Next Article in Journal
Effectiveness of High-Damping Rubber (HDR) Damper and Tuned Mass—HDR Damper in Suppressing Stay-Cable Vibration
Next Article in Special Issue
Simulation-Based Reliability Design Optimization Method for Industrial Robot Structural Design
Previous Article in Journal
Temporal Extraction of Complex Medicine by Combining Probabilistic Soft Logic and Textual Feature Feedback
Previous Article in Special Issue
A Crossrate-Based Approach for Reliability-Based Multidisciplinary Dynamic System Design Optimization
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Applied Sciences
Volume 13
Issue 5
10.3390/app13053355
Review Report
applsci-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
Article
Peer-Review Record

Structural Design of Aerostatic Bearing Based on Multi-Objective Particle Swarm Optimization Algorithm

Appl. Sci. 2023, 13(5), 3355; https://0-doi-org.brum.beds.ac.uk/10.3390/app13053355
by Biqing Ye, Guixin Yu, Yidong Zhang and Gang Li *
Reviewer 1: Anonymous
Reviewer 2:
Hesam Varaee
Appl. Sci. 2023, 13(5), 3355; https://0-doi-org.brum.beds.ac.uk/10.3390/app13053355
Submission received: 13 February 2023 / Revised: 2 March 2023 / Accepted: 4 March 2023 / Published: 6 March 2023
(This article belongs to the Special Issue Structural Optimization Methods and Applications)

Round 1

Reviewer 1 Report

A very nice paper overall.

Authors developed an engineering calculation method for an aero-static bearing. And in section 3.2, they showed the accuracy of the model by comparing it with the simulation results and the test results from another study. Then, using various optimization algorithms found the best working algorithm for their model. One small comment:

A comparison of the processor solution times of simulation vs developed analytical model and a comparison of solution times of different optimization algorithms used may be helpful. 

Author Response

Please see the attachment

Author Response File: Author Response.docx

Reviewer 2 Report

Using Latin hypercube Sampling to modify the particle distribution in the first cycle is not innovative.

It is suggested to conduct a more comprehensive study on similar works in the introduction and examine the strengths and weaknesses of previous works.

Explain the stopping conditions of the algorithm. Why is the algorithm stopped after 60 cycles? What is the justification for convergence?

A change in the Pareto slope can be seen at the end region of the obtained Pareto (in the range of 0.9 to 1). What is the authors' assessment of its cause?

Due to the existence of the random term in the original formulation of the MOPSO Algorithm, it is unlikely that the modification of particles in the first cycle will have a significant effect. It is suggested to use several benchmark problems (for example CEC problems) to prove the efficiency of the proposed method.

It seems that the Diversity Metric should be a number close to one (for better performance). While Figure 14 shows the opposite.

To control the convergence of the two-objective optimization algorithm, it is recommended to find the optimal answer of each objective as a single objective problem.

Information from many references is incomplete (for example, year of publication).

The conclusion section needs to be rewritten.

Author Response

Please see the attachment

Author Response File: Author Response.docx

Round 2

Reviewer 2 Report

The paper is now accepted.

Back to TopTop