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CFP: IEEE TEC special issue on PSO

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  • huxiaohui
    Call for Papers on Particle Swarm Optimization for a Special Issue of the IEEE Transactions on Evolutionary Computation This special issue will be devoted to
    Message 1 of 1 , May 26, 2002
      Call for Papers on Particle Swarm Optimization
      for a Special Issue of the IEEE Transactions on Evolutionary
      Computation

      This special issue will be devoted to exploring particle swarm
      optimization theory, paradigms, and implementations. Particle swarm
      optimization is a relatively new evolutionary algorithm. PSO is
      similar
      to other evolutionary algorithms (EAs) in that the system is
      initialized
      with a population of random solutions. It is unlike other EAs,
      however,
      in that each potential solution is also assigned a randomized
      velocity,
      and the potential solutions, called particles, are then "flown"
      through
      the problem space.

      Each particle keeps track of its coordinates in the problem space
      which
      are associated with the best solution (fitness) it has achieved so
      far.
      (The fitness value is also stored.) This value is called pbest.
      Another
      "best" value that is tracked by the global version of the particle
      swarm
      optimizer is the overall best value, and its location, obtained so far
      by any particle in the population. This location is called gbest.

      The particle swarm optimization concept consists of, at each time
      step,
      changing the velocity of (accelerating) each particle toward its pbest
      and gbest locations (global version of PSO). Acceleration is weighted
      by a random term, with separate random numbers being generated for
      acceleration toward pbest and gbest locations. There is also a local
      version of PSO in which, in addition to pbest, each particle keeps
      track
      of the best solution, called lbest, attained within a local
      topological
      neighborhood of particles.

      One of the reasons that particle swarm optimization is attractive is
      that there are few parameters to adjust. One version, with slight
      variations, works well in a wide variety of applications. Particle
      swarm
      optimization has been used for approaches that can be used across a
      wide
      range of applications, as well as for specific applications focused
      on
      a specific requirement.

      The main objective of this special issue is to assemble a collection
      of
      high-quality contributions that reflect the latest advances in the
      emerging field of particle swarm optimization. Original contributions
      are encouraged in, but are not limited to, the following areas:

      Particle swarm algorithms based on biological/social principles
      Self-organizing (emergent) properties of particle swarms
      Performance benchmarking of particle swarm optimization algorithms
      Applications of particle swarm optimization
      Hybrid algorithms utilizing neural networks, fuzzy systems, etc.
      Analyses of convergence of the particle swarm algorithm

      The deadline for submitting a full paper is July 10, 2002. Electronic
      submission is preferred. Send all submissions to one of the guest
      editors either through email or by post. Information on this special
      issue is available at the Internet address:
      http://www.engr.iupui.edu/~eberhart/IEEE-TEC-PSO.html.

      Guest Editors:
      Russ Eberhart
      Electrical and Computer Engineering Dept.
      723 West Michigan, SL-160
      Indianapolis, IN 46202-5132, USA
      reberhar@...

      Yuhui Shi
      EDS Embedded Systems Group
      1401 E. Hoffer Street
      Kokomo, IN 46902, USA
      yuhui.shi@...
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