Multiple Classifiers-Assisted Evolutionary Algorithm Based on Decomposition for High-Dimensional Multiobjective Problems

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 algorithm based on decomposition, which adapted for high-dimensional problems in terms following two insights. Compared approximation-based surrogates, accuracy classification-based surrogates robust few training samples. Furthermore, local classifiers can hedge risk overfitting issues. Accordingly, proposed builds with support vector machines (SVMs) decomposition-based algorithm, wherein each classifier trained corresponding scalarization function. Experimental results confirm that competitive state-of-the-art and efficient as well.