  This thesis is concerned with continuous, static, and single-objective
optimization problems subject to inequality constraints. Nevertheless, some
methods to handle other kinds of problems are briefly reviewed. The particle
swarm optimization paradigm was inspired by previous simulations of the
cooperative behaviour observed in social beings. It is a bottom-up, randomly
weighted, population-based method whose ability to optimize emerges from local,
individual-to-individual interactions. As opposed to traditional methods, it
can deal with different problems with few or no adaptation due to the fact that
it does profit from problem-specific features of the problem at issue but
performs a parallel, cooperative exploration of the search-space by means of a
population of individuals. The main goal of this thesis consists of developing
an optimizer that can perform reasonably well on most problems. Hence, the
influence of the settings of the algorithm's parameters on the behaviour of the
system is studied, some general-purpose settings are sought, and some
variations to the canonical version are proposed aiming to turn it into a more
general-purpose optimizer. Since no termination condition is included in the
canonical version, this thesis is also concerned with the design of some
stopping criteria which allow the iterative search to be terminated if further
significant improvement is unlikely, or if a certain number of time-steps are
reached. In addition, some constraint-handling techniques are incorporated into
the canonical algorithm to handle inequality constraints. Finally, the
capabilities of the proposed general-purpose optimizers are illustrated by
optimizing a few benchmark problems.
