Multiscale Parameter Search (MSPS): A Deterministic Approach for Black-box Global Optimization

Optimization problems from where no structural information can be obtained arise everyday in many fields of knowledge, increasing the importance of well-performing black-box optimizers. In this paper, we introduce a new approach for global optimization of black-box problems based on the synergistic combination of scaled local searches. By looking farther to the behavior of the problem, the idea is to speed up the search while avoiding it to become locally trapped. The method is fairly compared to other well-performing techniques, showing promising results. A testbed of benchmark problems and image registration problems are considered and the proposed approach outperforms the other algorithms in most cases.

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