[AISWorld] Abstract Announcement for International Journal of Swarm Intelligence Research (IJSIR) 5(2)

[Yuhui Shi]

[Apologies if you have received multiple copies of this Abstract Announcement; please forward to appropriate communities]
The contents of the latest issue of:
International Journal of Swarm Intelligence Research (IJSIR)
Volume 5, Issue 2, April - June 2014
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

Diversity and Mechanisms in Swarm Intelligence

Xin-She Yang (School of Science and Technology, Middlesex University London, London, UK)

Swarm intelligence based algorithms such as particle swarm optimization have become popular in the last two decades. Various new algorithms such as cuckoo search and bat algorithm also show promising efficiency. In all these algorithms, it is essential to maintain the balance of exploration and exploitation by controlling directly and indirectly the diversity of the population. Different algorithms may use different mechanisms to control such diversity. In this review paper, the author reviews and analyzes the roles of diversity and relevant mechanisms in swarm intelligence. The author also discuss parameter tuning and parameter control. In addition, the author highlights some key open questions in swarm intelligence.

To obtain a copy of the entire article, click on the link below.
www.igi-global.com/article/diversity-and-mechanisms-in-swarm-intelligence/122371<http://www.igi-global.com/article/diversity-and-mechanisms-in-swarm-intelligence/122371>

To read a PDF sample of this article, click on the link below.
www.igi-global.com/viewtitlesample.aspx?id=122371<http://www.igi-global.com/viewtitlesample.aspx?id=122371>

ARTICLE 2

An Improved PSO with Small-World Topology and Comprehensive Learning

Yanmin Liu (School of Mathematics and Computer Science, Zunyi Normal College, Zunyi, China), Ben Niu (School of Economics and Management, Tongji University, Shanghai, China)

Particle swarm optimization (PSO) is a heuristic global optimization method based on swarm intelligence, and has been proven to be a powerful competitor to other intelligent algorithms. However, PSO may easily get trapped in a local optimum when solving complex multimodal problems. To improve PSO's performance, in this paper the authors propose an improved PSO based on small world network and comprehensive learning strategy (SCPSO for short), in which the learning exemplar of each particle includes three parts: the global best particle (gbest), personal best particle (pbest), and the pbest of its neighborhood. Additionally, a random position around a particle is used to increase its probability to jump to a promising region. These strategies enable the diversity of the swarm to discourage premature convergence. By testing on five benchmark functions, SCPSO is proved to have better performance than PSO and its variants. SCPSO is then used to determine the optimal parameters involved in the Van-Genuchten model. The experimental results demonstrate the good performance of SCPSO compared with other methods.

To obtain a copy of the entire article, click on the link below.
www.igi-global.com/article/an-improved-pso-with-small-world-topology-and-comprehensive-learning/122372<http://www.igi-global.com/article/an-improved-pso-with-small-world-topology-and-comprehensive-learning/122372>

To read a PDF sample of this article, click on the link below.
www.igi-global.com/viewtitlesample.aspx?id=122372<http://www.igi-global.com/viewtitlesample.aspx?id=122372>

ARTICLE 3

Design of Optimal CMOS Inverter for Symmetric Switching Characteristics Using Firefly Algorithm with Wavelet Mutation

Bishnu Prasad De (Department of Electronics and Communication Engineering, NIT Durgapur, Durgapur, India), Rajib Kar (Department of Electronics and Communication Engineering, NIT Durgapur, Durgapur, India), Durbadal Mandal (Department of Electronics and Communication Engineering, NIT Durgapur, Durgapur, India), Sakti Prasad Ghoshal (Department of Electrical Engineering, NIT Durgapur, Durgapur, India)

In this article, a population based meta-heuristic search method called Firefly Algorithm with Wavelet Mutation (FAWM) is applied for the optimal switching characterization of CMOS inverter. In Firefly Algorithm (FA), behaviour of flashing firefly towards its competent mate is structured. In this algorithm attractiveness depends on brightness of light and brighter fireflies are considered as more attractive among the population. For the present minimization based optimization problem, brightness varies inversely proportional to the error fitness value, so the position of the brightest firefly gives the optimum result corresponding to the least error fitness in multidimensional search space. FAWM incorporates a new definition of swarm updating with the help of wavelet mutation based on wavelet theory. Wavelet mutation enhances the FA to explore the solution space more effectively compared with the other optimization methods. The performance of FAWM is compared with real coded genetic algorithm (RGA), and conventional PSO reported in the literature. FAWM based design results are also compared with the PSPICE results. The comparative simulation results establish the FAWM as a more competent optimization algorithm to other aforementioned evolutionary algorithms for the examples considered and can be efficiently used for CMOS inverter design.

To obtain a copy of the entire article, click on the link below.
www.igi-global.com/article/design-of-optimal-cmos-inverter-for-symmetric-switching-characteristics-using-firefly-algorithm-with-wavelet-mutation/122373<http://www.igi-global.com/article/design-of-optimal-cmos-inverter-for-symmetric-switching-characteristics-using-firefly-algorithm-with-wavelet-mutation/122373>

To read a PDF sample of this article, click on the link below.
www.igi-global.com/viewtitlesample.aspx?id=122373<http://www.igi-global.com/viewtitlesample.aspx?id=122373>

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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>

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