[AISWorld] Abstract Announcement for International Journal of Swarm Intelligence Research (IJSIR) 7(1)

[Yuhui Shi]

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The contents of the latest issue of:
International Journal of Swarm Intelligence Research (IJSIR)
Volume 7, Issue 1, January - March 2016
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

Cognitive Bare Bones Particle Swarm Optimisation with Jumps

Mohammad Majid al-Rifaie (Department of Computing, Goldsmiths, University of London, London, UK), Tim Blackwell (Department of Computing Goldsmiths, University of London, London, UK)

The 'bare bones' (BB) formulation of particle swarm optimisation (PSO) was originally advanced as a model of PSO dynamics. The idea was to model the forces between particles with sampling from a probability distribution in the hope of understanding swarm behaviour with a conceptually simpler particle update rule. 'Bare bones with jumps' (BBJ) proposes three significant extensions to the BB algorithm: (i) two social neighbourhoods, (ii) a tuneable parameter that can advantageously bring the swarm to the 'edge of collapse' and (iii) a component-by-component probabilistic jump to anywhere in the search space. The purpose of this paper is to investigate the role of jumping within a specific BBJ algorithm, cognitive BBJ (cBBJ). After confirming the effectiveness of cBBJ, this paper finds that: jumping in one component only is optimal over the 30 dimensional benchmarks of this study; that a small per particle jump probability of 1/30 works well for these benchmarks; jumps are chiefly beneficial during the early stages of optimisation and finally this work supplies evidence that jumping provides escape from regions surrounding sub-optimal minima.

To obtain a copy of the entire article, click on the link below.
www.igi-global.com/article/cognitive-bare-bones-particle-swarm-optimisation-with-jumps/144240<http://www.igi-global.com/article/cognitive-bare-bones-particle-swarm-optimisation-with-jumps/144240>

To read a PDF sample of this article, click on the link below.
www.igi-global.com/viewtitlesample.aspx?id=144240<http://www.igi-global.com/viewtitlesample.aspx?id=144240>

ARTICLE 2

A Scalable MapReduce-enabled Glowworm Swarm Optimization Approach for High Dimensional Multimodal Functions

Ibrahim Aljarah (Department of Business Information Technology, The University of Jordan, Amman, Jordan), Simone A. Ludwig (Department of Computer Science, North Dakota State University, Fargo, ND, USA)

Glowworm Swarm Optimization (GSO) is one of the common swarm intelligence algorithms, where GSO has the ability to optimize multimodal functions efficiently. In this paper, a parallel MapReduce-based GSO algorithm is proposed to speedup the GSO optimization process. The authors argue that GSO can be formulated based on the MapReduce parallel programming model quite naturally. In addition, they use higher dimensional multimodal benchmark functions for evaluating the proposed algorithm. The experimental results show that the proposed algorithm is appropriate for optimizing difficult multimodal functions with higher dimensions and achieving high peak capture rates. Furthermore, a scalability analysis shows that the proposed algorithm scales very well with increasing swarm sizes. In addition, an overhead of the Hadoop infrastructure is investigated to find if there is any relationship between the overhead, the swarm size, and number of nodes used.

To obtain a copy of the entire article, click on the link below.
www.igi-global.com/article/a-scalable-mapreduce-enabled-glowworm-swarm-optimization-approach-for-high-dimensional-multimodal-functions/144241<http://www.igi-global.com/article/a-scalable-mapreduce-enabled-glowworm-swarm-optimization-approach-for-high-dimensional-multimodal-functions/144241>

To read a PDF sample of this article, click on the link below.
www.igi-global.com/viewtitlesample.aspx?id=144241<http://www.igi-global.com/viewtitlesample.aspx?id=144241>

ARTICLE 3

Multi-Objective Optimization of Squeeze Casting Process using Evolutionary Algorithms

Manjunath Patel G C (Department of Mechanical Engineering, National Institute of Technology Karnataka, Surathkal, India), Prasad Krishna (Department of Mechanical Engineering, National Institute of Technology Karnataka, Surathkal, India), Mahesh B. Parappagoudar (Department of Mechanical Engineering, Chhatrapati Shivaji Institute of Technology, Bhilai, India), Pandu Ranga Vundavilli (School of Mechanical Sciences, Indian Institute of Technology, Bhubneswar, India)

The present work focuses on determining optimum squeeze casting process parameters using evolutionary algorithms. Evolutionary algorithms, such as genetic algorithm, particle swarm optimization, and multi objective particle swarm optimization based on crowing distance mechanism, have been used to determine the process variable combinations for the multiple objective functions. In multi-objective optimization, there are no single optimal process variable combination due to conflicting nature of objective functions. Four cases have been considered after assigning different combination of weights to the individual objective function based on the user importance. Confirmation tests have been conducted for the recommended process variable combinations obtained by genetic algorithm (GA), particle swarm optimization (PSO), and multiple objective particle swarm optimization based on crowing distance (MOPSO-CD). The performance of PSO is found to be comparable with that of GA for identifying optimal process variable combinations. However, PSO outperformed GA with regard to computation time.

To obtain a copy of the entire article, click on the link below.
www.igi-global.com/article/multi-objective-optimization-of-squeeze-casting-process-using-evolutionary-algorithms/144242<http://www.igi-global.com/article/multi-objective-optimization-of-squeeze-casting-process-using-evolutionary-algorithms/144242>

To read a PDF sample of this article, click on the link below.
www.igi-global.com/viewtitlesample.aspx?id=144242<http://www.igi-global.com/viewtitlesample.aspx?id=144242>

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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>