Multiple Classifiers-Assisted Evolutionary Algorithm Based on Decomposition for High-Dimensional Multiobjective Problems
提出一种基于分解的多分类器辅助进化算法，用多个支持向量机分类器替代传统代理模型，解决高维昂贵多目标优化问题，实验表明算法在效率和效果上均有竞争力。
Surrogate-assisted multiobjective evolutionary algorithms (MOEAs) have advanced the field of computationally expensive optimization, but their progress is often restricted to low-dimensional problems. This manuscript presents a multiple classifiers-assisted evolutionary algorithm based on decomposition, which is adapted for high-dimensional expensive problems in terms of the following two insights. Compared to approximation-based surrogates, the accuracy of classification-based surrogates is robust for few high-dimensional training samples. Furthermore, multiple local classifiers can hedge the risk of overfitting issues. Accordingly, the proposed algorithm builds multiple classifiers with support vector machines (SVMs) on a decomposition-based multiobjective algorithm, wherein each local classifier is trained for a corresponding scalarization function. Experimental results confirm that the proposed algorithm is competitive to the state-of-the-art algorithms and computationally efficient as well.