Evaluación de dos nuevos algoritmos en el diseño de granjas eólicas

En los últimos años el crecimiento en el consumo de energía eléctrica ha sido exorbitante, lo cual ha generado la necesidad de utilizar un recurso prometedor como el viento para extraer dicha energía. La distribución de turbinas de viento dentro de una granja eólica, con el objeto de optimizar la energía capturada, es un problema complejo de resolver. En este artículo se intenta solucionar este problema abordándolo de dos formas distintas: una es la adaptación del algoritmo GWO para vectores booleanos y la otra, DonQuijote, es un método nuevo que incluye el uso de la Evolución Diferencial y surge del analisis del problema. Para mostrar la eficiencia de los métodos se comparan con un Algoritmo Genético, tan estudiado en el área. La mejor propuesta participó en la competencia WFLO de la GECCO 2015.XVI Workshop Agentes y Sistemas Inteligentes (WASI)Red de Universidades con Carreras en Informática (RedUNCI

Evaluación de dos nuevos algoritmos en el diseño de granjas eólicas

En los últimos años el crecimiento en el consumo de energía eléctrica ha sido exorbitante, lo cual ha generado la necesidad de utilizar un recurso prometedor como el viento para extraer dicha energía. La distribución de turbinas de viento dentro de una granja eólica, con el objeto de optimizar la energía capturada, es un problema complejo de resolver. En este artículo se intenta solucionar este problema abordándolo de dos formas distintas: una es la adaptación del algoritmo GWO para vectores booleanos y la otra, DonQuijote, es un método nuevo que incluye el uso de la Evolución Diferencial y surge del analisis del problema. Para mostrar la eficiencia de los métodos se comparan con un Algoritmo Genético, tan estudiado en el área. La mejor propuesta participó en la competencia WFLO de la GECCO 2015.XVI Workshop Agentes y Sistemas Inteligentes (WASI)Red de Universidades con Carreras en Informática (RedUNCI

Evaluación de dos nuevos algoritmos en el diseño de granjas eólicas

En los últimos años el crecimiento en el consumo de energía eléctrica ha sido exorbitante, lo cual ha generado la necesidad de utilizar un recurso prometedor como el viento para extraer dicha energía. La distribución de turbinas de viento dentro de una granja eólica, con el objeto de optimizar la energía capturada, es un problema complejo de resolver. En este artículo se intenta solucionar este problema abordándolo de dos formas distintas: una es la adaptación del algoritmo GWO para vectores booleanos y la otra, DonQuijote, es un método nuevo que incluye el uso de la Evolución Diferencial y surge del analisis del problema. Para mostrar la eficiencia de los métodos se comparan con un Algoritmo Genético, tan estudiado en el área. La mejor propuesta participó en la competencia WFLO de la GECCO 2015.XVI Workshop Agentes y Sistemas Inteligentes (WASI)Red de Universidades con Carreras en Informática (RedUNCI

Manejo de diversidad en CHC aplicado a la optimización del costo energético en parques eólicos

En este trabajo analizaremos diferentes mecanismos de manejo de diversidad para el algoritmo CHC (Crossover elitism population, Half uniform crossover combination, Cataclysm mutation) para resolver problemas de optimización en parques eólicos de energía. El algoritmo CHC convencional contiene un mecanismo de reinicio poblacional aleatorio, esto conlleva a la posibilidad de perder cierto conocimiento adquirido si no se maneja adecuadamente. Es por ello que estudiaremos otros mecanismos de reinicio poblacional que tengan en cuenta el conocimiento adquirido durante su evolución para intentar lograr mejor convergencia en los resultados. El objetivo final es minimizar el costo del KWh analizando tres variantes de reinicio poblacional y cómo impactan en los resultados finales con respecto a la versión de CHC convencional.Workshop: WASI – Agentes y Sistemas InteligentesRed de Universidades con Carreras en Informátic

Informed mutation of wind farm layouts to maximise energy harvest

Correct placement of turbines in a wind farm is a critical issue in wind farm design optimisation. While traditional "trial and error"-based approaches suffice for small layouts, automated approaches are required for larger wind farms with turbines numbering in the hundreds. In this paper we propose an evolutionary strategy with a novel mutation operator for identifying wind farm layouts that minimise expected velocity deficit due to wake effects. The mutation operator is based on constructing a predictive model of velocity deficits across a layout so that mutations are inherently biased towards better layouts. This makes the operator informed rather than randomised. We perform a comprehensive evaluation of our approach on five challenging simulated scenarios using a simulation approach acceptable to industry [1]. We then compare our algorithm against two baseline approaches including the Turbine Displacement Algorithm [2]. Our results indicate that our informed mutation approach works effectively, with our approach identifying layouts with the lowest aggregate velocity deficits on all five test scenarios

Gene regulated car driving: using a gene regulatory network to drive a virtual car

This paper presents a virtual racing car controller based on an artificial gene regulatory network. Usually used to control virtual cells in developmental models, recent works showed that gene regulatory networks are also capable to control various kinds of agents such as foraging agents, pole cart, swarm robots, etc. This paper details how a gene regulatory network is evolved to drive on any track through a three-stages incremental evolution. To do so, the inputs and outputs of the network are directly mapped to the car sensors and actuators. To make this controller a competitive racer, we have distorted its inputs online to make it drive faster and to avoid opponents. Another interesting property emerges from this approach: the regulatory network is naturally resistant to noise. To evaluate this approach, we participated in the 2013 simulated racing car competition against eight other evolutionary and scripted approaches. After its first participation, this approach finished in third place in the competition

Gene regulated car driving: using a gene regulatory network to drive a virtual car

This paper presents a virtual racing car controller based on an artificial gene regulatory network. Usually used to control virtual cells in developmental models, recent works showed that gene regulatory networks are also capable to control various kinds of agents such as foraging agents, pole cart, swarm robots, etc. This paper details how a gene regulatory network is evolved to drive on any track through a three-stages incremental evolution. To do so, the inputs and outputs of the network are directly mapped to the car sensors and actuators. To make this controller a competitive racer, we have distorted its inputs online to make it drive faster and to avoid opponents. Another interesting property emerges from this approach: the regulatory network is naturally resistant to noise. To evaluate this approach, we participated in the 2013 simulated racing car competition against eight other evolutionary and scripted approaches. After its first participation, this approach finished in third place in the competition

Computer Science & Technology Series : XXI Argentine Congress of Computer Science. Selected papers

CACIC’15 was the 21thCongress in the CACIC series. It was organized by the School of Technology at the UNNOBA (North-West of Buenos Aires National University) in Junín, Buenos Aires. The Congress included 13 Workshops with 131 accepted papers, 4 Conferences, 2 invited tutorials, different meetings related with Computer Science Education (Professors, PhD students, Curricula) and an International School with 6 courses. CACIC 2015 was organized following the traditional Congress format, with 13 Workshops covering a diversity of dimensions of Computer Science Research. Each topic was supervised by a committee of 3-5 chairs of different Universities. The call for papers attracted a total of 202 submissions. An average of 2.5 review reports werecollected for each paper, for a grand total of 495 review reports that involved about 191 different reviewers. A total of 131 full papers, involving 404 authors and 75 Universities, were accepted and 24 of them were selected for this book.Red de Universidades con Carreras en Informática (RedUNCI

Computer Science & Technology Series : XXI Argentine Congress of Computer Science. Selected papers

CACIC’15 was the 21thCongress in the CACIC series. It was organized by the School of Technology at the UNNOBA (North-West of Buenos Aires National University) in Junín, Buenos Aires. The Congress included 13 Workshops with 131 accepted papers, 4 Conferences, 2 invited tutorials, different meetings related with Computer Science Education (Professors, PhD students, Curricula) and an International School with 6 courses. CACIC 2015 was organized following the traditional Congress format, with 13 Workshops covering a diversity of dimensions of Computer Science Research. Each topic was supervised by a committee of 3-5 chairs of different Universities. The call for papers attracted a total of 202 submissions. An average of 2.5 review reports werecollected for each paper, for a grand total of 495 review reports that involved about 191 different reviewers. A total of 131 full papers, involving 404 authors and 75 Universities, were accepted and 24 of them were selected for this book.Red de Universidades con Carreras en Informática (RedUNCI