Example of Structural and Multidisciplinary Optimization format
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Example of Structural and Multidisciplinary Optimization format Example of Structural and Multidisciplinary Optimization format Example of Structural and Multidisciplinary Optimization format Example of Structural and Multidisciplinary Optimization format Example of Structural and Multidisciplinary Optimization format Example of Structural and Multidisciplinary Optimization format Example of Structural and Multidisciplinary Optimization format Example of Structural and Multidisciplinary Optimization format Example of Structural and Multidisciplinary Optimization format Example of Structural and Multidisciplinary Optimization format Example of Structural and Multidisciplinary Optimization format Example of Structural and Multidisciplinary Optimization format Example of Structural and Multidisciplinary Optimization format Example of Structural and Multidisciplinary Optimization format Example of Structural and Multidisciplinary Optimization format Example of Structural and Multidisciplinary Optimization format Example of Structural and Multidisciplinary Optimization format Example of Structural and Multidisciplinary Optimization format
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Example of Structural and Multidisciplinary Optimization format Example of Structural and Multidisciplinary Optimization format Example of Structural and Multidisciplinary Optimization format Example of Structural and Multidisciplinary Optimization format Example of Structural and Multidisciplinary Optimization format Example of Structural and Multidisciplinary Optimization format Example of Structural and Multidisciplinary Optimization format Example of Structural and Multidisciplinary Optimization format Example of Structural and Multidisciplinary Optimization format Example of Structural and Multidisciplinary Optimization format Example of Structural and Multidisciplinary Optimization format Example of Structural and Multidisciplinary Optimization format Example of Structural and Multidisciplinary Optimization format Example of Structural and Multidisciplinary Optimization format Example of Structural and Multidisciplinary Optimization format Example of Structural and Multidisciplinary Optimization format Example of Structural and Multidisciplinary Optimization format Example of Structural and Multidisciplinary Optimization format
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open access Open Access
recommended Recommended

Structural and Multidisciplinary Optimization — Template for authors

Publisher: Springer
Categories Rank Trend in last 3 yrs
Control and Optimization #10 of 111 down down by 3 ranks
Computer Graphics and Computer-Aided Design #14 of 88 down down by 1 rank
Computer Science Applications #115 of 693 up up by 8 ranks
Control and Systems Engineering #46 of 260 up up by 4 ranks
Software #84 of 389 up up by 15 ranks
journal-quality-icon Journal quality:
High
calendar-icon Last 4 years overview: 1138 Published Papers | 7559 Citations
indexed-in-icon Indexed in: Scopus
last-updated-icon Last updated: 14/07/2020
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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.

3.377

14% from 2018

Impact factor for Structural and Multidisciplinary Optimization from 2016 - 2019
Year Value
2019 3.377
2018 3.925
2017 2.876
2016 2.377
graph view Graph view
table view Table view

6.6

6% from 2019

CiteRatio for Structural and Multidisciplinary Optimization from 2016 - 2020
Year Value
2020 6.6
2019 6.2
2018 5.1
2017 4.5
2016 4.8
graph view Graph view
table view Table view

insights Insights

  • Impact factor of this journal has decreased by 14% in last year.
  • This journal’s impact factor is in the top 10 percentile category.

insights Insights

  • CiteRatio of this journal has increased by 6% 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.

1.402

14% from 2019

SJR for Structural and Multidisciplinary Optimization from 2016 - 2020
Year Value
2020 1.402
2019 1.637
2018 1.835
2017 1.458
2016 1.482
graph view Graph view
table view Table view

1.71

3% from 2019

SNIP for Structural and Multidisciplinary Optimization from 2016 - 2020
Year Value
2020 1.71
2019 1.764
2018 1.887
2017 1.799
2016 1.836
graph view Graph view
table view Table view

insights Insights

  • SJR of this journal has decreased by 14% in last years.
  • This journal’s SJR is in the top 10 percentile category.

insights Insights

  • SNIP of this journal has decreased by 3% in last years.
  • This journal’s SNIP is in the top 10 percentile category.

Structural and Multidisciplinary Optimization

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Springer

Structural and Multidisciplinary Optimization

The aim of this international journal is to provide a vehicle for disseminating information about all aspects of structural and multidisciplinary optimization through a single medium. It covers a wide range of topics related to - optimally designing structures (stressed system...... Read More

Control and Optimization

Computer Graphics and Computer-Aided Design

Control and Systems Engineering

Computer Science Applications

Software

Mathematics

i
Last updated on
14 Jul 2020
i
ISSN
1615-147X
i
Impact Factor
High - 2.389
i
Open Access
No
i
Sherpa RoMEO Archiving Policy
Green faq
i
Plagiarism Check
Available via Turnitin
i
Endnote Style
Download Available
i
Bibliography Name
SPBASIC
i
Citation Type
Author Year
(Blonder et al, 1982)
i
Bibliography Example
Beenakker CWJ (2006) Specular andreev reflection in graphene. Phys Rev Lett 97(6):067,007, URL 10.1103/PhysRevLett.97.067007

Top papers written in this journal

Journal Article DOI: 10.1007/S00158-003-0368-6
Survey of multi-objective optimization methods for engineering
R.T. Marler1, Jasbir S. Arora1

Abstract:

A survey of current continuous nonlinear multi-objective optimization (MOO) concepts and methods is presented. It consolidates and relates seemingly different terminology and methods. The methods are divided into three major categories: methods with a priori articulation of preferences, methods with a posteriori articulation ... A survey of current continuous nonlinear multi-objective optimization (MOO) concepts and methods is presented. It consolidates and relates seemingly different terminology and methods. The methods are divided into three major categories: methods with a priori articulation of preferences, methods with a posteriori articulation of preferences, and methods with no articulation of preferences. Genetic algorithms are surveyed as well. Commentary is provided on three fronts, concerning the advantages and pitfalls of individual methods, the different classes of methods, and the field of MOO as a whole. The Characteristics of the most significant methods are summarized. Conclusions are drawn that reflect often-neglected ideas and applicability to engineering problems. It is found that no single approach is superior. Rather, the selection of a specific method depends on the type of information that is provided in the problem, the user’s preferences, the solution requirements, and the availability of software. read more read less
4,263 Citations
Journal Article DOI: 10.1007/S001580050176
A 99 line topology optimization code written in Matlab
Ole Sigmund1

Abstract:

The paper presents a compact Matlab implementation of a topology optimization code for compliance minimization of statically loaded structures. The total number of Matlab input lines is 99 including optimizer and Finite Element subroutine. The 99 lines are divided into 36 lines for the main program, 12 lines for the Optimalit... The paper presents a compact Matlab implementation of a topology optimization code for compliance minimization of statically loaded structures. The total number of Matlab input lines is 99 including optimizer and Finite Element subroutine. The 99 lines are divided into 36 lines for the main program, 12 lines for the Optimality Criteria based optimizer, 16 lines for a mesh-independency filter and 35 lines for the finite element code. In fact, excluding comment lines and lines associated with output and finite element analysis, it is shown that only 49 Matlab input lines are required for solving a well-posed topology optimization problem. By adding three additional lines, the program can solve problems with multiple load cases. The code is intended for educational purposes. The complete Matlab code is given in the Appendix and can be down-loaded from the web-site http://www.topopt.dtu.dk. read more read less

Topics:

Topology optimization (54%)54% related to the paper, Line (geometry) (53%)53% related to the paper, Topology (electrical circuits) (51%)51% related to the paper, Subroutine (51%)51% related to the paper, MATLAB (51%)51% related to the paper
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1,956 Citations
Journal Article DOI: 10.1007/S00158-013-0978-6
Topology optimization approaches: A comparative review
Ole Sigmund1, Kurt Maute2

Abstract:

Topology optimization has undergone a tremendous development since its introduction in the seminal paper by Bendsoe and Kikuchi in 1988. By now, the concept is developing in many different directions, including “density”, “level set”, “topological derivative”, “phase field”, “evolutionary” and several others. The paper gives ... Topology optimization has undergone a tremendous development since its introduction in the seminal paper by Bendsoe and Kikuchi in 1988. By now, the concept is developing in many different directions, including “density”, “level set”, “topological derivative”, “phase field”, “evolutionary” and several others. The paper gives an overview, comparison and critical review of the different approaches, their strengths, weaknesses, similarities and dissimilarities and suggests guidelines for future research. read more read less

Topics:

Topology optimization (54%)54% related to the paper, Topological derivative (54%)54% related to the paper
1,816 Citations
Journal Article DOI: 10.1007/S00158-001-0160-4
Comparative studies of metamodelling techniques under multiple modelling criteria
Ruichen Jin1, Wei Chen1, Timothy W. Simpson2

Abstract:

Despite advances in computer capacity, the enormous computational cost of running complex engineering simulations makes it impractical to rely exclusively on simulation for the purpose of design optimization. To cut down the cost, surrogate models, also known as metamodels, are constructed from and then used in place of the a... Despite advances in computer capacity, the enormous computational cost of running complex engineering simulations makes it impractical to rely exclusively on simulation for the purpose of design optimization. To cut down the cost, surrogate models, also known as metamodels, are constructed from and then used in place of the actual simulation models. In this paper, we systematically compare four popular metamodelling techniques – polynomial regression, multivariate adaptive regression splines, radial basis functions, and kriging – based on multiple performance criteria using fourteen test problems representing different classes of problems. Our objective in this study is to investigate the advantages and disadvantages of these four metamodelling techniques using multiple criteria and multiple test problems rather than a single measure of merit and a single test problem. read more read less

Topics:

Multivariate adaptive regression splines (54%)54% related to the paper, Polynomial regression (51%)51% related to the paper
1,567 Citations
open accessOpen access Journal Article DOI: 10.1007/S00158-006-0087-X
Morphology-based black and white filters for topology optimization
Ole Sigmund1

Abstract:

To ensure manufacturability and mesh independence in density-based topology optimization schemes, it is imperative to use restriction methods. This paper introduces a new class of morphology-based restriction schemes that work as density filters; that is, the physical stiffness of an element is based on a function of the desi... To ensure manufacturability and mesh independence in density-based topology optimization schemes, it is imperative to use restriction methods. This paper introduces a new class of morphology-based restriction schemes that work as density filters; that is, the physical stiffness of an element is based on a function of the design variables of the neighboring elements. The new filters have the advantage that they eliminate grey scale transitions between solid and void regions. Using different test examples, it is shown that the schemes, in general, provide black and white designs with minimum length-scale constraints on either or both minimum hole sizes and minimum structural feature sizes. The new schemes are compared with methods and modified methods found in the literature. read more read less

Topics:

Topology optimization (56%)56% related to the paper
View PDF
1,305 Citations
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Structural and Multidisciplinary Optimization format uses SPBASIC citation style.

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3. Can I cite my article in multiple styles in Structural and Multidisciplinary 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 Structural and Multidisciplinary Optimization citation style.

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12. Is Structural and Multidisciplinary 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 Structural and Multidisciplinary 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 Structural and Multidisciplinary 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 Structural and Multidisciplinary Optimization?

The 5 most common citation types in order of usage for Structural and Multidisciplinary Optimization are:.

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

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16. Can I download Structural and Multidisciplinary 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 Structural and Multidisciplinary Optimization Endnote style according to Elsevier guidelines.

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