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Non-location distributed follows the proposed distribution algorithm but applied over-the-classic RSRP indicators from the UE served by each cell.
In this paper, an effective estimation of distribution algorithm (EDA) is proposed to solve the distributed permutation flow-shop scheduling problem (DPFSP).
The Univariate Marginal Distribution Algorithm (UMDA) – a popular estimation-of-distribution algorithm – is studied from a run time perspective.
This Modified Adaptive Gamma Correction with Weighting Distribution algorithm exhibits better contrast and high PSNR value than that of the existing Adaptive Gamma Correction with Weighting Distribution algorithm.
To solve the problem, a Pareto-based estimation of distribution algorithm (PBEDA) is proposed.
An evolutionary technique based on Estimation of Distribution Algorithm (EDA) is used for this purpose.
A Boltzmann-based estimation of distribution algorithm was used as optimizer.
The experimental results have demonstrated the scalability and efficiency of the proposed distribution algorithm.
Then, a spot template distribution algorithm is proposed to generate camouflage textures gradually.
The estimation of distribution algorithm is used to optimize the sampling period and control parameters for better performance.
This paper introduces a new approach for estimation of distribution algorithms called the Boltzmann Univariate Marginal Distribution Algorithm (BUMDA).
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com