Example of Swarm Intelligence format
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Example of Swarm Intelligence format Example of Swarm Intelligence format Example of Swarm Intelligence format Example of Swarm Intelligence format Example of Swarm Intelligence format Example of Swarm Intelligence format Example of Swarm Intelligence format Example of Swarm Intelligence format Example of Swarm Intelligence format Example of Swarm Intelligence format Example of Swarm Intelligence format Example of Swarm Intelligence format Example of Swarm Intelligence format Example of Swarm Intelligence format Example of Swarm Intelligence format Example of Swarm Intelligence format Example of Swarm Intelligence format Example of Swarm Intelligence format
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Example of Swarm Intelligence format Example of Swarm Intelligence format Example of Swarm Intelligence format Example of Swarm Intelligence format Example of Swarm Intelligence format Example of Swarm Intelligence format Example of Swarm Intelligence format Example of Swarm Intelligence format Example of Swarm Intelligence format Example of Swarm Intelligence format Example of Swarm Intelligence format Example of Swarm Intelligence format Example of Swarm Intelligence format Example of Swarm Intelligence format Example of Swarm Intelligence format Example of Swarm Intelligence format Example of Swarm Intelligence format Example of Swarm Intelligence format
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open access Open Access

Swarm Intelligence — Template for authors

Publisher: Springer
Categories Rank Trend in last 3 yrs
Artificial Intelligence #66 of 227 down down by 39 ranks
journal-quality-icon Journal quality:
Good
calendar-icon Last 4 years overview: 51 Published Papers | 303 Citations
indexed-in-icon Indexed in: Scopus
last-updated-icon Last updated: 05/06/2020
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Related Journals

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IEEE

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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.

2.556

16% from 2018

Impact factor for Swarm Intelligence from 2016 - 2019
Year Value
2019 2.556
2018 2.208
2017 1.52
2016 3.115
graph view Graph view
table view Table view

5.9

8% from 2019

CiteRatio for Swarm Intelligence from 2016 - 2020
Year Value
2020 5.9
2019 6.4
2018 5.9
2017 5.8
2016 8.8
graph view Graph view
table view Table view

insights Insights

  • Impact factor of this journal has increased by 16% in last year.
  • This journal’s impact factor is in the top 10 percentile category.

insights Insights

  • CiteRatio of this journal has decreased by 8% 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.

0.455

48% from 2019

SJR for Swarm Intelligence from 2016 - 2020
Year Value
2020 0.455
2019 0.881
2018 0.511
2017 0.709
2016 1.364
graph view Graph view
table view Table view

1.377

38% from 2019

SNIP for Swarm Intelligence from 2016 - 2020
Year Value
2020 1.377
2019 2.206
2018 2.161
2017 1.54
2016 2.885
graph view Graph view
table view Table view

insights Insights

  • SJR of this journal has decreased by 48% in last years.
  • This journal’s SJR is in the top 10 percentile category.

insights Insights

  • SNIP of this journal has decreased by 38% in last years.
  • This journal’s SNIP is in the top 10 percentile category.

Swarm Intelligence

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Springer

Swarm Intelligence

Swarm Intelligence is the principal peer-reviewed publication dedicated to reporting on research and developments in the multidisciplinary field of swarm intelligence. The journal publishes original research articles and occasional review articles on theoretical, experimental ...... Read More

Artificial Intelligence

Computer Science

i
Last updated on
05 Jun 2020
i
ISSN
1935-3812
i
Impact Factor
High - 1.694
i
Open Access
No
i
Sherpa RoMEO Archiving Policy
Green faq
i
Plagiarism Check
Available via Turnitin
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Endnote Style
Download Available
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Bibliography Name
SPBASIC
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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/S11721-007-0002-0
Particle swarm optimization
James Kennedy, Russell C. Eberhart1
01 Jan 1995 - Swarm Intelligence

Abstract:

A concept for the optimization of nonlinear functions using particle swarm methodology is introduced The evolution of several paradigms is outlined, and an implementation of one of the paradigms is discussed Benchmark testing of the paradigm is described, and applications, including nonlinear function optimization and neural ... A concept for the optimization of nonlinear functions using particle swarm methodology is introduced The evolution of several paradigms is outlined, and an implementation of one of the paradigms is discussed Benchmark testing of the paradigm is described, and applications, including nonlinear function optimization and neural network training, are proposed The relationships between particle swarm optimization and both artificial life and genetic algorithms are described read more read less

Topics:

Multi-swarm optimization (77%)77% related to the paper, Metaheuristic (69%)69% related to the paper, Particle swarm optimization (65%)65% related to the paper, Swarm intelligence (61%)61% related to the paper, Artificial neural network (51%)51% related to the paper
View PDF
18,439 Citations
open accessOpen access Journal Article DOI: 10.1007/S11721-012-0075-2
Swarm robotics: a review from the swarm engineering perspective
Manuele Brambilla1, Eliseo Ferrante1, Mauro Birattari1, Marco Dorigo1
17 Jan 2013 - Swarm Intelligence

Abstract:

Swarm robotics is an approach to collective robotics that takes inspiration from the self-organized behaviors of social animals. Through simple rules and local interactions, swarm robotics aims at designing robust, scalable, and flexible collective behaviors for the coordination of large numbers of robots. In this paper, we a... Swarm robotics is an approach to collective robotics that takes inspiration from the self-organized behaviors of social animals. Through simple rules and local interactions, swarm robotics aims at designing robust, scalable, and flexible collective behaviors for the coordination of large numbers of robots. In this paper, we analyze the literature from the point of view of swarm engineering: we focus mainly on ideas and concepts that contribute to the advancement of swarm robotics as an engineering field and that could be relevant to tackle real-world applications. Swarm engineering is an emerging discipline that aims at defining systematic and well founded procedures for modeling, designing, realizing, verifying, validating, operating, and maintaining a swarm robotics system. We propose two taxonomies: in the first taxonomy, we classify works that deal with design and analysis methods; in the second taxonomy, we classify works according to the collective behavior studied. We conclude with a discussion of the current limits of swarm robotics as an engineering discipline and with suggestions for future research directions. read more read less

Topics:

Swarm robotics (78%)78% related to the paper, Ant robotics (66%)66% related to the paper, Swarm behaviour (64%)64% related to the paper, Robot (56%)56% related to the paper, Robotics (54%)54% related to the paper
View PDF
1,405 Citations
Journal Article DOI: 10.1007/S11721-007-0004-Y
The biological principles of swarm intelligence
Simon Garnier1, Jacques Gautrais1, Guy Theraulaz1
17 Jul 2007 - Swarm Intelligence

Abstract:

The roots of swarm intelligence are deeply embedded in the biological study of self-organized behaviors in social insects. From the routing of traffic in telecommunication networks to the design of control algorithms for groups of autonomous robots, the collective behaviors of these animals have inspired many of the foundatio... The roots of swarm intelligence are deeply embedded in the biological study of self-organized behaviors in social insects. From the routing of traffic in telecommunication networks to the design of control algorithms for groups of autonomous robots, the collective behaviors of these animals have inspired many of the foundational works in this emerging research field. For the first issue of this journal dedicated to swarm intelligence, we review the main biological principles that underlie the organization of insects’ colonies. We begin with some reminders about the decentralized nature of such systems and we describe the underlying mechanisms of complex collective behaviors of social insects, from the concept of stigmergy to the theory of self-organization in biological systems. We emphasize in particular the role of interactions and the importance of bifurcations that appear in the collective output of the colony when some of the system’s parameters change. We then propose to categorize the collective behaviors displayed by insect colonies according to four functions that emerge at the level of the colony and that organize its global behavior. Finally, we address the role of modulations of individual behaviors by disturbances (either environmental or internal to the colony) in the overall flexibility of insect colonies. We conclude that future studies about self-organized biological behaviors should investigate such modulations to better understand how insect colonies adapt to uncertain worlds. read more read less

Topics:

Swarm robotics (60%)60% related to the paper, Swarm intelligence (56%)56% related to the paper, Stigmergy (54%)54% related to the paper
502 Citations
open accessOpen access Journal Article DOI: 10.1007/S11721-012-0072-5
ARGoS: a modular, parallel, multi-engine simulator for multi-robot systems
16 Nov 2012 - Swarm Intelligence

Abstract:

We present a novel multi-robot simulator named ARGoS. ARGoS is designed to simulate complex experiments involving large swarms of robots of different types. ARGoS is the first multi-robot simulator that is at the same time both efficient (fast performance with many robots) and flexible (highly customizable for specific experi... We present a novel multi-robot simulator named ARGoS. ARGoS is designed to simulate complex experiments involving large swarms of robots of different types. ARGoS is the first multi-robot simulator that is at the same time both efficient (fast performance with many robots) and flexible (highly customizable for specific experiments). Novel design choices in ARGoS have enabled this breakthrough. First, in ARGoS, it is possible to partition the simulated space into multiple sub-spaces, managed by different physics engines running in parallel. Second, ARGoS’ architecture is multi-threaded, thus designed to optimize the usage of modern multi-core CPUs. Finally, the architecture of ARGoS is highly modular, enabling easy addition of custom features and appropriate allocation of computational resources. We assess the efficiency of ARGoS and showcase its flexibility with targeted experiments. Experimental results demonstrate that simulation run-time increases linearly with the number of robots. A 2D-dynamics simulation of 10,000 e-puck robots can be performed in 60 % of the time taken by the corresponding real-world experiment. We show how ARGoS can be extended to suit the needs of an experiment in which custom functionality is necessary to achieve sufficient simulation accuracy. ARGoS is open source software licensed under GPL3 and is downloadable free of charge. read more read less

Topics:

Modular design (50%)50% related to the paper
View PDF
486 Citations
Journal Article DOI: 10.1007/S11721-008-0021-5
Glowworm swarm optimization for simultaneous capture of multiple local optima of multimodal functions
K. N. Krishnanand1, Debasish Ghose1
01 Jun 2009 - Swarm Intelligence

Abstract:

This paper presents glowworm swarm optimization (GSO), a novel algorithm for the simultaneous computation of multiple optima of multimodal functions. The algorithm shares a few features with some better known swarm intelligence based optimization algorithms, such as ant colony optimization and particle swarm optimization, but... This paper presents glowworm swarm optimization (GSO), a novel algorithm for the simultaneous computation of multiple optima of multimodal functions. The algorithm shares a few features with some better known swarm intelligence based optimization algorithms, such as ant colony optimization and particle swarm optimization, but with several significant differences. The agents in GSO are thought of as glowworms that carry a luminescence quantity called luciferin along with them. The glowworms encode the fitness of their current locations, evaluated using the objective function, into a luciferin value that they broadcast to their neighbors. The glowworm identifies its neighbors and computes its movements by exploiting an adaptive neighborhood, which is bounded above by its sensor range. Each glowworm selects, using a probabilistic mechanism, a neighbor that has a luciferin value higher than its own and moves toward it. These movements—based only on local information and selective neighbor interactions—enable the swarm of glowworms to partition into disjoint subgroups that converge on multiple optima of a given multimodal function. We provide some theoretical results related to the luciferin update mechanism in order to prove the bounded nature and convergence of luciferin levels of the glowworms. Experimental results demonstrate the efficacy of the proposed glowworm based algorithm in capturing multiple optima of a series of standard multimodal test functions and more complex ones, such as stair-case and multiple-plateau functions. We also report the results of tests in higher dimensional spaces with a large number of peaks. We address the parameter selection problem by conducting experiments to show that only two parameters need to be selected by the user. Finally, we provide some comparisons of GSO with PSO and an experimental comparison with Niche-PSO, a PSO variant that is designed for the simultaneous computation of multiple optima. read more read less

Topics:

Glowworm swarm optimization (79%)79% related to the paper, Multi-swarm optimization (62%)62% related to the paper, Metaheuristic (57%)57% related to the paper, Swarm intelligence (55%)55% related to the paper, Particle swarm optimization (55%)55% related to the paper
413 Citations
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Swarm Intelligence format uses SPBASIC citation style.

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Frequently asked questions

1. Can I write Swarm Intelligence in LaTeX?

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

2. Do you follow the Swarm Intelligence guidelines?

Yes, the template is compliant with the Swarm Intelligence 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 Swarm Intelligence?

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 Swarm Intelligence citation style.

4. Can I use the Swarm Intelligence 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 Swarm Intelligence.

5. Can I use a manuscript in Swarm Intelligence 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 Swarm Intelligence that you can download at the end.

6. How long does it usually take you to format my papers in Swarm Intelligence?

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

7. Where can I find the template for the Swarm Intelligence?

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 Swarm Intelligence'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 Swarm Intelligence'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. Swarm Intelligence an online tool or is there a desktop version?

SciSpace's Swarm Intelligence 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 Swarm Intelligence?

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 Swarm Intelligence?”

11. What is the output that I would get after using Swarm Intelligence?

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

12. Is Swarm Intelligence'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 Swarm Intelligence?

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 Swarm Intelligence. 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 Swarm Intelligence?

The 5 most common citation types in order of usage for Swarm Intelligence 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 Swarm Intelligence?

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 Swarm Intelligence's guidelines and download the same in Word, PDF and LaTeX formats? Give us a try!.

16. Can I download Swarm Intelligence 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 Swarm Intelligence 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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