A new prediction strategy combining T-S fuzzy nonlinear regression prediction and multi-step prediction for dynamic multi-objective optimization

Abstract Many dynamic multi-objective optimization problems have been widely developed to track the changing optima quickly and effectively in dynamic environments. Prediction-based methods can be used to predict future changes by learning past experience. This paper employed a new prediction strategy combining Takagi-Sugeno fuzzy nonlinear regression prediction and multi-step prediction named TSMP to estimate the new initial Pareto solutions whenever the environment changes. In TSMP, when environmental changes occur, the next initial center of Pareto solutions (PS) is predicted by a Takagi-Sugeno fuzzy nonlinear regression prediction model and then one trail population is generated by combining the predicted center and an approximate manifold of PS. Moreover, the other trail population is generated by a linear multi-step prediction model. Furthermore, the next initial PS is reinitialized by a random hybridization of these two trail populations. The proposed TSMP strategy is systematically compared with re-initialization strategy (RIS), feed-forward prediction strategy (FPS) and population prediction strategy (FPS) under different multi-objective optimizers on benchmark test problems with different features. Experimental results and performance comparisons with other state-of-the-art algorithms indicate that TSMP is effective and promising for solving dynamic multi-objective optimization problems.