Many-objective Optimization

This paper presents a novel surrogate-assisted evolutionary algorithm based on scalarization function approximation, which is designed to strengthen its robustness against this deterioration. The proposed algorithm, called SFA/DE, constructs an approximation model for each scalarization function defined in a decomposition-based framework. Each decomposed problem is then solved using multiple independent models trained for its neighbor problems.

This paper compares four popular adaptive DEs on the MOEA/D-DE framework to evaluate their scalability to the number of decision variables and objectives. Specifically, we employ jDE, JADE, EPSDE, and SaDE in this paper. Our experimental results provide several novel observations.