Abstract The knowledge of rainfall regimes is a relevant requirement for many activities such as water resources planning, risk management, agriculture activities management, and other hydrologic applications. The present study has consisted of validating four techniques (one linear, one non-linear, and two hybrids) that allow identifying homogenous regions. We take the monthly rainfall in the Southwestern Colombia (Nariño), an area of 33,268 km2 characterized by complex topography and local factors that can influence the rainfall behavior, to test all techniques. The results showed overall the best performance for the approach related to non-linear principal component analysis and self-organizing map. However, in all mainly prevail two regions: the Andean Region and Pacific Region with a bimodal and unimodal regime, respectively. The bimodal one dominates over the Andes mountains range and the unimodal one the coastal zone. The application of non-linear approaches provided a better understanding of the seasonality of rainfall, and the results may be useful for water resource management.

Resumen La metodología de clustering fue utilizada para agrupar tres barrios en Quibdó teniendo en cuenta factores que favorecen el desarrollo de la malaria. Los mapas auto-organizados de Kohonen fueron utilizados para el análisis de las características más significativas en la clasificación. Los clusters detectados fueron comparados con la clasificación geográfica de las casas, encontrando, que los mapas auto-organizados de Kohonen clasifican las casas por las condiciones ambientales propicias para el desarrollo del mosquito más que por la clasificación administrativa de la ciudad.

Abstract Clustering methodology was used to group three neighborhoods in Quibdo taking into account factors that favor the development of malaria. The Kohonen self-organizing maps were used for the analysis of the most significant features in the standings. The detected clusters were compared with the geographical classification of houses, finding that the Kohonen self-organizing maps households classified by environmental conditions conducive to development rather than the administrative classification of the city.

Resumo A Metodologia de Clustering foi usada para agrupar três bairros em Quibdo, Colômbia, levando em consideração fatores que favorecem o desenvolvimento da malária. Mapas auto-organizados de Kohonen foram utilizados para a análise das características mais significativas no agrupamento. Os Clusters detectados foram comparados com o agrupamento geo-gráfico de casas, mostrando que os mapas auto-organizados de Kohonen agrupam as casas pelas condições ambientais favoráveis ao desenvolvimento do mosquito e não pelo agrupamento administrativo da cidade.

Desde años recientes, el paralelismo hace parte de la arquitectura de las computadoras personales al incluir unidades de co-procesamiento como las unidades de procesamiento gráfico, para conformar así una arquitectura heterogénea. Este artículo presenta la implementación de algoritmos de enjambres sobre esta arquitectura para resolver problemas de optimización de funciones, destacando su estructura inherentemente paralela y sus propiedades de control distribuido. En estos algoritmos se paralelizan los individuos de la población y las dimensiones del problema gracias a la granuralidad del sistema de procesamiento, que además proporciona una baja latencia de comunicaciones entre los individuos debido al procesamiento embebido. Para evaluar las potencialidades de los algoritmos de enjambres sobre la plataforma heterogénea, son implementados dos de ellos: el algoritmo de enjambre de partículas y el algoritmo de enjambre de bacterias. Se utiliza la aceleración como métrica para contrastar los algoritmos en la arquitectura heterogénea compuesta por una GPU NVIDIA GTX480 y una unidad de procesamiento secuencial, donde el algoritmo de enjambre de partículas obtiene una aceleración de hasta 36,82x y el algoritmo de enjambre de bacterias logra una aceleración de hasta 9,26x. Además, se evalúa el efecto al incrementar el tamaño en las poblaciones donde la aceleración es significativamente diferenciable pero con riesgos en la calidad de las soluciones.

Since few years ago, the parallel processing has been embedded in personal computers by including co-processing units as the graphics processing units resulting in a heterogeneous platform. This paper presents the implementation of swarm algorithms on this platform to solve several functions from optimization problems, where they highlight their inherent parallel processing and distributed control features. In the swarm algorithms, each individual and dimension problem are parallelized by the granularity of the processing system which also offer low communication latency between individuals through the embedded processing. To evaluate the potential of swarm algorithms on graphics processing units we have implemented two of them: the particle swarm optimization algorithm and the bacterial foraging optimization algorithm. The algorithms’ performance is measured using the acceleration where they are contrasted between a typical sequential processing platform and the NVIDIA GeForce GTX480 heterogeneous platform; the results show that the particle swarm algorithm obtained up to 36.82x and the bacterial foraging swarm algorithm obtained up to 9.26x. Finally, the effect to increase the size of the population is evaluated where we show both the dispersion and the quality of the solutions are decreased despite of high acceleration performance since the initial distribution of the individuals can converge to local optimal solution.