论文名称:A clustering procedure for reducing the number of representative solutions in the Pareto Front of multiobjective optimization problems
动机
假设一个三目标优化问题
\[\begin{aligned}
& \text { Availability: } \max _\theta J_1(\theta)=\max _{\theta_p, p=1, \ldots, 5}\left[\prod_{p=1}^u A_p\left(\theta_p\right)\right] \\
& \text { Cost: } \quad \min _\theta J_2(\theta)=\underset{\theta_{p h}, p=1, \ldots, 5}{ }\left[\sum_{p=1}^u \sum_{h=1}^{m_p} c_{p h} \theta_{p h}\right] \\
& h=1, \ldots, m_p \\
&
\end{aligned}\\
\text { Weight: } \quad \min _\theta J_3(\theta)=\min _{\substack{\theta_{p h}, p=1, \ldots, 5 \\ h=1, \ldots, m_p}}\left[\sum_{p=1}^u \sum_{h=1}^{m_p} w_{p h} \theta_{p h}\right]
\]
Pareto Front上有很多解,毫无疑问,对于多目标决策来说,需要找到一个办法,找出其中的代表性解。
算法
Clustering是一个不错的思路。
- multiobjective representative optimization clustering proceduremultiobjective representative optimization clustering with multiobjective optimization large-scale multiobjective multiobjective classifiers imbalanced ensemble representative representing represents time zone unrecognized represents procedure procedures