Heterogeneous Constraint Handling for Particle Swarm Optimization
Department of Computer Science
University of Erlangen-Nuremberg, Germany
We propose a generic, hybrid constraint handling scheme for particle swarm
optimization called Heterogeneous Constraint Handling. Inspired by the notion of
social roles, we assign different constraint handling methods to the particles,
one for each social role. In this paper, we investigate two social roles for
particles, `self' and `neighbor.'
Due to the usual particle dynamics, a powerful mixture
of the two corresponding constraint handling methods emerges.
We evaluate this heterogeneous constraint handling approach with
respect to the complete set of
the CEC 2006 benchmark instances. Our results indicate that a such a
heterogeneous combination of two constraint handling methods
often leads to significantly better results than
running each individual constraint handling method separately
and returning the best solution obtained.
Proc. IEEE Swarm Intelligence Symposium (SIS); pp. 37-43, 2011.