  Particle Swarm Optimization (PSO) is a popular nature-inspired meta-heuristic
for solving continuous optimization problems. Although this technique is widely
used, the understanding of the mechanisms that make swarms so successful is
still limited. We present the first substantial experimental investigation of
the influence of the local attractor on the quality of exploration and
exploitation. We compare in detail classical PSO with the social-only variant
where local attractors are ignored. To measure the exploration capabilities, we
determine how frequently both variants return results in the neighborhood of
the global optimum. We measure the quality of exploitation by considering only
function values from runs that reached a search point sufficiently close to the
global optimum and then comparing in how many digits such values still deviate
from the global minimum value. It turns out that the local attractor
significantly improves the exploration, but sometimes reduces the quality of
the exploitation. As a compromise, we propose and evaluate a hybrid PSO which
switches off its local attractors at a certain point in time. The effects
mentioned can also be observed by measuring the potential of the swarm.
