A Dual Surrogate-Based Evolutionary Algorithm for High-Dimensional Expensive Multiobjective Optimization Problems

In surrogate-assisted multiobjective evolutionary al-gorithms (SAMOEAs), approximation and classification models are frequently employed to screen candidate solutions, but there is a tradeoff between both models in terms of the model accuracy and the screening capacity. This tradeoff is highlighted especially when the problem dimension increases, making SAMOEAs difficult to solve high-dimensional problems. This paper proposes a dual surrogate-based SAMOEA for solving high-dimensional expensive multi-objective optimization problems. The proposed algorithm, called DSEAID, is designed to adaptively select either approximation models or classification models dependent on the model accuracy. Compared to existing algorithms which use both approximation and classification models simultaneously, DSEAID possesses a robust framework against the deterioration of the model accuracy. Experimental results on benchmark problems with up to 150 decision variables show that our dual surrogate-based framework is effective in addressing high-dimensional problems. Moreover, we show DSEAID is competitive with state-of-the-art SAMOEAs.