Example of Engineering Optimization format
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Example of Engineering Optimization format Example of Engineering Optimization format Example of Engineering Optimization format Example of Engineering Optimization format Example of Engineering Optimization format Example of Engineering Optimization format
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Example of Engineering Optimization format Example of Engineering Optimization format Example of Engineering Optimization format Example of Engineering Optimization format Example of Engineering Optimization format Example of Engineering Optimization format
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open access Open Access

Engineering Optimization — Template for authors

Publisher: Taylor and Francis
Categories Rank Trend in last 3 yrs
Applied Mathematics #65 of 548 -
Control and Optimization #19 of 111 down down by 6 ranks
Industrial and Manufacturing Engineering #75 of 336 down down by 17 ranks
Management Science and Operations Research #40 of 166 down down by 9 ranks
Computer Science Applications #191 of 693 down down by 19 ranks
journal-quality-icon Journal quality:
High
calendar-icon Last 4 years overview: 496 Published Papers | 2271 Citations
indexed-in-icon Indexed in: Scopus
last-updated-icon Last updated: 02/06/2020
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Related Journals

open access Open Access
recommended Recommended

Taylor and Francis

Quality:  
High
CiteRatio: 8.2
SJR: 1.331
SNIP: 1.866
open access Open Access

Elsevier

Quality:  
Medium
CiteRatio: 1.7
SJR: 0.661
SNIP: 0.966
open access Open Access

Taylor and Francis

Quality:  
High
CiteRatio: 6.4
SJR: 0.884
SNIP: 1.244
open access Open Access
recommended Recommended

Taylor and Francis

Quality:  
High
CiteRatio: 6.8
SJR: 1.321
SNIP: 1.764

Journal Performance & Insights

Impact Factor

CiteRatio

Determines the importance of a journal by taking a measure of frequency with which the average article in a journal has been cited in a particular year.

A measure of average citations received per peer-reviewed paper published in the journal.

2.165

20% from 2018

Impact factor for Engineering Optimization from 2016 - 2019
Year Value
2019 2.165
2018 1.809
2017 1.622
2016 1.728
graph view Graph view
table view Table view

4.6

10% from 2019

CiteRatio for Engineering Optimization from 2016 - 2020
Year Value
2020 4.6
2019 4.2
2018 3.6
2017 3.5
2016 3.4
graph view Graph view
table view Table view

insights Insights

  • Impact factor of this journal has increased by 20% in last year.
  • This journal’s impact factor is in the top 10 percentile category.

insights Insights

  • CiteRatio of this journal has increased by 10% in last years.
  • This journal’s CiteRatio is in the top 10 percentile category.

SCImago Journal Rank (SJR)

Source Normalized Impact per Paper (SNIP)

Measures weighted citations received by the journal. Citation weighting depends on the categories and prestige of the citing journal.

Measures actual citations received relative to citations expected for the journal's category.

0.601

6% from 2019

SJR for Engineering Optimization from 2016 - 2020
Year Value
2020 0.601
2019 0.636
2018 0.557
2017 0.576
2016 0.757
graph view Graph view
table view Table view

1.294

Year Value
2020 1.294
2019 1.294
2018 1.093
2017 1.277
2016 1.166
graph view Graph view
table view Table view

insights Insights

  • SJR of this journal has decreased by 6% in last years.
  • This journal’s SJR is in the top 10 percentile category.

insights Insights

  • This journal’s SNIP is in the top 10 percentile category.
Engineering Optimization

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Taylor and Francis

Engineering Optimization

This journal continues to serve the large technical community concerned with quantitative and computational methods of optimization, and their application to engineering planning, design, manufacture and operational processes. The policy of the journal treats optimization as a...... Read More

Mathematics

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Last updated on
02 Jun 2020
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ISSN
0305-215X
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Impact Factor
High - 1.274
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Open Access
No
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Sherpa RoMEO Archiving Policy
Green faq
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Plagiarism Check
Available via Turnitin
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Endnote Style
Download Available
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Bibliography Name
Taylor and Francis Custom Citation
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Citation Type
Numbered
[25]
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Bibliography Example
Blonder GE, Tinkham M, Klapwijk TM. Transition from metallic to tunneling regimes in superconducting microconstrictions: Excess current, charge imbalance, and supercurrent conversion. Phys Rev B. 1982; 25(7):4515–4532. Available from: 10.1103/PhysRevB.25.4515.

Top papers written in this journal

Journal Article DOI: 10.1080/03052150500384759
Shuffled frog-leaping algorithm: a memetic meta-heuristic for discrete optimization
Muzaffar Eusuff, Kevin Lansey1, Fayzul Pasha1
01 Mar 2006 - Engineering Optimization

Abstract:

A memetic meta-heuristic called the shuffled frog-leaping algorithm (SFLA) has been developed for solving combinatorial optimization problems. The SFLA is a population-based cooperative search metaphor inspired by natural memetics. The algorithm contains elements of local search and global information exchange. The SFLA consi... A memetic meta-heuristic called the shuffled frog-leaping algorithm (SFLA) has been developed for solving combinatorial optimization problems. The SFLA is a population-based cooperative search metaphor inspired by natural memetics. The algorithm contains elements of local search and global information exchange. The SFLA consists of a set of interacting virtual population of frogs partitioned into different memeplexes. The virtual frogs act as hosts or carriers of memes where a meme is a unit of cultural evolution. The algorithm performs simultaneously an independent local search in each memeplex. The local search is completed using a particle swarm optimization-like method adapted for discrete problems but emphasizing a local search. To ensure global exploration, the virtual frogs are periodically shuffled and reorganized into new memplexes in a technique similar to that used in the shuffled complex evolution algorithm. In addition, to provide the opportunity for random generation of improved information,... read more read less

Topics:

Local search (optimization) (58%)58% related to the paper, Memetic algorithm (56%)56% related to the paper, Combinatorial optimization (54%)54% related to the paper, Population (52%)52% related to the paper, Discrete optimization (51%)51% related to the paper
832 Citations
open accessOpen access Journal Article DOI: 10.1080/0305215X.2013.832237
Flower pollination algorithm: A novel approach for multiobjective optimization
Xin-She Yang1, Mehmet Karamanoglu1, Xingshi He2
13 May 2014 - Engineering Optimization

Abstract:

Multiobjective design optimization problems require multiobjective optimization techniques to solve, and it is often very challenging to obtain high-quality Pareto fronts accurately. In this article, the recently developed flower pollination algorithm (FPA) is extended to solve multiobjective optimization problems. The propos... Multiobjective design optimization problems require multiobjective optimization techniques to solve, and it is often very challenging to obtain high-quality Pareto fronts accurately. In this article, the recently developed flower pollination algorithm (FPA) is extended to solve multiobjective optimization problems. The proposed method is used to solve a set of multiobjective test functions and two bi-objective design benchmarks, and a comparison of the proposed algorithm with other algorithms has been made, which shows that the FPA is efficient with a good convergence rate. Finally, the importance for further parametric studies and theoretical analysis is highlighted and discussed. read more read less

Topics:

Multi-objective optimization (58%)58% related to the paper, Metaheuristic (57%)57% related to the paper, Optimization problem (55%)55% related to the paper
View PDF
370 Citations
open accessOpen access Journal Article DOI: 10.1080/03052150211751
Exploration of Metamodeling Sampling Criteria for Constrained Global Optimization
Michael J. Sasena1, Panos Y. Papalambros1, Pierre Goovaerts1
01 Jan 2002 - Engineering Optimization

Abstract:

The use of surrogate models or metamodeling has lead to new areas of research in simulation-based design optimization Metamodeling approaches have advantages over traditional techniques when dealing with the noisy responses and/or high computational cost characteristic of many computer simulations This paper focuses on a part... The use of surrogate models or metamodeling has lead to new areas of research in simulation-based design optimization Metamodeling approaches have advantages over traditional techniques when dealing with the noisy responses and/or high computational cost characteristic of many computer simulations This paper focuses on a particular algorithm, Efficient Global Optimization (EGO) that uses kriging metamodels Several infill sampling criteria are reviewed, namely criteria for selecting design points at which the true functions are evaluated The infill sampling criterion has a strong influence on how efficiently and accurately EGO locates the optimum Variance-reducing criteria substantially reduce the RMS error of the resulting metamodels, while other criteria influence how locally or globally EGO searches Criteria that place more emphasis on global searching require more iterations to locate optima and do so less accurately than criteria emphasizing local search read more read less

Topics:

Metamodeling (56%)56% related to the paper, Global optimization (55%)55% related to the paper, Local search (optimization) (52%)52% related to the paper, Kriging (50%)50% related to the paper
View PDF
367 Citations
Journal Article DOI: 10.1080/03052150410001704854
An improved particle swarm optimizer for mechanical design optimization problems
Shan He, Emmanuel Prempain, Qinghua Wu
01 Oct 2004 - Engineering Optimization

Abstract:

This paper presents an improved particle swarm optimizer (PSO) for solving mechanical design optimization problems involving problem-specific constraints and mixed variables such as integer, discrete and continuous variables. A constraint handling method called the ‘fly-back mechanism’ is introduced to maintain a feasible pop... This paper presents an improved particle swarm optimizer (PSO) for solving mechanical design optimization problems involving problem-specific constraints and mixed variables such as integer, discrete and continuous variables. A constraint handling method called the ‘fly-back mechanism’ is introduced to maintain a feasible population. The standard PSO algorithm is also extended to handle mixed variables using a simple scheme. Five benchmark problems commonly used in the literature of engineering optimization and nonlinear programming are successfully solved by the proposed algorithm. The proposed algorithm is easy to implement, and the results and the convergence performance of the proposed algorithm are better than other techniques. read more read less

Topics:

Multi-swarm optimization (65%)65% related to the paper, Optimization problem (63%)63% related to the paper, Continuous optimization (63%)63% related to the paper, Metaheuristic (63%)63% related to the paper, Meta-optimization (62%)62% related to the paper
335 Citations
Journal Article DOI: 10.1080/03052150500211895
The harmony search heuristic algorithm for discrete structural optimization
Kang-Seok Lee1, Zong Woo Geem2, Sang-ho Lee3, Kyu-woong Bae
01 Oct 2005 - Engineering Optimization

Abstract:

Many methods have been developed and are in use for structural size optimization problems, in which the cross-sectional areas or sizing variables are usually assumed to be continuous. In most practical structural engineering design problems, however, the design variables are discrete. This paper proposes an efficient optimiza... Many methods have been developed and are in use for structural size optimization problems, in which the cross-sectional areas or sizing variables are usually assumed to be continuous. In most practical structural engineering design problems, however, the design variables are discrete. This paper proposes an efficient optimization method for structures with discrete-sized variables based on the harmony search (HS) heuristic algorithm. The recently developed HS algorithm was conceptualized using the musical process of searching for a perfect state of harmony. It uses a stochastic random search instead of a gradient search so that derivative information is unnecessary. In this article, a discrete search strategy using the HS algorithm is presented in detail and its effectiveness and robustness, as compared to current discrete optimization methods, are demonstrated through several standard truss examples. The numerical results reveal that the proposed method is a powerful search and design optimization tool f... read more read less

Topics:

Discrete optimization (70%)70% related to the paper, Metaheuristic (65%)65% related to the paper, Continuous optimization (65%)65% related to the paper, Beam search (65%)65% related to the paper, Best-first search (64%)64% related to the paper
327 Citations
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Engineering Optimization format uses Taylor and Francis Custom Citation citation style.

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Frequently asked questions

1. Can I write Engineering Optimization in LaTeX?

Absolutely not! Our tool has been designed to help you focus on writing. You can write your entire paper as per the Engineering Optimization guidelines and auto format it.

2. Do you follow the Engineering Optimization guidelines?

Yes, the template is compliant with the Engineering Optimization guidelines. Our experts at SciSpace ensure that. If there are any changes to the journal's guidelines, we'll change our algorithm accordingly.

3. Can I cite my article in multiple styles in Engineering Optimization?

Of course! We support all the top citation styles, such as APA style, MLA style, Vancouver style, Harvard style, and Chicago style. For example, when you write your paper and hit autoformat, our system will automatically update your article as per the Engineering Optimization citation style.

4. Can I use the Engineering Optimization templates for free?

Sign up for our free trial, and you'll be able to use all our features for seven days. You'll see how helpful they are and how inexpensive they are compared to other options, Especially for Engineering Optimization.

5. Can I use a manuscript in Engineering Optimization that I have written in MS Word?

Yes. You can choose the right template, copy-paste the contents from the word document, and click on auto-format. Once you're done, you'll have a publish-ready paper Engineering Optimization that you can download at the end.

6. How long does it usually take you to format my papers in Engineering Optimization?

It only takes a matter of seconds to edit your manuscript. Besides that, our intuitive editor saves you from writing and formatting it in Engineering Optimization.

7. Where can I find the template for the Engineering Optimization?

It is possible to find the Word template for any journal on Google. However, why use a template when you can write your entire manuscript on SciSpace , auto format it as per Engineering Optimization's guidelines and download the same in Word, PDF and LaTeX formats? Give us a try!.

8. Can I reformat my paper to fit the Engineering Optimization's guidelines?

Of course! You can do this using our intuitive editor. It's very easy. If you need help, our support team is always ready to assist you.

9. Engineering Optimization an online tool or is there a desktop version?

SciSpace's Engineering Optimization is currently available as an online tool. We're developing a desktop version, too. You can request (or upvote) any features that you think would be helpful for you and other researchers in the "feature request" section of your account once you've signed up with us.

10. I cannot find my template in your gallery. Can you create it for me like Engineering Optimization?

Sure. You can request any template and we'll have it setup within a few days. You can find the request box in Journal Gallery on the right side bar under the heading, "Couldn't find the format you were looking for like Engineering Optimization?”

11. What is the output that I would get after using Engineering Optimization?

After writing your paper autoformatting in Engineering Optimization, you can download it in multiple formats, viz., PDF, Docx, and LaTeX.

12. Is Engineering Optimization's impact factor high enough that I should try publishing my article there?

To be honest, the answer is no. The impact factor is one of the many elements that determine the quality of a journal. Few of these factors include review board, rejection rates, frequency of inclusion in indexes, and Eigenfactor. You need to assess all these factors before you make your final call.

13. What is Sherpa RoMEO Archiving Policy for Engineering Optimization?

SHERPA/RoMEO Database

We extracted this data from Sherpa Romeo to help researchers understand the access level of this journal in accordance with the Sherpa Romeo Archiving Policy for Engineering Optimization. The table below indicates the level of access a journal has as per Sherpa Romeo's archiving policy.

RoMEO Colour Archiving policy
Green Can archive pre-print and post-print or publisher's version/PDF
Blue Can archive post-print (ie final draft post-refereeing) or publisher's version/PDF
Yellow Can archive pre-print (ie pre-refereeing)
White Archiving not formally supported
FYI:
  1. Pre-prints as being the version of the paper before peer review and
  2. Post-prints as being the version of the paper after peer-review, with revisions having been made.

14. What are the most common citation types In Engineering Optimization?

The 5 most common citation types in order of usage for Engineering Optimization are:.

S. No. Citation Style Type
1. Author Year
2. Numbered
3. Numbered (Superscripted)
4. Author Year (Cited Pages)
5. Footnote

15. How do I submit my article to the Engineering Optimization?

It is possible to find the Word template for any journal on Google. However, why use a template when you can write your entire manuscript on SciSpace , auto format it as per Engineering Optimization's guidelines and download the same in Word, PDF and LaTeX formats? Give us a try!.

16. Can I download Engineering Optimization in Endnote format?

Yes, SciSpace provides this functionality. After signing up, you would need to import your existing references from Word or Bib file to SciSpace. Then SciSpace would allow you to download your references in Engineering Optimization Endnote style according to Elsevier guidelines.

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I spent hours with MS word for reformatting. It was frustrating - plain and simple. With SciSpace, I can draft my manuscripts and once it is finished I can just submit. In case, I have to submit to another journal it is really just a button click instead of an afternoon of reformatting.

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