The Scientific World Journal

Swarm Intelligence and Its Applications


Publishing date
12 Jul 2013
Status
Published
Submission deadline
03 May 2013

1Brain Image Processing, Columbia University, New York, NY, USA

2Anand International College of Engineering, Near Kanota, Agra Road, Jaipur, India

3Ambedkar Institute of Advanced Communication Technologies and Research, New Delhi, India

4Department of Electrical Engineering, Islamic Azad University, Gonabad Branch, Gonabad, Iran

5Suzhou University, Suzhou, China


Swarm Intelligence and Its Applications

Description

Swarm intelligence (SI) is the collective behavior of decentralized, self-organized systems, natural or artificial. SI systems are typically made up of a population of simple agents or boids interacting locally with one another and with their environment. The inspiration often comes from nature, especially biological systems. The agents follow very simple rules, and although there is no centralized control structure dictating how individual agents should behave, local, and to a certain degree random, interactions between such agents lead to the emergence of “intelligent” global behavior, unknown to the individual agents. Natural examples of SI include ant colonies, bird flocking, animal herding, bacterial growth, and fish schooling.

Recently, SI algorithms have attracted close attention of researchers and have also been applied successfully to solve optimization problems in engineering. Nevertheless, for large and complex problems, SI algorithms consume considerable computation time due to stochastic feature of the search approaches. Therefore, there is a potential requirement to develop efficient algorithm to find solutions under the limited resources, time, and money in real-world applications.

The aim of this special issue is to highlight the most significant recent developments on the topics of SI and to apply SI algorithms in real-life scenario. Contributions containing new insights and findings in this field are welcome. Particular attention will be given to the following theme areas; however, it should be stressed that a broad range of submissions are encouraged. We invite authors to contribute with original research articles as well as review articles to this special issue. Potential topics include, but are not limited to:

  • Convergence proof for SI algorithms
  • Benchmarking and evaluation of new SI algorithms
  • Comparative theoretical and empirical studies on SI algorithms (e.g., artificial bee optimization, ant colony optimization, artificial fish algorithm, artificial immune system, bat algorithm, bacterial foraging optimization, cuckoo search, firefly algorithm, intelligent water drops, magnetic optimization algorithm, and particle swarm optimization)
  • SI algorithms for real-world applications (e.g., aerospace engineering, bioengineering, chemical engineering, computer engineering, electrical engineering, image processing, industrial engineering and manufacturing systems, mechanical engineering, signal processing, etc.)

Before submission authors should carefully read over the journal's Author Guidelines, which are located at http://www.hindawi.com/journals/tswj/guidelines/. Prospective authors should submit an electronic copy of their complete manuscript through the journal Manuscript Tracking System at http://mts.hindawi.com/submit/journals/tswj/computer.science/sia/ according to the following timetable:


Articles

  • Special Issue
  • - Volume 2013
  • - Article ID 528069
  • - Editorial

Swarm Intelligence and Its Applications

Yudong Zhang | Praveen Agarwal | ... | Jie Yan
  • Special Issue
  • - Volume 2013
  • - Article ID 636484
  • - Research Article

Discrete Particle Swarm Optimization with Scout Particles for Library Materials Acquisition

Yi-Ling Wu | Tsu-Feng Ho | ... | Bertrand M. T. Lin
  • Special Issue
  • - Volume 2013
  • - Article ID 547656
  • - Research Article

A Multiuser Detector Based on Artificial Bee Colony Algorithm for DS-UWB Systems

Zhendong Yin | Xiaohui Liu | Zhilu Wu
  • Special Issue
  • - Volume 2013
  • - Article ID 419187
  • - Research Article

Feature Selection Method Based on Artificial Bee Colony Algorithm and Support Vector Machines for Medical Datasets Classification

Mustafa Serter Uzer | Nihat Yilmaz | Onur Inan
  • Special Issue
  • - Volume 2013
  • - Article ID 160687
  • - Research Article

QPSO-Based Adaptive DNA Computing Algorithm

Mehmet Karakose | Ugur Cigdem
  • Special Issue
  • - Volume 2013
  • - Article ID 370172
  • - Research Article

An Improved Marriage in Honey Bees Optimization Algorithm for Single Objective Unconstrained Optimization

Yuksel Celik | Erkan Ulker
  • Special Issue
  • - Volume 2013
  • - Article ID 805343
  • - Research Article

Immunity-Based Optimal Estimation Approach for a New Real Time Group Elevator Dynamic Control Application for Energy and Time Saving

Mehmet Baygin | Mehmet Karakose
  • Special Issue
  • - Volume 2013
  • - Article ID 581846
  • - Research Article

Reinforcement Learning Based Artificial Immune Classifier

Mehmet Karakose
  • Special Issue
  • - Volume 2013
  • - Article ID 969734
  • - Research Article

An Adaptive Cauchy Differential Evolution Algorithm for Global Numerical Optimization

Tae Jong Choi | Chang Wook Ahn | Jinung An
  • Special Issue
  • - Volume 2013
  • - Article ID 718345
  • - Research Article

Application of Particle Swarm Optimization Algorithm in the Heating System Planning Problem

Rong-Jiang Ma | Nan-Yang Yu | Jun-Yi Hu
The Scientific World Journal
 Journal metrics
Acceptance rate17%
Submission to final decision84 days
Acceptance to publication37 days
CiteScore1.430
Impact Factor-
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