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Particle Swarm Optimization
(PSO) is a relatively new, evolution-based
search and optimization technique. We explain
the PSO algorithm in detail and demonstrate its
performance on one- and two-dimensional continuous
search problems.
The implemented PSO notebook
is currently being developed into a set of tutorials
and experimentation frameworks, which will become
part of the Evolvica (Jacob 2001) system for exploration
of evolutionary algorithms. This integration and
the PSO implementation in Mathematica will provide
an easy-to-use programming and experimentation
environment to further investigate comparisons
of PSO and other evolutionary search algorithms,
such as Evolution Strategies (Rechenberg 1973;
Rechenberg 1994; Schwefel 1981) and Genetic Algorithms
(Holland 1975; Mitchell 1996) (which are already
part of Evolvica).
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