A data augmentation based Kriging-assisted reference vector guided evolutionary algorithm for expensive dynamic multi-objective optimization

Abstract

Expensive dynamic multi-objective optimization problems (EXDMOPs) involve multiple objective functions changing over time steps. In this kind of problems, only a small number of function evaluations can be allowed in each time step. The challenge of EXDMOP is how to quickly and accurately track the changing optimal solutions with only a small number of function evaluations. Although EXDMOP can be solved as a series of independent expensive multi-objective optimization problems or as an ordinary dynamic multi-objective optimization problems, these two methods either ignore the connection between different time steps or degrade the performance significantly due to the requirement for a large number of function evaluations. In this paper, we incorporate two proposed strategies named training data augmentation and sampling pool expansion to the Kriging-assisted reference vector guided evolutionary algorithm to construct a novel algorithm for solving EXDMOPs. The training data augmentation strategy tries to look for the previous similar time step, and adds the solutions evaluated by the objective functions at that time step to current time step. The accuracies of the Kriging models are improved in this way. The sampling pool expansion strategy sends the predicted optimal solutions to the competition of the function evaluations assignment at each time step, where the predicted optimal solutions are generated by the improved population prediction strategy. We set the ablation experiment for the proposed algorithm and compare it with four dynamic multi-objective evolutionary algorithms on various of problems. The experiment results validate the effectiveness of the proposed two strategies. It also indicates that the proposed algorithm is promising for solving EXDMOPs.