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Tran Anh Tuan1, Long P Hoang2, Dung D Le2

  • 1School of Applied Mathematics and Informatics, Hanoi University of Science and Technology, Ha Noi, Viet Nam.

概括

本研究介绍了用于多目标优化 (MOO) 的可控帕雷托前线学习 (CPFL). 与现有方法相比,CPFL准确地接近帕雷托前线,计算效率得到了提高.

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Introduction to Scalars

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Pareto Chart00:52

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Cartesian Form for Vector Formulation01:26

Cartesian Form for Vector Formulation

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Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

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Scalar and Vectors01:22

Scalar and Vectors

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