A hybrid fuzzy inference prediction strategy for dynamic multi-objective optimization

Many real-world multi-objective optimization problems (MOPs) are dynamic in which variables of search space and/or objective space change over time. Hence the optimization algorithms should can quickly and efficiently track the Pareto front in dealing with dynamic environments. In this paper, a hybrid population prediction strategy based on fuzzy inference and one-step prediction (FIOPPS) is presented to extrapolate ahead the trajectory (position and/or orientation) of the new Pareto optimal solution set from the previous Pareto optimal solution sets and ensure the algorithm to respond quickly and effectively when the environment changes thus tracking the changing Pareto front. In our algorithm, the fuzzy inference model based on the Maximum Entropy Principle is extracted automatically from the previously found Pareto optimal solution sets to predict the Pareto solution sets at the beginning of the next time. Moreover, a new one-step prediction model is proposed to improve the prediction accuracy for environmental changes from motion state to static state and vice versa. Furthermore, a new variant of teaching-learning-based optimization algorithm with decomposition is first proposed as the MOEA optimizer for solving dynamic multi-objective optimization problems (DMOPs). In the proposed MOTLBO/D variant, the multi-objective decomposition mechanism is adopted and neighbor strategy is introduced into teaching-learning-based optimization algorithm (TLBO) to maintain the diversity of population and avoid the algorithm trapping into the local areas. Finally, to verify the performance of the proposed methods, ten benchmark test functions are simulated and evaluated. The statistical results indicate that the proposed FIOPPS strategy is promising for dealing with DMOPs.