[AISWorld] Abstract Announcement for International Journal of Swarm Intelligence Research (IJSIR) 6(1)

[Yuhui Shi]

The contents of the latest issue of:
International Journal of Swarm Intelligence Research (IJSIR)
Volume 6, Issue 1, January - March 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

Supply Chain Inventory Coordination under Uncertain Demand via Combining Monte Carlo Simulation and Fitness Inheritance PSO

Heting Cao (Computer School, Beijing University of Posts and Telecommunications, Beijing, China), Xingquan Zuo (Computer School, Beijing University of Posts and Telecommunications, Beijing, China)

Supply chain coordination consists of multiple aspects, among which inventory coordination is the most widely used in practice. Inventory coordination is challenging due to the uncertainty of customers' demand. Existing researches typically assume that the demand is either a deterministic constant or a stochastic variable following a known distribution function. However, the former cannot reflect the practical costumers' demand, and the later make the model inaccurate when the demand distribution is ambiguous or highly variable. In this paper, the authors propose a Monte Carlo simulation model of such problem, which can mimic the inventory changing procedure of a supply chain with uncertain demand following an arbitrary distribution function. Then, a PSO is combined with the simulation model to achieve a coordination decision scheme to minimize the total inventory cost. Experiments show that their approach is able to produce a high quality solution within a short computational time and outperforms comparative approaches.

To obtain a copy of the entire article, click on the link below.
www.igi-global.com/article/supply-chain-inventory-coordination-under-uncertain-demand-via-combining-monte-carlo-simulation-and-fitness-inheritance-pso/127707<http://www.igi-global.com/article/supply-chain-inventory-coordination-under-uncertain-demand-via-combining-monte-carlo-simulation-and-fitness-inheritance-pso/127707>

To read a PDF sample of this article, click on the link below.
www.igi-global.com/viewtitlesample.aspx?id=127707<http://www.igi-global.com/viewtitlesample.aspx?id=127707>

ARTICLE 2

Multi-Objective Fish School Search

Carmelo J. A. Bastos-Filho (University of Pernambuco, Recife, Brazil), Augusto C. S. Guimarães (University of Pernambuco, Recife, Brazil)

The authors propose in this paper a very first version of the Fish School Search (FSS) algorithm for Multi-Objective Optimization. The proposal allows the optimization of problems with two or more conflicting objectives. The authors incorporated the dominance concept within the traditional FSS operators, creating a new approach called Multi-objective Fish School Search, MOFSS. They also adapted the barycenter concept deployed in the original FSS, which was replaced by the set of existing solutions in an external archive created to store the non-dominated solutions found during the search process. From their results in the DTLZ set of benchmark functions, the authors observed that the MOFSS obtained a similar performance when compared to well-known and well-established multi-objective swarm-based optimization algorithms. They detected some convergence problems in functions with a high number of local Pareto fronts. However, adaptive schemes can be used in future work to overcome this weakness.

To obtain a copy of the entire article, click on the link below.
www.igi-global.com/article/multi-objective-fish-school-search/127708<http://www.igi-global.com/article/multi-objective-fish-school-search/127708>

To read a PDF sample of this article, click on the link below.
www.igi-global.com/viewtitlesample.aspx?id=127708<http://www.igi-global.com/viewtitlesample.aspx?id=127708>

ARTICLE 3

A Local Best Particle Swarm Optimization Based on Crown Jewel Defense Strategy

Jiarui Zhou (School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, China), Junshan Yang (College of Information Engineering, Shenzhen University, Shenzhen, China), Ling Lin (College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China), Zexuan Zhu (College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China), Zhen Ji (College of Information Engineering, Shenzhen University, Shenzhen, China)

Particle swarm optimization (PSO) is a swarm intelligence algorithm well known for its simplicity and high efficiency on various optimization problems. Conventional PSO suffers from premature convergence due to the rapid convergence speed and lack of population diversity. PSO is easy to get trapped in local optimal, which largely deteriorates its performance. It is natural to detect stagnation during the optimization, and reactivate the swarm to search towards the global optimum. In this work the authors impose the reflecting bound-handling scheme and von Neumann topology on PSO to increase the population diversity. A novel Crown Jewel Defense (CJD) strategy is also introduced to restart the swarm when it is trapped in a local optimal. The resultant algorithm named LCJDPSO-rfl is tested on a group of unimodal and multimodal benchmark functions with rotation and shifting, and compared with other state-of-the-art PSO variants. The experimental results demonstrate stability and efficiency of LCJDPSO-rfl on most of the functions.

To obtain a copy of the entire article, click on the link below.
www.igi-global.com/article/a-local-best-particle-swarm-optimization-based-on-crown-jewel-defense-strategy/127709<http://www.igi-global.com/article/a-local-best-particle-swarm-optimization-based-on-crown-jewel-defense-strategy/127709>

To read a PDF sample of this article, click on the link below.
www.igi-global.com/viewtitlesample.aspx?id=127709<http://www.igi-global.com/viewtitlesample.aspx?id=127709>

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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>