Offline and Online Objective Reduction via Gaussian Mixture Model Clustering
针对高维多目标优化问题，提出用高斯混合模型聚类将非支配前沿分成子集，再对每个子集分别进行目标约简，开发了离线和在线两种方法，实验表明对退化或非退化帕累托前沿都有效。
The objective reduction has been regarded as a basic issue in many-objective optimization. Existing objective reduction methods identify one set of essential objectives using an approximate nondominated front. However, if the Pareto front (PF) of a many-objective optimization problem (MaOP) is irregular, one single set of essential objectives may not be efficient for objective reduction. This article proposes to produce several different sets of essential objectives in objective reduction. More specifically, we use the Gaussian mixture model clustering to classify the obtained nondominated front into different subsets and perform objective reduction on each subset. Both an offline objective reduction method and an online objective reduction method are developed. The experimental results indicate that our proposed methods work well for MaOPs with degenerate or nondegenerate PFs.