LAUSR

Prediction-based methods have become more popular for solving dynamic multiobjective optimization problems. However, most of these proposed methods only use the optimal solutions in the previous two or three environments to predict the new optimal solutions after a change. Doing so neglects information from earlier in the history, which may reduce the accuracy of the prediction method. Conversely, if all history information is utilized for prediction, it will incur exorbitant computing costs, and it is not necessary because it is not all old optimal solutions correlate strongly with predicted solutions. Therefore, a novel prediction method based on fractional displacement (FDPM) is proposed. In this method, the previous optimal solutions that have a certain degree of correlation with the new solutions after a change are identified using the proposed prediction model, the parameters of which are obtained by training a certain length of previous optimal solution series. Then, these identified solutions are used to predict the optimal solutions in the new environment. This can balance the accuracy and computation cost of the prediction method. The performance of the proposed method is compared with five chosen state-of-the-art algorithms over fourteen benchmark problems covering diverse properties, and the results demonstrate that the proposed method is superior to the other selected algorithms.