论文标题
多目标优化问题中均匀帕累托前近似的排斥动力学
Repulsion dynamics for uniform Pareto front approximation in multi-objective optimization problems
论文作者
论文摘要
标量允许通过解决许多由某些参数确定的许多单目标子问题来解决多目标优化问题。在这项工作中，我们提出了几种自适应策略来选择此类参数，以获得帕累托前沿的均匀近似。这是通过引入启发式动力学来完成的，其中参数通过二进制排斥潜力相互作用。该方法旨在最大程度地减少用于量化计算解决方案多样性的相关能量电位。还添加了随机组件以克服非最佳能量配置。数值实验显示了针对不同帕累托前几何形状的双目标问题所提出的方法的有效性。
Scalarization allows to solve a multi-objective optimization problem by solving many single-objective sub-problems, uniquely determined by some parameters. In this work, we propose several adaptive strategies to select such parameters in order to obtain a uniform approximation of the Pareto front. This is done by introducing a heuristic dynamics where the parameters interact through a binary repulsive potential. The approach aims to minimize the associated energy potential which is used to quantify the diversity of the computed solutions. A stochastic component is also added to overcome non-optimal energy configurations. Numerical experiments show the validity of the proposed approach for bi- and tri-objectives problems with different Pareto front geometries.
