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Article
Peer-Review Record

Electric Vehicle Charging Station Layout for Tourist Attractions Based on Improved Two-Population Genetic PSO

Energies 2023, 16(2), 983; https://doi.org/10.3390/en16020983
by Shuang Che 1, Yan Chen 1 and Longda Wang 2,*
Reviewer 1: Anonymous
Reviewer 3:
Energies 2023, 16(2), 983; https://doi.org/10.3390/en16020983
Submission received: 2 December 2022 / Revised: 7 January 2023 / Accepted: 10 January 2023 / Published: 16 January 2023
(This article belongs to the Special Issue Internet of Vehicles for Intelligent Transportation System)

Round 1

Reviewer 1 Report

This paper proposed an improved PSO to optimize the charging station layout for tourist attractions. The paper is written with a good flow, which is convincing and readable. The paper can be accepted after minor modifications:

1. The sentences on page 14, lines 357-359, need to be rewritten.

2. The author should elaborate on the setup for two cases at the beginning of the simulation results (section 4.3). Besides, what is the difference between case 1 and case 2? Why does the author only compare two methods in case 2, while four methods are in case 1?

3. Fig. 7 and Fig.8 seem identical. Is this an unintended mistake? Please clarify.

 

4. The figure quality should be improved before publication. 

Author Response

Please see the attachment

Author Response File: Author Response.pdf

Reviewer 2 Report

A very interesting article.

Line 222, 245 consider whether it is better to use the number of disturbances (interferences are countable or not?)

Photo 6 showing poor quality.

Author Response

Please see the attachment

Author Response File: Author Response.pdf

Reviewer 3 Report

General remarks:

The authors conducted an interesting research result about an improved, multi-objective particle swarm optimization algorithm in the case of electric vehicle charging station layout design. The authors confirmed the efficiency of the improvements.

Chapter 4. demonstrates the applicability of the improved optimization methodology but doesn’t count on any tourist-related factors, only on residents and business people. What is the reason behind putting the „tourist attractions” in the title of the paper? Only the selection of the investigated area doesn’t mean sense to it. How the application of the proposed methodology was different in an area without tourist attractions? I guess there isn’t any remarkable difference between them.

The introduction of the PSO algorithm seems to be totally missing. Consequently, the interpretation of Chapters 3 and 4 is almost impossible for future readers.

Extensive English revision is needed:

-          a couple of too-long and wordy sentences,

- unnecessary division of words,

-          unopened abbreviations (e.g., QDE, IPOQEA)

Specific remarks:

Chapter 1.: the authors list several methodological improvements in the form of sentences, mainly lacking any reasoning (why these methodologies), arguments (e.g., the field of application), and evaluation (pros and cons). The authors introduce the particle swarm optimization methodology but don’t underpin its applicability in the field of EV charging station layout design. I suggest a throughout text revision here.

Equations (2) and (3): where is the „j” index in the equation that the authors let run in the summa from 1 to m?

Equation (10) versus line 176: the equation shows inequality, but the text reports equality. Please revise.

Chapter 3.1. the authors report shortcomings of the PSO algorithm. What does it mean? What are the shortcomings? Chapter 1. doesn’t contain any critical evaluation of PSO. Furthermore, it is confusing that the title of chapter 3 starts with „Basic PSO”, but the authors continue immediately with „Improved PSO” (Chapter 3.1). Where is the description of the „basic PSO” algorithm?

 

Line 199-200: the authors refer to the importance of parameters in the PSO, but the paper doesn’t contain so far any description of PSO parameters and their impacts. 

Author Response

Please see the attachment

Author Response File: Author Response.pdf

Round 2

Reviewer 3 Report

Thank the authors for the modifications. All of them are appreciated and accepted.

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