  Swarm Intelligence (SI) is the property of a system whereby the collective
behaviors of (unsophisticated) entities interacting locally with their
environment cause coherent functional global patterns to emerge. SI provides a
basis with wich it is possible to explore collective (or distributed) problem
solving without centralized control or the provision of a global model. In this
paper we present a Swarm Search Algorithm with varying population of agents.
The swarm is based on a previous model with fixed population which proved its
effectiveness on several computation problems. We will show that the variation
of the population size provides the swarm with mechanisms that improves its
self-adaptability and causes the emergence of a more robust self-organized
behavior, resulting in a higher efficiency on searching peaks and valleys over
dynamic search landscapes represented here - for the purpose of different
experiments - by several three-dimensional mathematical functions that suddenly
change over time. We will also show that the present swarm, for each function,
self-adapts towards an optimal population size, thus self-regulating.
