1 research outputs found
Empirical computation of the quasi-optimal number of informants in particle swarm optimization
In the standard particle swarm optimization (PSO), a new
particle鈥檚 position is generated using two main informant elements:
the best position the particle has found so far and the
best performer among its neighbors. In fully informed PSO,
each particle is influenced by all the remaining ones in the
swarm, or by a series of neighbors structured in static
topologies (ring, square, or clusters). In this paper, we generalize
and analyze the number of informants that take part in
the calculation of new particles. Our aim is to discover if a
quasi-optimal number of informants exists for a given
problem. The experimental results seem to suggest that 6 to 8
informants could provide our PSO with higher chances of
success in continuous optimization for well-known benchmarks.Ministerio de Ciencia, Innovaci贸n y Universidades TIN2008-06491-C04-01Ministerio de Ciencia, Innovaci贸n y Universidades BES-2009-018767Junta de Andaluc铆a P07-TIC-0304