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Finding all nondominated vectors for multi‐objective combinatorial optimization (MOCO) problems is computationally very hard in general. We approximate the nondominated frontiers of MOCO problems by fitting smooth hypersurfaces. For a given problem, we fit the hypersurface using a single nondominated reference vector. We experiment with different types of MOCO problems and demonstrate that in all cases the fitted hypersurfaces approximate all nondominated vectors well. We discuss that such an approximation is useful to find the neighborhood of preferred regions of the nondominated vectors with very little computational effort. Further computational effort can then be spent in the identified region to find the actual nondominated vectors the decision maker will prefer. © 2009 Wiley Periodicals, Inc. Naval Research Logistics, 2009 相似文献
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根据近年来装备保障的实践和国外的发展,从装备作战使用的角度辨析了装备保障的定义,界定了其范围,重点提出了装备保障性目标总要求,阐述了其内涵和任务能力的关系,简述了其确定过程及关键技术,基于装备任务能力与装备执行任务能力对装备保障性进行了系统分析。 相似文献
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针对现有高维多目标可视化技术不能有效显示决策偏好信息这一难题,提出一种基于径向轴图的交互式Pareto前沿可视化决策方法。进行标准化适应度函数处理,消除量纲差距;根据交互更新的轴向量矩阵建立最小二乘问题模型,通过Moore-Penrose广义逆矩阵方法求解投影点,从而获得Pareto前沿分布图。该方法能够有效显示目标各维度信息,直观呈现决策偏好,动态表现目标性能趋势。通过实验与传统可视化技术对比分析,验证了该方法的有效性和实用性。 相似文献
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