Decentralized optimization is typically studied under the assumption of noise-free transmission. However, real-world scenarios often involve the presence of noise due to factors such as additive white Gaussian noise c...
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This article considers a class of real-time stochastic optimization problems dependent on an unknown probability distribution. In the considered scenario, data are streaming frequently while trying to reach a decision...
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This article considers a class of real-time stochastic optimization problems dependent on an unknown probability distribution. In the considered scenario, data are streaming frequently while trying to reach a decision. Thus, we aim to devise a procedure that incorporates samples (data) of the distribution sequentially and adjusts decisions accordingly. We approach this problem in a distributionally robust optimization framework and propose a novel Online Data Assimilation Algorithm (OnDA Algorithm) for this purpose. This algorithm guarantees out-of-sample performance of decisions with high probability, and gradually improves the quality of the decisions by incorporating the streaming data. We show that the OnDA Algorithm converges under a sufficiently slow data streaming rate, and provide a criteria for its termination after certain number of data have been collected. Simulations illustrate the results.
In order to solve the problem of selecting optimal control parameters for various genetic operators in genetic algorithms, this article propose a comprehensive performance evaluation function based on minimizing Eucli...
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Recently, evolutionary multi-objective optimization (EMO) algorithms have been used in various application fields. Whereas many new EMO algorithms are proposed every year, well-known EMO algorithms such as NSGA-II, MO...
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Many techniques for real-time trajectory optimization and control require the solution of optimization problems at high frequencies. However, ill-conditioning in the optimization problem can significantly reduce the s...
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Support vector machines (SVM) are commonly used to solve classification and regression problems, however a suitable kernel function needs to be selected to achieve an effective solution. To solve this problem, we prop...
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Aiming at the problem that the iForest algorithm is not sensitive enough to local anomalies and produces a large number of false alarms in the detection results on some low sea state datasets, this paper proposes the ...
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Subset selection is always a hot topic in the community of evolutionary multi-objective optimization (EMO) since it is used in mating selection, environmental selection, and final selection. In the first two scenarios...
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With the rapid development of mobile communication technology, the construction of the 5G network has become a hotspot worldwide. In this context, rational PCI network planning becomes particularly important. This stu...
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In response to the growing concerns over electromagnetic radiation (EMR) emanating from urban electric power facilities, this study proposes a novel optimization framework. The rapid expansion of urban infrastructure ...
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