Call for Papers on Particle Swarm Optimization
for a Special Issue of the IEEE Transactions on Evolutionary
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
to other evolutionary algorithms (EAs) in that the system is
with a population of random solutions. It is unlike other EAs,
in that each potential solution is also assigned a randomized
and the potential solutions, called particles, are then "flown"
the problem space.
Each particle keeps track of its coordinates in the problem space
are associated with the best solution (fitness) it has achieved so
(The fitness value is also stored.) This value is called pbest.
"best" value that is tracked by the global version of the particle
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
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
of the best solution, called lbest, attained within a local
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
optimization has been used for approaches that can be used across a
range of applications, as well as for specific applications focused
a specific requirement.
The main objective of this special issue is to assemble a collection
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:
Electrical and Computer Engineering Dept.
723 West Michigan, SL-160
Indianapolis, IN 46202-5132, USA
EDS Embedded Systems Group
1401 E. Hoffer Street
Kokomo, IN 46902, USA