthiness optimisation. International journal of vehicle design, 80(2-4), 223-240.

Enhanced grasshopper optimization algorithm using elite opposition-based learning for solving real-world engineering problems, DOI: 10.1007/s00366-021-01368-w, Engineering with Computers

A novel chaotic Henry gas solubility optimization algorithm for solving real-world engineering problems. Engineering with Computers, 38(2), 871-883.

Robust design of a robot gripper mechanism using new hybrid grasshopper optimization algorithm. Expert Systems, 38(3), DOI: 10.1111/exsy.12666, 2021

A new chaotic Lévy flight distribution optimization algorithm for solving constrained engineering problems. Expert Systems, 2022, e12992.

Robust design of electric vehicle components using a new hybrid salp swarm algorithm and radial basis function-based approach. International Journal of Vehicle Design, 83(1), 38-53.

Comparison of metaheuristic optimization algorithms for solving constrained mechanical design optimization problems. Expert Systems with Applications, 183, 115351.

Multi-surrogate-assisted metaheuristics for crashworthiness optimisation. International journal of vehicle design, 80(2-4), 223-240.

Experimental and numerical fatigue-based design optimisation of clutch diaphragm spring in the automotive industry." International Journal of Vehicle Design 80.2-4 (2019): 330-345.

Comparative investigation of the moth-flame algorithm and whale optimization algorithm for optimal spur gear design." Materials Testing 63.3 (2021): 266-271.

Mechanical engineering design optimisation using novel adaptive differential evolution algorithm." International Journal of vehicle design 80.2-4 (2019): 285-329.

Optimal design of planetary gear train for automotive transmissions using advanced meta-heuristics." International Journal of Vehicle Design 80.2-4 (2019): 121-136.

 

A comparative study of recent multi-objective metaheuristics for solving constrained truss optimisation problems." Archives of Computational Methods in Engineering (2021): 1-17.

Author Response

Thanks to the experts for the valuable opinions! The following is the modification description, which can be consulted:

1.The abstract should mention the contribution of the paper clearly. Starting few lines of abstract can be improved.

Answer: Thank you for the suggestion. We have added our contributions to the abstract already. The below paragraph shows our edits.

 

2. Language of the paper needs to be improved.

Answer: Thank you for your advice. We have improved the language as possible as we can.

 

3. The literature review  needs to be extended. The following paper should be considered to give an opportunity to readers about recent real-life applications of recent Mhs.

Answer: Thank you for your advice. We extended the literature review about the recent real-life applications of Intelligent optimization. The revisions are shown below:

 

 

After revisions, I would like to see the manuscript again.

Robust design of electric vehicle components using a new hybrid salp swarm algorithm and radial basis function-based approach." International Journal of Vehicle Design 83.1 (2020): 38-53.

Answer: The manuscript you want is attached in the compressed package, please check it.

Author Response File: Author Response.docx

Reviewer 2 Report (New Reviewer)

The authors propose a "swarm intelligence algorithm called the Migration and Reproduction Algorithm (MARA)" that can be used to solve optimization problems.

This reviewer has the following doubts/concerns/suggestions: 

1 - The introduction section needs to be expanded with a literature review on existing optimization algorithms, highlighting the gaps they may present and the authors' proposed algorithm can fill.

2 - Equations in Table 1 should be treated as mathematical expressions in the document. I think using a table with the equations is not a good option. 

3 - The text needs to be edited, and The reader needs more careful and organized treatment. As it stands, reading is complex, and the intended message becomes less understandable.

4 - The presented pseudo-codes do not add relevant information. So, as they stand, they can be dispensed with. I propose that the authors review the situation, eventually integrating the various pseudo-code snippets into a more global structure.

5 - Section 2.4 needs further development. Following a previous suggestion, it may make sense to integrate this content in the introduction section, developing it. The authors do not address numerous optimization algorithms. I recommend that authors make a careful review of existing optimization algorithms in the literature, in particular those that are closest to the context of the proposed algorithm.

6 - There are unnumbered tables, for example, the table on page 6.

7 - The benchmark functions presented by the authors in the table on page 6 need to be clarified and explicitly supported in the literature. What does each of these functions measure?

8 - The comparison made by the authors only involves two optimization algorithms other than the one proposed. I believe it would be essential to consider a more significant universe of algorithms to demonstrate the advantages of the proposed algorithm with greater security. In particular, but not limited to this, authors should consider more bio-inspired algorithms, including Artificial flora optimization algorithm; Bacterial colony optimization; Fish migration optimization based on fishy biology, etc.

9 - Authors definitely need to revise the text and reorganize it carefully. Authors should avoid text such as the one on page 10: "(3) What if some microgrids can suffice their load demand and some microgrids cannot". The text must be formal.

 

It needs to be edited. 

Author Response

Thanks to the experts for the valuable opinions! The following is the modification description, which can be consulted:

1. The introduction section needs to be expanded with a literature review on existing optimization algorithms, highlighting the gaps they may present and the authors' proposed algorithm can fill.

Answer: Thank you for your advice. The introduction section has been expanded.

 

2. Equations in Table 1 should be treated as mathematical expressions in the document. I think using a table with the equations is not a good option. 

Answer: Thank you for your advice, we have modified it!

3 The text needs to be edited, and The reader needs more careful and organized treatment. As it stands, reading is complex, and the intended message becomes less understandable.

Answer: Thank you for your advice, we have improve the language as possible as we can.

 

4. The presented pseudo-codes do not add relevant information. So, as they stand, they can be dispensed with. I propose that the authors review the situation, eventually integrating the various pseudo-code snippets into a more global structure.

Answer: The presented pseudo-codes is actually more like the appendix code, but we believe that put those pseudo-codes in article can make reader easier to understand, so we put it in the article.

 

5. Section 2.4 needs further development. Following a previous suggestion, it may make sense to integrate this content in the introduction section, developing it. The authors do not address numerous optimization algorithms. I recommend that authors make a careful review of existing optimization algorithms in the literature, in particular those that are closest to the context of the proposed algorithm.

Answer: Thank you for your advice, we have modified it. In Section 2.4, our main point is to propose the characteristics of MARA. The comparison between your proposed and existing algorithms has been mentioned in the introduction section.

 

6. There are unnumbered tables, for example, the table on page 6.

Answer: Thank you for your advice, we have modified it

 

7. The benchmark functions presented by the authors in the table on page 6 need to be clarified and explicitly supported in the literature. What does each of these functions measure?

Answer: Thank you for your advice, we have modified it!

 

8 .The comparison made by the authors only involves two optimization algorithms other than the one proposed. I believe it would be essential to consider a more significant universe of algorithms to demonstrate the advantages of the proposed algorithm with greater security. In particular, but not limited to this, authors should consider more bio-inspired algorithms.

Answer: Thank you for your advice, we have added more bio-inspired algorithms in introduction part, like Slime Mould Algorithm. Just as we said, the MARA is a new bio-inspired algorithms which is different from the other kind of bio-inspired algorithms. We believe that using the classic optimization algorithm to compare with MARA can also reflect the advantages. Your advice are very valuable, we will pay more attention to using the same type of bio-inspired algorithms for comparison in our future work.

Author Response File: Author Response.docx

Round 2

Reviewer 2 Report (New Reviewer)

I would like to thanks the authors for their attempt to answer my suggestions. 

I believe the manuscript is better now. I also believe that the authors could still achieve a better work if they responded in full to the suggestions I presented previously. Still, the manuscript appears to be of sufficient quality for publication.

In my opinion, a revision of the text remains necessary. 

This manuscript is a resubmission of an earlier submission. The following is a list of the peer review reports and author responses from that submission.

Round 1

Reviewer 1 Report

A new metaheuristic called MARA is proposed.

 

The design of metaheuristics is interesting research.

Also, this is important in practice, as one can create approaches for solving problems better than other methods from the literature.

 

Some comments follow regarding the submitted manuscript, with suggestions on how to improve it.

 

The text requires improvements.

There are some phrases hard to read or with typos, such as (but is not restricted to) the following ones:

• "The population belongs to the common" ==> common what?
• These two type populations described above will reproduce offspring populations.
• Next, we will discuss reproduction mechanism form mathematic perspective mainly.

 

When the text after an equation is part of the same phrase, this text is aligned to left.

 

Which are the bounds of the random number generator? -1 and 1?

 

As commented by the authors, MARA is similar to other metaheuristics. In fact, some operators are about the same as PSO. The migration strategy can be considered as that from the island model. In addition, one also can observe similarities with FA. Thus, I guess that the proposed approach is a hybrid method that combines metaheuristics.

 

The computational experiments are composed of only 9 test problems with only 2 dimensions. Currently, new metaheuristics must be evaluated using a larger benchmark and with a wide range of dimensions. Thus, the evaluation of the proposal must be improved.

 

In "Finally, we analyze them statistically", what does it mean? Do you perform a statistical test for significance? Which test? Where are the results?

 

Figure 3 presents the best result. However, there is no comparison regarding mean and standard deviation in the engineering problem. Also, the Power Dispatching Model optimized is well-known in the literature. Aren't other newer techniques solving the same problem in the literature?

Author Response

First of all, thank the experts for your valuable opinions! The following is the modification description, which can be consulted:

1.There are some phrases hard to read or with typos, such as (but is not restricted to) the following ones:
•    "The population belongs to the common" ==> common what?
•    These two type populations described above will reproduce offspring populations.
•    Next, we will discuss reproduction mechanism form mathematic perspective mainly.
Answer:We are grateful for the suggestion. we have modified it.

2. When the text after an equation is part of the same phrase, this text is aligned to left.
Answer: Thank you for the suggestion. we have modified it.

3. Which are the bounds of the random number generator? -1 and 1?
Answer: Thank you for the comment, and have supplemented it.

4. As commented by the authors, MARA is similar to other metaheuristics. In fact, some operators are about the same as PSO. The migration strategy can be considered as that from the island model. In addition, one also can observe similarities with FA. Thus, I guess that the proposed approach is a hybrid method that combines metaheuristics.
Answer: Thank you for the comment. Although MARA has some features in common with PSO and FA, but it also has own special character likes reproduction mechanism which is original. Moreover, MARA is inspired by the migration and reproduction progress of species in nature to explore a more suitable habitat.

5. The computational experiments are composed of only 9 test problems with only 2 dimensions. Currently, new metaheuristics must be evaluated using a larger benchmark and with a wide range of dimensions. Thus, the evaluation of the proposal must be improved.
Answer: We are extremely grateful to reviewer for pointing out this problem. Using larger benchmarks and wider range of dimensions means that needing better computer hardware support. Next, we will take improving computer hardware as one of our works, and show the results that using a larger benchmark functions and broad dimensions in the next paper. Thus, we have not modified according the comment.

6. In "Finally, we analyze them statistically", what does it mean? Do you perform a statistical test for significance? Which test? Where are the results?
Answer: Thank you for your comment, We have showed the experimental results in the appendix table1 and appendix table2.

7.Figure 3 presents the best result. However, there is no comparison regarding mean and standard deviation in the engineering problem. Also, the Power Dispatching Model optimized is well-known in the literature. Aren't other newer techniques solving the same problem in the literature?
Answer: Thank you for your comment. It is truth that the Power Dispatching Model optimized is well-known. In this paper, we used minimizing the comprehensive operation cost as objective function. We applied MARA in this dispatching optimal problem in order to verify that MARA is more feasible and effective to solve practical problem compared with GWO and HHO. The comparison regarding mean and standard deviation is analysis on different algorithms in Benchmark functions after 20 independent testing, as shown in Appendix-table 1 and Appendix-table 2.

Author Response File: Author Response.docx

Reviewer 2 Report

this paper proposes a new swarm intelligence algorithm called the Migration and Reproduction Algorithm (MARA). We discussed the organism’s behavior and its mathematics and how it can be used to solve optimization problems. We can see that MARA has features in common with other optimization methods such as particle swarm optimization (PSO) and fireworks algorithm (FA). This makes MARA can be also applicable to many same types of problems that PSO and FA are used to, namely, high-dimensional optimization problems. However, MARA also has some unique features among biology-based optimization methods. We demonstrated the performance of MARA on set of nine standard benchmarks and on a practical coordinated power dispatching problem for multi-microgrids system through cloud platform. 

 

1.      The abstract should mention the contribution of the paper clearly.  Starting few lines of abstract can be improved.

2.      Language of the paper needs to be improved.

 

3.    The literature review  needs to be extended. The following paper should be considered to give an opportunity to readers about recent real-life applications of recent Mhs.

  

After revisions, I would like to see the manuscript again.

 

Sine-cosine optimization algorithm for the conceptual design of automobile components‎, MATERIALS TESTING,   Volume: ‏ 62   Issue: ‏ 7   Pages: ‏ 744-748   Published: ‏ JUL 2020

 

Comparision of the political optimization algorithm, the Archimedes optimization algorithm and the Levy flight algorithm for design optimization in industry" Materials Testing, vol. 63, no. 4, 2021, pp. 356-359. https://doi.org/10.1515/mt-2020-0053

Conceptual comparison of the ecogeography-based algorithm, equilibrium algorithm, marine predators algorithm and slime mold algorithm for optimal product design" Materials Testing, vol. 63, no. 4, 2021, pp. 336-340. https://doi.org/10.1515/mt-2020-0049

Multi-surrogate-assisted metaheuristics for crashworthiness optimisation. International journal of vehicle design, 80(2-4), 223-240.

A new chaotic Lévy flight distribution optimization algorithm for solving constrained engineering problems. Expert Systems, 2022, e12992.

Enhanced grasshopper optimization algorithm using elite opposition-based learning for solving real-world engineering problems, DOI: 10.1007/s00366-021-01368-w, Engineering with Computers

Slime mould algorithm and kriging surrogate model-based approach for enhanced crashworthiness of electric vehicles." International Journal of Vehicle Design 83.1 (2020): 54-68.

 

Robust design of electric vehicle components using a new hybrid salp swarm algorithm and radial basis function-based approach. International Journal of Vehicle Design, 83(1), 38-53.

Author Response

First of all, thank the experts for your valuable opinions! The following is the modification description, which can be consulted:

1. The abstract should mention the contribution of the paper clearly.  Starting few lines of abstract can be improved. 
Answer: Thank you for your comment. we have supplemented the contribution of the paper into abstract.

2. Language of the paper needs to be improved.

Answer: We apologize for the language problems in the original manuscript. The full text has been read, the text of the improper place to modify, see the mark of the revised version.

3.The literature review  needs to be extended. The following paper should be considered to give an opportunity to readers about recent real-life applications of recent Mhs.
Answer: Thank you for your advice, we have supplemented the content, and references have been updated according to review comments.

Author Response File: Author Response.docx

Reviewer 3 Report

The MARA is an interesting novel proposal from the swarm inteligence class. The paper is clearly written. The reader is properly guided and acquainted with the subsequent stages of the algorithm. 

I suggest that three situations of the velocity vector uptading equation should be written in enumerate list in the second page. 

Author Response

First of all, thank the experts for your valuable opinions! The following is the modification description, which can be consulted:

I suggest that three situations of the velocity vector uptading equation should be written in enumerate list in the second page.

Answer: Thank you for your suggestion, we have modified it

Author Response File: Author Response.docx

Round 2

Reviewer 1 Report

The text remains hard to read.
For instance (again: not limited to), taking the same phrase from the first review:
"The population belongs to the global optimal, that means the population resided in the best known HSI habitat comparing with all species."
a grammar-checking software pointed out 2 grammatical mistakes.

Notice that (i) this is a single phrase and (ii) this phrase was fixed according to the authors.

 

The number of test problems of the computational experiments was not increased as suggested.
Apologies, but "better computer hardware support" is a requirement in high-level research involving metaheuristics.

No statistical test for significance was performed. Tables 1 and 2 in the Appendix show only some statistical values regarding the results, such as average and variation (standard deviation?).

Finally, some of my comments are not present in the revised version of the manuscript.