  Particle Swarm Optimization is a global optimizer in the sense that it has
the ability to escape poor local optima. However, if the spread of information
within the population is not adequately performed, premature convergence may
occur. The convergence speed and hence the reluctance of the algorithm to
getting trapped in suboptimal solutions are controlled by the settings of the
coefficients in the velocity update equation as well as by the neighbourhood
topology. The coefficients settings govern the trajectories of the particles
towards the good locations identified, whereas the neighbourhood topology
controls the form and speed of spread of information within the population
(i.e. the update of the social attractor). Numerous neighbourhood topologies
have been proposed and implemented in the literature. This paper offers a
numerical comparison of the performances exhibited by five different
neighbourhood topologies combined with four different coefficients' settings
when optimizing a set of benchmark unconstrained problems. Despite the optimum
topology being problem-dependent, it appears that dynamic neighbourhoods with
the number of interconnections increasing as the search progresses should be
preferred for a non-problem-specific optimizer.
