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Several real-world problems from various elds of Science and Engineering can be modeled as global optimization problems. In general, complex and large-scale problems can not be solved e ciently by exact techniques. In this context, Probabilistic algorithms, such as metaheuristics, have shown relevant results. Nevertheless, as the complexity of the problem increases, due to a large number of variables or several regions of the search space with sub-optimal solutions, the running time augments and the probability that the metaheuristics will nd the global optimum is signi cantly reduced. To improve the performance of metaheuristics applied to these problems, new e ciency-enhancement techniques (EETs) are proposed in this thesis. These EETs can be applied to di erent types of global optimization algorithms, rather than creating a new or a hybrid optimization algorithm. For continuous problems, the proposed EETs are the Domain Optimization Algorithm (DOA) and the Smart Sampling (SS) architecture. In fact, they are pre-processing algorithms that determine one or more promising regions of the search-space, containing a large amount of high-quality solutions, with higher chance of containing the global optimum. The DOA and SS were tested with signi cant success in several global optimization problems used as benchmark in the literature. The application of DOA to metaheuristics produced a performance improvement in 50% of problems tested. On the other hand, the application of SS have produced reductions of 80% of the evaluations of the objective function, as well as increased the success rate of nding the global optimum. For discrete problems (binary), we focused on metaheuristics that use probabilistic models to identify correlations among variables that are frequent in complex problems. The main EET proposed for discrete problems is called Population Size Management (PSM), which improves the probabilistic models constructed by such algorithms. The PSM produced a reduction of 50% of function evaluations maintaining the success rate of 100%. In summary, the results show that the proposed EETs can signi cantly increase the performance of metaheuristics for both discrete and continuous problems.

Técnicas de aumento de eficiência para metaheurísticas aplicadas a otimização global contínua e discreta