[AISWorld] Abstract Announcement for International Journal of Swarm Intelligence Research (IJSIR) 6(4)

[Yuhui Shi]

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The contents of the latest issue of:
International Journal of Swarm Intelligence Research (IJSIR)
Volume 6, Issue 4, October - December 2015
Published: Quarterly in Print and Electronically
ISSN: 1947-9263; EISSN: 1947-9271;
Published by IGI Global Publishing, Hershey, USA
www.igi-global.com/ijsir<http://www.igi-global.com/journal/international-journal-swarm-intelligence-research/1149>

Editor(s)-in-Chief: Yuhui Shi (Xi'an Jiaotong-Liverpool University, China)
Note: There are no submission or acceptance fees for manuscripts submitted to the International Journal of Swarm Intelligence Research (IJSIR). All manuscripts are accepted based on a double-blind peer review editorial process.

ARTICLE 1

Dynamic Particle Swarm Optimization with Any Irregular Initial Small-World Topology

Shuangxin Wang (School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing, China), Guibin Tian (School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing, China), Dingli Yu (School of Engineering, Liverpool John Moores University, Liverpool, UK), Yijiang Lin (School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing, China)

It is realized that the topological structure of the particle swarm optimization (PSO) algorithm has a great influence on its optimization ability. This paper presents a new dynamic small-world neighborhood PSO (D-SWPSO) algorithm whose neighbourhood structure can be constructed with any irregular initial networks. The choice of the learning exemplar is not only based upon the big clustering coefficient and the average shortest distance for a regular network, but also based upon the eigenvalues of Laplacian matrix for irregular networks. Therefore, the D-SWPSO is a PSO algorithm based on small-world topological neighbourhood with universal significance. The proposed algorithm is tested by some typical benchmark test functions, and the results confirm that there is a significant improvement over the basic PSO algorithm. Finally, the algorithm is applied to a real-world optimization problem, the economic dispatch on the IEEE30 system with wind farms. The results demonstrate that the proposed D-SWPSO is a practically feasible and effective algorithm.

To obtain a copy of the entire article, click on the link below.
www.igi-global.com/article/dynamic-particle-swarm-optimization-with-any-irregular-initial-small-world-topology/137085<http://www.igi-global.com/article/dynamic-particle-swarm-optimization-with-any-irregular-initial-small-world-topology/137085>

To read a PDF sample of this article, click on the link below.
www.igi-global.com/viewtitlesample.aspx?id=137085<http://www.igi-global.com/viewtitlesample.aspx?id=137085>

ARTICLE 2

A New Swarm Intelligence Technique of Artificial Haemostasis System for Suspicious Person Detection with Visual Result Mining

Hadj Ahmed Bouarara (GeCode Laboratory, Tahar Moulay University of Saida Algeria, Saida, Algeria), Reda Mohamed Hamou (GeCode Laboratory,Tahar Moulay University of Saida Algeria, Saida, Algeria), Abdelmalek Amine (GeCode Laboratory, Tahar Moulay University of Saida Algeria, Saida, Algeria)

In the last few years, the video surveillance system is ubiquitous and can be found in many sectors (banking, transport, industry) or living areas (cities, office building, and store). Unfortunately, this technology has several drawbacks such as the violation of individual freedom and the inability to prevent malicious acts (stealing, crime, and terrorist attack ... etc.). The authors' work deals on the development of a new video surveillance system to detect suspicious person based on their gestures instead of their faces, using a new artificial haemostasis system composed of four steps: pre-processing (pre-haemostasis) for digitalization of images using a novel technique of representation called n-gram pixel, and the weighting normalized term frequency; Each image vector passes through three filters: primary detection (primary haemostasis), secondary detection (secondary haemostasis) and the final detection (fibrinolysis), with an identification step (plasminogen activation) to evaluate the malicious degree of the person presents in this image; the results obtained by their system are promising compared to the performance of classical machine learning algorithms (C4.5 and KNN). The authors' system is composed of a visualization tool in order to see the results with more realism using the functionality of zooming and rotating. Their objectives are to help the justice in its investigations and ensure the safety of people and nation.

To obtain a copy of the entire article, click on the link below.
www.igi-global.com/article/a-new-swarm-intelligence-technique-of-artificial-haemostasis-system-for-suspicious-person-detection-with-visual-result-mining/137086<http://www.igi-global.com/article/a-new-swarm-intelligence-technique-of-artificial-haemostasis-system-for-suspicious-person-detection-with-visual-result-mining/137086>

To read a PDF sample of this article, click on the link below.
www.igi-global.com/viewtitlesample.aspx?id=137086<http://www.igi-global.com/viewtitlesample.aspx?id=137086>

ARTICLE 3

Two Diverse Swarm Intelligence Techniques for Supervised Learning

Tad Gonsalves (Department of Information and Communication Sciences, Sophia University, Tokyo, Japan)

Particle Swarm Optimization (PSO) and Enhanced Fireworks Algorithm (EFWA) are two diverse optimization techniques of the Swarm Intelligence paradigm. The inspiration of the former comes from animate swarms like those of birds and fish efficiently hunting for prey, while that of the latter comes from inanimate swarms like those of fireworks illuminating the night sky. This novel study, aimed at extending the application of these two Swarm Intelligence techniques to supervised learning, compares and contrasts their performance in training a neural network to perform the task of classification on datasets. Both the techniques are found to be speedy and successful in training the neural networks. Further, their prediction accuracy is also found to be high. Except in the case of two datasets, the training and prediction accuracies of the Enhanced Fireworks Algorithm driven neural net are found to be superior to those of the Particle Swarm Optimization driven neural net.

To obtain a copy of the entire article, click on the link below.
www.igi-global.com/article/two-diverse-swarm-intelligence-techniques-for-supervised-learning/137087<http://www.igi-global.com/article/two-diverse-swarm-intelligence-techniques-for-supervised-learning/137087>

To read a PDF sample of this article, click on the link below.
www.igi-global.com/viewtitlesample.aspx?id=137087<http://www.igi-global.com/viewtitlesample.aspx?id=137087>

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For full copies of the above articles, check for this issue of the International Journal of Swarm Intelligence Research (IJSIR) in your institution's library. This journal is also included in the IGI Global aggregated "InfoSci-Journals" database: www.igi-global.com/isj<http://www.igi-global.com/e-resources/infosci-databases/infosci-journals/>.
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CALL FOR PAPERS

Mission of IJSIR:

The mission of the International Journal of Swarm Intelligence Research (IJSIR) is to become a leading international and well-referred journal in swarm intelligence, nature-inspired optimization algorithms, and their applications. This journal publishes original and previously unpublished articles including research papers, survey papers, and application papers, to serve as a platform for facilitating and enhancing the information shared among researchers in swarm intelligence research areas ranging from algorithm developments to real-world applications.

Indices of IJSIR:

  *   ACM Digital Library
  *   Bacon's Media Directory
  *   DBLP
  *   Google Scholar
  *   INSPEC
  *   JournalTOCs
  *   MediaFinder
  *   The Standard Periodical Directory
  *   Ulrich's Periodicals Directory

Coverage of IJSIR:

Topics to be discussed in this journal include (but are not limited to) the following:

  *   Ant colony optimization
  *   Applications in bioengineering
  *   Applications in bioinformatics
  *   Applications in business
  *   Applications in control systems
  *   Applications in data mining and data clustering
  *   Applications in decision making
  *   Applications in distributed computing
  *   Applications in evolvable hardware
  *   Applications in finance and economics
  *   Applications in games
  *   Applications in graph partitioning
  *   Applications in information security
  *   Applications in machine learning
  *   Applications in planning and operations in industrial systems, transportation systems, and other systems
  *   Applications in power system
  *   Applications in supply-chain management
  *   Applications in wireless sensor networks
  *   Artificial immune system
  *   Constrained optimization
  *   Culture algorithm
  *   Differential Evolution
  *   Foraging algorithm
  *   Large scale optimization problems
  *   Modeling and analysis of biological collective systems such as social insects colonies, school, and flocking vertebrates
  *   Multi-objective optimization
  *   Optimization in dynamic and uncertain environment
  *   Particle swarm optimization
  *   Scheduling and timetabling
  *   Swarm robotics
  *   Other nature-inspired optimization algorithms

Interested authors should consult the journal's manuscript submission guidelines www.igi-global.com/calls-for-papers/international-journal-swarm-intelligence-research/1149<http://www.igi-global.com/calls-for-papers/international-journal-swarm-intelligence-research/1149>