A two stages prediction strategy for evolutionary dynamic multi-objective optimization

In many engineering and scientific research processes, the dynamic multi-objective problems (DMOPs) are widely involved. It's a quite challenge, which involves multiple conflicting objects changing over time or environment. The main task of DMOPs is tracking the Pareto front as soon as possible when the object changes over time. To accelerate the tracking process, a two stages prediction strategy (SPS) for DMOPs is proposed. To improve the prediction accuracy, population prediction is divided into center point prediction and manifold prediction when the change is detected. Due to the limitations of the support vector machine, the new population is predicted by the combination of the elite solution in the previous environment and Kalman filter in the early stage. Experimental results show that the proposed algorithm performs better on convergence and distribution when dealing with nonlinear problems, especially in the problems where the environmental change occurs frequently.