The image stitching process is based on the alignment and composition of multiple images that represent parts of a 3D scene. The automatic construction of panoramas from multiple digital images is a technique of great importance, finding applications in different areas such as remote sensing and inspection and maintenance in many work environments. In traditional automatic image stitching, image alignment is generally performed by the Levenberg–Marquardt numerical-based method. Although these traditional approaches only present minor flaws in the final reconstruction, the final result is not appropriate for industrial grade applications. To improve the final stitching quality, this work uses a RGBD robot capable of precise image positing. To optimize the final adjustment, this paper proposes the use of bio-inspired algorithms such as Bat Algorithm, Grey Wolf Optimizer, Arithmetic Optimization Algorithm, Salp Swarm Algorithm and Particle Swarm Optimization in order verify the efficiency and competitiveness of metaheuristics against the classical Levenberg–Marquardt method. The obtained results showed that metaheuristcs have found better solutions than the traditional approach.

A previsibilidade da vazão disponível para as plantas hidrelétricas alguns dias a frente é de extrema importância para o planejamento e operação do sistema elétrico. É possível obter estas previsões através do uso de modelos hidrológicos que caracterizam a região de estudo. Este trabalho visa aplicar o modelo SMAP a três sub-bacias brasileiras, com o uso de algoritmos de inteligência computacional para a calibração dos parâmetros de chuva. GWO e BA foram os algoritmos utilizados. Os resultados mostraram que o GWO apresentou uma convergência mais rápida, porém os resultados obtidos pelo BA foram melhores.

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