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- 实验方案
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[IEEE 2019 IEEE 8th International Conference on Advanced Optoelectronics and Lasers (CAOL) - Sozopol, Bulgaria (2019.9.6-2019.9.8)] 2019 IEEE 8th International Conference on Advanced Optoelectronics and Lasers (CAOL) - Monte Carlo method for analyzing the propagation of radiation in the skin layers containing blood in photoplethysmography
摘要： In evolutionary multiobjective optimization, it is very important to be able to visualize approximations of the Pareto front (called approximation sets) that are found by multi-objective evolutionary algorithms. While scatter plots can be used for visualizing 2-D and 3-D approximation sets, more advanced approaches are needed to handle four or more objectives. This paper presents a comprehensive review of the existing visualization methods used in evolutionary multiobjective optimization, showing their outcomes on two novel 4-D benchmark approximation sets. In addition, a visualization method that uses prosection (projection of a section) to visualize 4-D approximation sets is proposed. The method reproduces the shape, range, and distribution of vectors in the observed approximation sets well and can handle multiple large approximation sets while being robust and computationally inexpensive. Even more importantly, for some vectors, the visualization with prosections preserves the Pareto dominance relation and relative closeness to reference points. The method is analyzed theoretically and demonstrated on several approximation sets.
关键词： projection,evolutionary multiobjective optimization,Pareto front,evolutionary algorithm,visualization,Approximation set
更新于2025-09-23 15:19:57
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[IEEE 2019 IEEE Sustainable Power and Energy Conference (iSPEC) - Beijing, China (2019.11.21-2019.11.23)] 2019 IEEE Sustainable Power and Energy Conference (iSPEC) - The Probabilistic Assessment of Outgoing Transformer Operation Risk Considering the Correlation Between Wind Power and Photovoltaic
摘要： Rough set proposed by Pawlak in 1982 is an important tool to process uncertain information. As an extended model of rough set, an approximation set model of rough set was proposed and proved to be feasible to establish an approximation target set with existing knowledge base. However, there still is a lack of effective methods for knowledge acquisition based on the approximation set model. In this paper, related methods of attribute reduction based on approximation set model of rough set are discussed in algebraic view and information view, respectively. First, a distribution reduction method on the basic of discernibility matrix according to approximation set is proposed and discussed in algebraic view. Furthermore, an algorithm of attribute reduction based on conditional information entropy of approximation set model is presented in information view. Finally, many experimental results show that the proposed algorithm could acquire more effective knowledge from uncertain information system compared with other algorithms based on classical rough set theory.
关键词： attribute reduction,approximation set,information view,algebra view,Rough set
更新于2025-09-19 17:13:59