Examinando por Materia "genetic algorithms"

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    Ítem
    Heuristic Optimization for the Energy Management and Race Strategy of a Solar Car
    (MDPI AG, 2017-10-01) Betancur E.; Osorio-Gómez G.; Rivera J.C.; Betancur E.; Osorio-Gómez G.; Rivera J.C.; Universidad EAFIT. Departamento de Ciencias; Matemáticas y Aplicaciones
    Solar cars are known for their energy efficiency, and different races are designed to measure their performance under certain conditions. For these races, in addition to an efficient vehicle, a competition strategy is required to define the optimal speed, with the objective of finishing the race in the shortest possible time using the energy available. Two heuristic optimization methods are implemented to solve this problem, a convergence and performance comparison of both methods is presented. A computational model of the race is developed, including energy input, consumption and storage systems. Based on this model, the different optimization methods are tested on the optimization of the World Solar Challenge 2015 race strategy under two different environmental conditions. A suitable method for solar car racing strategy is developed with the vehicle specifications taken as an independent input to permit the simulation of different solar or electric vehicles.
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    Ítem
    Heuristic Optimization for the Energy Management and Race Strategy of a Solar Car
    (MDPI AG, 2017-10-01) Betancur E.; Osorio-Gómez G.; Rivera J.C.; Universidad EAFIT. Departamento de Ingeniería de Diseño; Ingeniería de Diseño (GRID)
    Solar cars are known for their energy efficiency, and different races are designed to measure their performance under certain conditions. For these races, in addition to an efficient vehicle, a competition strategy is required to define the optimal speed, with the objective of finishing the race in the shortest possible time using the energy available. Two heuristic optimization methods are implemented to solve this problem, a convergence and performance comparison of both methods is presented. A computational model of the race is developed, including energy input, consumption and storage systems. Based on this model, the different optimization methods are tested on the optimization of the World Solar Challenge 2015 race strategy under two different environmental conditions. A suitable method for solar car racing strategy is developed with the vehicle specifications taken as an independent input to permit the simulation of different solar or electric vehicles.
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    Ítem
    Investment portfolio optimization with transaction costs through a multiobjective genetic algorithm: an applied case to the Colombian Stock Exchange
    (Universidad Icesi, 2018-01-01) De Greiff, Samuel; Carlos Rivera, Juan; Universidad EAFIT. Escuela de Ciencias; Modelado Matemático
    This paper discusses portfolio optimization by considering constraints imposed by financial markets and conditions of projects with excess liquidity, such as transaction costs, limited budget and short time planning horizons. In light of these constraints, conventional models are found to generate non-efficient portfolios. Consequently, a mathematical model is formulated and a multiobjective genetic algorithm is implemented in order to find efficient portfolios in the Colombian Stock Exchange (Bolsa de Valores de Colombia), minimizing risks and maximizing profits. In addition, results are shown which allow comparison between those portfolios obtained through the proposed model and the mean-variance model, highlighting the importance of transaction costs and budget in investment decision making.
  • No hay miniatura disponible
    Ítem
    Investment portfolio optimization with transaction costs through a multiobjective genetic algorithm: an applied case to the Colombian Stock Exchange
    (Universidad Icesi, 2018-01-01) De Greiff, Samuel; Carlos Rivera, Juan; De Greiff, Samuel; Carlos Rivera, Juan; Universidad EAFIT. Departamento de Ciencias; Matemáticas y Aplicaciones
    This paper discusses portfolio optimization by considering constraints imposed by financial markets and conditions of projects with excess liquidity, such as transaction costs, limited budget and short time planning horizons. In light of these constraints, conventional models are found to generate non-efficient portfolios. Consequently, a mathematical model is formulated and a multiobjective genetic algorithm is implemented in order to find efficient portfolios in the Colombian Stock Exchange (Bolsa de Valores de Colombia), minimizing risks and maximizing profits. In addition, results are shown which allow comparison between those portfolios obtained through the proposed model and the mean-variance model, highlighting the importance of transaction costs and budget in investment decision making.