In pervasive systems, context is a direct cause to adapt the content of multimedia documents so that they comply, as far as possible, with the current constraints. In this respect, several adaptation approaches have a...
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In pervasive systems, context is a direct cause to adapt the content of multimedia documents so that they comply, as far as possible, with the current constraints. In this respect, several adaptation approaches have already been proposed, in which adaptation services are often selected from shortlists of services. Practically speaking, adaptation services are provided in various instances and ways, thus making the selection task more difficult. Furthermore, existing approaches for the service selection paradigm cannot be properly applied mainly because constraints on execution time and the availability of computation resources must be considered. To deal with this issue, we propose a framework for adaptive service selection using a bag of metaheuristics ranging from local to global search methods. Depending on the contextual constraints, a sub-bag of algorithms is selected, for which the budget is distributed, using a reinforcement learning mechanism related to their performances. The proposal is validated through a set of experiments and comparisons. The obtained results are satisfactory and encouraging.
This work presents a new algorithm specifically developed to solve the Optimal Active Power Dispatch (OAPD) problems. The OAPD problem aims to minimize total voltage deviation, quadratic fuel cost, and complex nonline...
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Evolutionary algorithms are effective techniques for optimizing non-linear and complex high-dimensional problems. However, most of them require a precise fine-tuning of their functioning settings to achieve satisfacto...
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The Particle Swarm optimization (PSO) algorithm is a well-known and widely used technique for solving complex optimization problems, often providing very good results. However, precise parameter selection or auto-adap...
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This study revives the Turtle Trading Rule, offering new parameters and a more stable strategy for increased profit opportunities. Proposed in the 1980s, the Turtle Trading method is a classic trend trading strategy c...
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This article proposes two optimization schemes for the position of elements in sparse circular spherical array with low sidelobes based on genetic algorithm. The first optimization scheme is to first optimize the radi...
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The article presents the possibility of using the Moth-Flame optimization (MFO) algorithm for Abrasive Water Jet machining (AWJ) of structural steel materials. In order to carry out the optimization, an original progr...
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In view of the limitations of the traditional D-P algorithm in manpower partial least squares optimization, a partial least squares optimization scheme based on deep learning is proposed. Firstly, the influencing fact...
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At present, with the continuous increase of Internet users in rural China, the scale of online shopping is also expanding, and the development prospect of rural e-commerce is obvious. The scattered rural population an...
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In the cloud-edge collaborative task scheduling framework, when the task volume on the edge node reaches a certain threshold, the edge node becomes unable to effectively migrate additional tasks. To solve the problem ...
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