Expensive Many-Objective Optimization by Learning of the Strengthened Dominance Relation

Expensive many-objective optimization problems (EMaOPs) are common in the real world, whose objective values need to be calculated by time-consuming computational simulations or expensive experiments. For EMaOPs, guiding the optimization process by surrogate models is a popular method. In this paper, an FNN-assisted evolutionary algorithm is proposed for better solving EMaOPs. Concretely, a feedforward neural network (FNN) is constructed by learning the strengthened dominance relation (SDR) between two solutions. Then promising samples are generated by evolutionary search based on the constructed FNN. The proposed method is compared with four state-of-the-art peer algorithms on a set of benchmark problems. Experimental results demonstrate its superiority.