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A min-max method with adaptive weightings for uniformly spaced Pareto optimum points

机译:具有均匀分布帕累托最优点的自适应加权的最小-最大方法

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摘要

This work aims at obtaining uniformly spaced Pareto optimum points in the objective space when multicriteria optimization problems are solved. An original adaptive scheme is proposed to update automatically weighting coefficients involved in the min-max method. By means of a novel bilevel approach, it is shown that with the calculation of the tangent and normal directions of the Pareto curve, Pareto optimum points can be obtained sequentially with a uniformly spaced distribution. Meanwhile, the distance between two adjacent Pareto optimum points is controllable depending upon the prescribed step length along the tangent direction. To validate the method, numerical bicriteria examples are solved to show its effectiveness.
机译:这项工作旨在在解决多准则优化问题时在目标空间中获得均匀分布的帕累托最优点。提出了一种原始的自适应方案来自动更新最小-最大方法中涉及的加权系数。通过一种新颖的双级方法,表明通过计算帕累托曲线的切线方向和法线方向,可以以均匀间隔的分布顺序获得帕累托最优点。同时,两个相邻的帕累托最优点之间的距离是可控制的,这取决于沿切线方向的规定步长。为了验证该方法,对数值双标准示例进行了求解以证明其有效性。

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