Dedicated to My Parents, for the support

The optimization of parameter driven simulations has been the focus of many re-search papers. Algorithms like Hill Climbing, Tabu Search and Simulated Annealing have been thoroughly discussed and analyzed. However, these algorithms do not take into account the fact that simulations can have dynamic scenarios. In this dissertation, the possibility of using the classical optimization methods just mentioned, combined with clustering techniques, in order to optimize parameter driven simulations having dynamic scenarios, will be analyzed. This will be accomplished by optimizing simulations in several random static sce-narios. The optimum results of each of these optimizations will be clustered in order to nd a set of typical solutions for the simulation. These typical solutions can then be used in dynamic scenario simulations as references that will help the simulation adapt to scenario changes. A generic optimization and clustering system was developed in order to test the method just described. A simple trac simulation system, to be used as a testbed, was also developed. The results of this approach show that, in some cases, it is possible to improve the outcome of simulations in dynamic environments and still use the classical methods developed for static scenarios. Optimizac~ao de Simulac~oes em Cenario