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This paper explores, with numerical case studies, the performance of an optimization algorithm that is a variant of EPSO, the Evolutionary Particle Swarm Optimization method. EPSO is already a hybrid approach that may be seen as a PSO with self-adaptive weights or an Evolutionary Programming approach with a self-adaptive recombination operator. The new hybrid DEEPSO retains the self-adaptive properties...
Context-Aware Recommender Systems (CARS) have emerged as a different way of providing more precise and interesting recommendations through the use of data about the context in which consumers buy goods and/or services. CARS consider not only the ratings given to items by consumers (users), but also the context attributes related to these ratings. Several algorithms and methods have been proposed in...
This paper makes a study about optimal supply of the energy service, using simulations of network operation scenarios, in order to optimize resources and minimize the variables: operation cost, energy losses, generation cost and consumers shedding. These simulations create optimal operation models of the network, allowing the system operator obtain knowledge to take pre-established procedures that...
The purpose of this work is to apply a hybrid algorithm based on Particle Swarm Optimization (PSO) and Genetic Algorithms (GA) for solving the problem of Economic Dispatch, which is based on supplying an energy demand, subjected to some restriction and reach out the best possible cost. Basically, we use the mutation operator from GAs aiming to explore regions in the search space that cannot be reached...
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